diff --git a/.github/workflows/functional_tests.yaml b/.github/workflows/functional_tests.yaml
index cde7e8ba..9b913d09 100644
--- a/.github/workflows/functional_tests.yaml
+++ b/.github/workflows/functional_tests.yaml
@@ -4,7 +4,7 @@
 name: Python package
 
 on:
-  push: 
+  push:
   pull_request:
 
 jobs:
@@ -17,16 +17,16 @@ jobs:
         python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
 
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
     - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v3
+      uses: actions/setup-python@v5
       with:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
         if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
-        python -m pip install flake8 pytest qiskit-aer qiskit_ibm_runtime
+        python -m pip install flake8 pytest
     - name: Lint with flake8
       run: |
         # stop the build if there are Python syntax errors or undefined names
diff --git a/.github/workflows/lint.yaml b/.github/workflows/lint.yaml
index 4495c0e7..6af4cfc2 100644
--- a/.github/workflows/lint.yaml
+++ b/.github/workflows/lint.yaml
@@ -14,9 +14,9 @@ jobs:
   lint:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
       - name: Setup Python 3.8
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: ${{ env.PYTHON_VERSION }}
       - name: Update pip and install lint utilities
diff --git a/.github/workflows/pull_request.yaml b/.github/workflows/pull_request.yaml
index 5d423f1f..35746edd 100644
--- a/.github/workflows/pull_request.yaml
+++ b/.github/workflows/pull_request.yaml
@@ -9,8 +9,8 @@ jobs:
   pre-commit:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v3
-      - uses: actions/setup-python@v4
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
         with:
           python-version: ${{ env.PYTHON_VERSION }}
       - uses: pre-commit/action@v2.0.3
diff --git a/README.md b/README.md
index ddc1982c..433956fa 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,5 @@
 <p align="center">
-<img src="torchquantum_logo.jpg" alt="torchquantum Logo" width="450">
+<img src="https://github.com/mit-han-lab/torchquantum/blob/main/torchquantum_logo.jpg?raw=true" alt="torchquantum Logo" width="450">
 </p>
 
 <h2><p align="center">Quantum Computing in PyTorch</p></h2>
@@ -55,7 +55,7 @@ Simulate quantum computations on classical hardware using PyTorch. It supports s
 Researchers on quantum algorithm design, parameterized quantum circuit training, quantum optimal control, quantum machine learning, quantum neural networks.
 #### Differences from Qiskit/Pennylane
 
-Dynamic computation graph, automatic gradient computation, fast GPU support, batch model tersorized processing.
+Dynamic computation graph, automatic gradient computation, fast GPU support, batch model tensorized processing.
 
 ## News
 - Torchquantum is used in the winning team for ACM Quantum Computing for Drug Discovery Challenge.
diff --git a/examples/ICCAD22_tutorial/README.md b/examples/ICCAD22_tutorial/README.md
index c0e78c78..06d73735 100644
--- a/examples/ICCAD22_tutorial/README.md
+++ b/examples/ICCAD22_tutorial/README.md
@@ -1,6 +1,6 @@
 # ICCAD 2022 Tutorial [[slides]](./iccad_tutorial.pdf)
-## Section 1: TorchQuantum Basic Usage: [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mit-han-lab/torchquantum/blob/master/ICCAD22_tutorial/sec1_basic.ipynb)
+## Section 1: TorchQuantum Basic Usage: [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mit-han-lab/torchquantum/blob/master/examples/ICCAD22_tutorial/sec1_basic.ipynb)
 
-## Section 2: Use TorchQuantum on Pulse Level Optimizations: [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mit-han-lab/torchquantum/blob/master/ICCAD22_tutorial/sec2_pulse.ipynb)
+## Section 2: Use TorchQuantum on Pulse Level Optimizations: [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mit-han-lab/torchquantum/blob/master/examples/ICCAD22_tutorial/sec2_pulse.ipynb)
 
-## Section 3: Use TorchQuantum on Gate Level Optimizations: [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mit-han-lab/torchquantum/blob/master/ICCAD22_tutorial/sec3_gate.ipynb)
+## Section 3: Use TorchQuantum on Gate Level Optimizations: [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mit-han-lab/torchquantum/blob/master/examples/ICCAD22_tutorial/sec3_gate.ipynb)
diff --git a/examples/PauliSumOp/pauli_sum_op_noise.py b/examples/PauliSumOp/pauli_sum_op_noise.py
new file mode 100644
index 00000000..e69de29b
diff --git a/examples/QCBM/README.md b/examples/QCBM/README.md
new file mode 100644
index 00000000..cf61c65c
--- /dev/null
+++ b/examples/QCBM/README.md
@@ -0,0 +1,42 @@
+# Quantum Circuit Born Machine
+(Implementation by: [Gopal Ramesh Dahale](https://github.com/Gopal-Dahale))
+
+Quantum Circuit Born Machine (QCBM) [1] is a generative modeling algorithm which uses Born rule from quantum mechanics to sample from a quantum state $|\psi \rangle$ learned by training an ansatz $U(\theta)$ [1][2]. In this tutorial we show how `torchquantum` can be used to model a Gaussian mixture with QCBM.
+
+## Setup
+
+Below is the usage of `qcbm_gaussian_mixture.py` which can be obtained by running `python qcbm_gaussian_mixture.py -h`.
+
+```
+usage: qcbm_gaussian_mixture.py [-h] [--n_wires N_WIRES] [--epochs EPOCHS] [--n_blocks N_BLOCKS] [--n_layers_per_block N_LAYERS_PER_BLOCK] [--plot] [--optimizer OPTIMIZER] [--lr LR]
+
+options:
+  -h, --help            show this help message and exit
+  --n_wires N_WIRES     Number of wires used in the circuit
+  --epochs EPOCHS       Number of training epochs
+  --n_blocks N_BLOCKS   Number of blocks in ansatz
+  --n_layers_per_block N_LAYERS_PER_BLOCK
+                        Number of layers per block in ansatz
+  --plot                Visualize the predicted probability distribution
+  --optimizer OPTIMIZER
+                        optimizer class from torch.optim
+  --lr LR
+```
+
+For example:
+
+```
+python qcbm_gaussian_mixture.py --plot --epochs 100 --optimizer RMSprop --lr 0.01 --n_blocks 6 --n_layers_per_block 2 --n_wires 6
+```
+
+Using the command above gives an output similar to the plot below.
+
+<p align="center">
+<img src ='./assets/sample_output.png' width-500 alt='sample output of QCBM'>
+</p>
+
+
+## References
+
+1. Liu, Jin-Guo, and Lei Wang. “Differentiable learning of quantum circuit born machines.” Physical Review A 98.6 (2018): 062324.
+2. Gili, Kaitlin, et al. "Do quantum circuit born machines generalize?." Quantum Science and Technology 8.3 (2023): 035021.
\ No newline at end of file
diff --git a/examples/QCBM/assets/sample_output.png b/examples/QCBM/assets/sample_output.png
new file mode 100644
index 00000000..c1626a4e
Binary files /dev/null and b/examples/QCBM/assets/sample_output.png differ
diff --git a/examples/QCBM/qcbm_gaussian_mixture.ipynb b/examples/QCBM/qcbm_gaussian_mixture.ipynb
new file mode 100644
index 00000000..849f7cdc
--- /dev/null
+++ b/examples/QCBM/qcbm_gaussian_mixture.ipynb
@@ -0,0 +1,255 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1cfe7a69-13c6-48ce-bc02-62e2047eef22",
+   "metadata": {},
+   "source": [
+    "# Learning Gaussian Mixture with Quantum Circuit Born Machine\n",
+    "\n",
+    "A QCBM is a generative model that represents the probability distribution of a classical dataset as a quantum pure state. It is a quantum circuit that generates samples via projective measurements on qubits. Given a target distribution $\\pi(x)$, we can generate samples closer to it using a QCBM.\n",
+    "\n",
+    "The Kerneled MMD loss is used to measure the difference between the generated samples and the target distribution.\n",
+    "\n",
+    "$$\n",
+    "\\mathcal{L} = \\underset{x, y \\sim p_\\boldsymbol{\\theta}}{\\mathbb{E}}[{K(x,y)}]-2\\underset{x\\sim p_\\boldsymbol{\\theta},y\\sim \\pi}{\\mathbb{E}}[K(x,y)]+\\underset{x, y \\sim \\pi}{\\mathbb{E}}[K(x, y)]\n",
+    "$$\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4d440b94-63d4-4f6d-882e-45827d54cb4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "from torchquantum.algorithm import QCBM, MMDLoss\n",
+    "import torchquantum as tq\n",
+    "from qcbm_gaussian_mixture import gaussian_mixture_pdf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d14c9f7-4e5d-4fe1-98b4-83962d949519",
+   "metadata": {},
+   "source": [
+    "We use the function `gaussian_mixture_pdf` to generate a gaussian mixture which will be the target distribution $\\pi(x)$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5483ab05-1a08-4bdc-8799-0e67433131af",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7c8e443138b0>]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "n_wires = 6\n",
+    "x_max = 2**n_wires\n",
+    "x_input = np.arange(x_max)\n",
+    "mus = [(2 / 8) * x_max, (5 / 8) * x_max]\n",
+    "sigmas = [x_max / 10] * 2\n",
+    "data = gaussian_mixture_pdf(x_input, mus, sigmas)\n",
+    "\n",
+    "# This is the target distribution that the QCBM will learn\n",
+    "target_probs = torch.tensor(data, dtype=torch.float32)\n",
+    "\n",
+    "plt.plot(x_input, target_probs, linestyle=\"-.\", label=r\"$\\pi(x)$\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b1bb110-e81c-455e-86a6-6b722f3a4433",
+   "metadata": {},
+   "source": [
+    "Using `torchquantum`, we can create a parameterized quantum circuit which will be used to generate a probability distribution. The gradient-based learning algorithm is used to update the circuit parameters $\\theta$ iteratively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8347fa01-d519-40e3-a7ea-67fabca8ed56",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/gopald/Documents/tq-env/lib/python3.10/site-packages/qiskit/visualization/circuit/matplotlib.py:266: FutureWarning: The default matplotlib drawer scheme will be changed to \"iqp\" in a following release. To silence this warning, specify the current default explicitly as style=\"clifford\", or the new default as style=\"iqp\".\n",
+      "  self._style, def_font_ratio = load_style(self._style)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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bktSzZ88azzNq1ChZLBbdf//9nqimV10y8h5ZrSF68+t7//b63Qqxhuq6p05Uz/bDNCztQh/V0POq64O+nc7QKT0m6LG5l2r2R9frlvNfUXxMQx/W0nNqeg9UeHr+tSopLdJtF7zh3YoBcIrNJu3N8V35e7J9V7YzVmyTypy84nzpH0xyBzJiIWIhYiEgcPgyHknPNi6C9HeHj0prdjl3TEau7y4shXcQDxEPEQ8BgeHwUeOxPb5ilrmhZS48AmrZH+6vB/wHsRCxUCDHQgGZ8J87d65sNpsuueQSxcbGVrtPVFSUpJoT/u+//77Wrl3rqSp6XYtG7TWs54Vas+07bdixrPL10JAwdWkzSIcLsnR6nyt8WEPPq6kPJo95QnsPbVPfTqPUv/OZPqyhZ9XUfkn6ePkz+nnzZ3pg0gJFhkf7qIYAHHEwv+4l6j0p3QRf6sptrt2tn3nYuPobgYlYiFiIWAgIHL6MR4pKpUNefl6uK37a5tqFCct9tDwwvIN4iHiIeAgIDL6emzFDwv9IkbR+j/PHbUg3Vo1EYCIWIhYK5Fgo1NcV8IRFixZJkoYNG1bjPunp6ZKqT/jn5eXppptu0hNPPKFLL7203vXp06ePMjMznTomPDRKL0117zfti0bcpcVr5+rNb+7VE9culiRt2LFM36x6Q+MGT9Xzn9yoF9utVURYlNPn7tCxg0rK3PdgZ0+0X6q+D6LCY9QsKVVtk7vX69zu6gNPtV2qvv1rty3WK5/froev+lLJSW3qdX53vw8AHK9h6z4adv2CarfdcoYUX8ef8PjIv/57/zk175d3VJr11fGv//jLWv3rsrMcq6yPRMYn66y7Vrl07JU33KttP77m5hr5l9H/WKnohGbKyMxQSkpfX1enRmaLhST3joPBHAtV8NZ7gFgIMJ8R075UYovq/2bVFQ85GgtJNcdDI84Yq+zdqx2srW8MnPiyWnQb5fRxy1f/qXsuOskDNfIfwRwLScwNSeaJh7zZfnfFQ8RCgHe07jNBfc+fVe02b8wNvfPex7rx7BscrK1vNE4dpFOued/p4+x2acRZl2r/liXur5SfIBby7NyQ5P+5IrPEQpL55oZcab/N9tcytUOGDNGaNWtcKjsgE/67dhnr1rVu3bra7WVlZfrhhx8kVZ/wv+uuu9SxY0ddcsklbkn4Z2Zmau/evU4dExnm/NUjPdsN1beP13wJf+umnfX1zPLKfx8tztfj8ybpylGPaszA6zT9xVP02pf/0HVjn3S67Ix9+1RU6r5L31xpv+R8H7iTu/rA1bZLzrc/M3un/vX2BF191uPq2W6oy+VWcPf7AEA14trVuCk+Smrg4J8Qq9XxfY9VbpPTY5q3JZRFunxs/tFiv29ffZWXl1f+15/barZYSHLvOBjMsVAFb7wHiIUAcyovr/lz7mg85GosJEmHsnO0z4/HUEkqdfVPvTXMr+MDdwjkWEhibkgKnHjIW+13ZzxELAR4R4MONS835I25oaKSUr8eQyUprHGBy8cePlLg9+2rD2Ihz84NSb7PFQVKLCSZb26ovu3fv9/1Z6wFZMK/oMD4Y370aPVXUcybN09ZWVmKi4tT27Ztq2xbtWqVXn75Zf36669uq09ycrLTx4SHun71kKPmfDpdyUltNXbQ9bJYLLptwhu69qk0De52jnqknuzUuZo1b+72q3bMxl194K22F5UU6r43ztbALmN19uCpbjmnu98HAI6XlJhQ47Y8Bz5+8ZHGFzqbTcqr5XlvNZ3LarGpRYsWdRfkQ+HRrif8o8Ll9+2rr5CQkMr/+nNbzRYLSe4dB4M5Fqrg6T4gFgLMy2qx1bitrnjI0ViotnMlNYiXxY/HUEmy2opdOs5Wku/X8YE7EAtVxdyQe5ktHnR3PEQsBHhHfFzNCTBvzA1FhFr9egyVpPho11NfcVFhft+++iAWqsrdc0OS+XJF7mS2eNAfYiGbzaaMjAxJUtOmTV0uOyAT/snJycrJydHq1as1cODAKtsyMjJ02223SZJ69Oghi8VSua28vFzXXHONpk6dqq5du7qtPqtWOb+scHmJtPgZt1XhOL/8/qWWrJunl25ZX9kHzRu105WjHtUT867QnOnrFRUe4/D5tm7ZqpBw99XP0+33BHf1gbfavmzDfO3IWKe9WVu0ZN2847a/eusmNUls5dQ53f0+AHC8I0elez6qflt1y6z93f3nGFdv5xVJ93/sfPkjT+ql/9yZ7vyBXvbU19LOLOeOsVikT956TIkxj3mmUn7ivo+kw0elZsnNKh9x5I/MFgtJ7h0HgzkWquDpPiAWAszrjWXS2t3Vb6srHqpvLCRJK5Z8rljXry/0il93Sv/5wfnjzh3RSa9P99/4wB2Ihf7C3JD7mS0edHc8RCwEeMf2A9Lsb6vf5o25ocmTztPHs85z/kAvKiuX/rmg7gs8/y4mQvpl8fsKC/FItfwCsdBfPDE3JJkvV+ROZosH/SEWKigoUGxsrCRp+fLlzh18jIBM+I8cOVKbN2/WY489plNPPVUdO3aUJK1cuVITJ05UVpaRAUhLS6ty3LPPPqv9+/fr/vvv93KNva9fp1Fa8GDuca+PGzxF4wZP8X6FfOzf1y3xdRW87tTeE3Vq74m+rgYAJ8VFSQlRRmDuCy2TfFOus4Z0dD7h362FlOh8DA+TIhaqilgIgJmkJNWc8Pe0xGj5fbJfknq2lD6OlPKdmOS2SBrU3mNVgh8iHqqKeAiAWaQkGuN2zQtWe5YZ5oZCQ6QB7aVvfnPuuP7tFNDJflRFLFQVsZC5WX1dAU+YMWOGGjZsqD179qhr167q3r27OnTooH79+ik1NVXDhw+XJPXs2bPymKysLN1zzz269957VVZWptzcXOXm5kqSioqKlJubK5ut5mUDAQDwlpYNfVi2Cb7USVJaKym55qcfHCfEKo103+I+AADAg1r5MhbyYdnOCA2RTnUytunfTkqK9Ux9AACA+0SESY3jfVd+iknmhgZ3lGIjHN8/JkI6qaPn6gMAnhSQCf+UlBQtW7ZMZ555piIjI7Vz504lJSVpzpw5+vzzz7VlyxZJVRP+6enpOnLkiK655holJiZW/kjSY489psTERO3e7aNbCAAAOEa3FN+UGxMhtWnsm7KdFRoiXTNMaujApLXVIk0cJLVu5Pl6AQCA+mvbWIr20ZLRvorDXHHyCdIpnRzbt3Nz6by+nq0PAABwn+4+iklaJhkrT5pBQpQ0eZgU5UDcGBkmXXUKKz8CMK+AXNJfkjp37qzPPvvsuNfz8/O1c+dOWa1WdevWrfL19u3ba/HixcftP2zYMF1++eWaNGmSkpOTPVpnAAAc0au19N/V0tES75Y7wGTLmiXGSDedJi1YbSz7W17NQj2tG0pnpUkdGOIBADCNsBDjbvTFm71bbnS4sYqQWVgs0tm9pCZx0sJNUk7B8ftEhxuPQjq9u7HiEQAAMIdBHaRFm7y/rP/gjkaMYRatGhpzQ/9dLW3eV31/dWomjeslNWvg7doBgPsEbMK/Jhs3bpTdblfHjh0VHR1d+XpsbKyGDh1a7TFt2rSpcRsAAN4WHir1S5W+/917ZVpkfJk0m7goaeJg6eze0sod0pfrpdJyow9vGGmeZXkBAEBVgztISzZ7d5K7fzsjhjATi8WYmB/Y3pjk3pwh/bzdiIeiwqT7zzFfmwAAgLGiYefm0qZ93iszKty4CcVsmiYYd/pnHZFW/mlcKFFaLkWESreOlhrH+bqGAFB/QXf99oYNGyRVXc4fAACzObWrFBvpvfJO6eTY8vj+Ki5SGt7lr+V/o8JI9gMAYGaN4qSTTvBeeXGR0siu3ivP3axWqWuKsWx/RTwUHkqyHwAAMxvby7sr9JyVZu7YoVGcNKrHX7FQZBjJfgCBw8R/nl3jbMLfbvf2ojhwxXMLpmnFpk+0P2eXXrhpjdq3SKtxX7vdrhlzRmjr3tVa8GBu5evvL3lc3656Uza7TS0bn6BbL3hdsVENPF53AHBFbKR0fl/p9WWeL6txnDSa6+QAv3f7S6cp50imLBaroiPjNGXcM2rf4sTj9vvyl1f13uJHZbfZlNZ+uKaNf16hIWGV22uKlfxZSWmRHnrnQu3av0kRYVFqENtE08a/oBaN2lfZLyP7Tz341nkqt5XLZitTy6addfO5LykuOlGS9N7ix/TtqjcVGhKu8LBITRn3jDq16ueLJgFwwFlp0qa9Ula+58ua0E+KifB8OYCrHB0LJelAzm7N/niK0rO2yGoJ0ZiB1+nsITdIYiwEADNJTjAS2J+t9XxZHZOlQccPKQAAP0HCP0A5mgB3dL+vVr6uf7//f7r/8o81uNvZHqu3q07qcZ4mDJ2hm58fUue+85c+qWYN22nr3tWVr/265Vt9vfJ1zb7hZ0VHxumdhf/Sa1/epWnjn/Nktd0q/eBWPT7vch0uyFJMZIJuu+ANtUk+/haUSx9uo7DQCIWHRkmSLhp+p4amXSCp9gmClb9/pde/vltlZSWKCI/WTefOUbvmgf05Avxdz1bG0v6/7HD8mLyjVf9bl1CrdMkgc1/BjeDjzIT3z5u/0Btf3y2bzSabrUznD71Np/W5XJLjCXR/cc/E9ysvVly+4WM9Pm+S5tyyrso+Gdl/6o2v79ELN65WYlxT3fvGOH3+00saN3hK5T7VxUpmMLr/ZPXrNEoWi0ULfnhWsz64Sv++bkmVfRrGN9eTU5YrIsyIg577741669v7NWXc09q2d60+/fF5vXLrRkVFxGrhr2/r2QVT9ey0X3zQGgCOCA814pTnFkplNseOcTYWkqQB7aTuLZ2vH+BtjoyFdrtd9795ji4YdodO6Xm+JCnnyH5JCrix0JE5r7rixtpiRX/mTDxcoaa5P7P2ARAshnU2Htuz/YDjxzgbD8VGSBf2Nx4VBPg7R/IkdY2TjsQQ/p4zq+BoDrC2/E9ewSHdNmdE5b7FpYXKyN6hD+47oPjoJG80wy3c0Rf+LOim7xctWuTrKniFowlwR/bLzN6pL39+WZ1bDXB3Nd2mR+rJDu23M3Ojfty4QLdOeF1L139Q+fqOfevUre0QRUcaa/j06zRat7441FQJ/6fnX6PR/Sfr9L6TtHT9h3p83iQ9d+PKave965J5Nf4xq26C4P7LP9Yjcy/RrOuWqk1yV23YsUyPvnuJXr71Nw+2CIAjLugvFZZIv6U7tv+srxw/d4hVmnSS1KaRa3UDfMnRCe/H5l6qJ65dotTmPZSZvVP/93gnDek2XtGRcQ4l0P3JsSsTFRQdlnT8bMyy9R9qYJexSopPliSdNeBazV30cGXCv6ZYyd+Fh0Wqf+fRlf/u3GqAPvz+ieP3C/3r9txyW7mKSgoUFW48r8RisajMVmq8FhGr/KJcNUpI8XzlAdRL28ZGvPL6MqncgaS/M7GQJHVPkc7n5maYgKNj4Zqt3yksNKIy2S9JiXFNJQXeWOjo3FhNcWNdsaK/cyQerlDT3J/Z+wAIBiFW6apTpOe+k9KzHTvGmXgoKly6driUZOLHPCK4OJonqW2crCuGMEPOrIIj8dCRwpxa8z/xMQ0155a1lft/sOQJrd/xvamS/ZJ7+sKfefEJL/CmHqknq3GDur+U1bWfzWbTrA+u0pSzZyss1NzrF5aVl+rJD6/WjefOkdUaUmVbh5TeWr11obLzMmW32/XdmndUWHxEeYUORkk+lpN/QFvSV2lkr0slSSd1P1cHc/dob9Y2p85TMUFg+d/lmp1bDdD+nJ3ad2i74qMbVl4J1z31JB3I3a2t6ea68w8IRCFW6YqTpD5t3HveiFDjC2M3887vIYjVNJ5Vy2JRflGuJKmwKE/x0Q0rYx5HEuj+5rG5l+nif7XUm1/fozsu+s9x2w/k7lbTxNaV/05OaqMDubsl1R4rmc3Hy5/WwK7jqt1WWlaia2al6bz7G2lv1lZdftoDkqR2zXvq3JNu1sRH2uqif6Xoo6VPaurZs71ZbQAu6pZixC0Rbr6loW9b42ICbz4bF3CXmsbCXQc2KSGmsR56+0Jd++SJuv+Nc5RxyFgyLNDGQkfmxuqMG2uJFf2ZM/FwnXN/Ju0DIJhEhUtTRkjtmrj3vAlR0g0jpRRz5fQQxBzNk9Q1TtYWQ5gtZ+ZIPORs/ufLla/qjH5Xur2unuaJvvAnQXeHP5wzf+ksdW0zWB1Tevu6KvX2n28f0JBu49W6aWdlZu+ssi2t/TCdf8qtuvv1sxRiCdHgbudIkkKs5viIHMzdo6T4ZgoJMeprsVjUJLGVDuTurna5tpnvXSa77OrUsp+uHP2oGsQ2rva8FRMEKY06KK/wkDbu/FFd2wzSjxs/UWHxEWXm7FSHlF4ebRuAuoX8b9n9Ts2lj1YZd/zXR8dkY6k2rt5GoKhpwttisejuS+bpgTfHKzI8RvlHc3TfZR8pLDS8cp/H5l6mddsXS5IeuvILr9XZVbdf9JYk6ZtVb+rlL27Xw07UubZYyUze/e5h7cvappnXfFft9rDQcM25Za1Ky0r03IIb9NlPc3TBsBnKyP5Tyzd8pDdu36ZGCc214Idn9a+3L9BTU5Z7uQUAXNG5uTTjTOm9n6St++t3ruhw6dy+Uq/WLF0Lc6ptLCwvL9Pa7Yv0zNSf1Ca5qz5d8aIefHuCnr9xFWOhqsaNjsSKZlHbxZC1zf0FUh8Aga4i6b/kd+mLdY4/7qgmfdpK5/SWYvw/nwlUcjZPUqG2cfLvAilnVsGZ/M/GnT8qvzBHAzqf5aPaepaZc2HmyGaiimmzB2pv1tZqt71w8xo1aeCehwv+mfmblm2Yr1nXL3XL+Xxt/Y7vdSBnt/7747Mqt5WpsDhPlz7cRs9OW6kGsY01dtD1GjvoeknSpl0/qXFCimIi431ca0Ndv3NnzLpuqZoktlJZeale/+puzZx3ebXJgGMnCCLDo3XvxA/16pd3qqg4X51bD1Trpl1Mc0EEEAwsFuPLWIdk44vd6p1Sablz52gcJ43oKvVPZXIb/s2ZWKiuCe93vvuX7rv8I/VIPVl/7Fmpe18fq5emb1BCjPEsi/ok0H3ptD6X6+n51yqv4JDiYxpWvt6kQSvtO7S98t+Z2TvVpEErSXXHSmbwwZIntPy3jzRz8kJFhkfXum9YaLhO63uFnvzwal0wbIaWr5+vts26q1FCc0nS6X2v0HMLblBpWQmT2oBJNIyVrh8h/bRdWrRJOnjEuePDQqTebaTRPaX4KI9UEfC4usbCJomt1L75iZV3LY3sPVGzP75eZeWlphoLPTE39ve40ZFY0VfcFQ/XNffnz30A4HhWqzS8i9S1hfT5OuPxjza7c+do1VA6vZvUlRUf4YfcmSepUNdNA8fyt5yZu+KhmKgEh/M/X/3yqk7tfVnlRRX+whd94W/8v4Y4zjM3rPBKOb/tWKb9OTs16bEOkqTsI5l66sPJys7L0JhB13mlDu705PXLKv8/M3unrn0yTW//Y2fla4fyMtQwvpmKSgr15tf3asLQGT6oZfXq+p2HhUYoOy9D5eVlCgkJld1u14Gc3ZUT+Mdqkmi8FhoSpvEn3aQrZnY8bp/qJgjS2g9TWvthkqSSsmJd8M9ktW7apb5NA+BmCVHSRQOksSdKv+yQ1u6W9uXUnPxPiDKefTuwvXFnP4l+mIGjsVBdE97b9q3Vobx96pF6siTphJZ91SghRdv2rlHvjqdW2bemBLq/yD+aq6KSwsoJ+h9+W6D4mIaK+9vz1E7qfq5uen6ILjv1fiXGNdVnP72ooWkXSqo7VvJ3H34/S4vXztVjkxdWeRzDsfbn7FJCTGNFhkfLZrNp6foPlNqshyQpuWGqvl71uo4W5ysqIlY/b/pMKY07+l2CA0DtLBYjrunfTtqaKa3YJv15UDp8tPr9w0KkFolSWiupX6oUzV1sMDFHxsK+nUbp5c9nKOvwXjVKaKFfNn+hVk06KzQkzFRjobvnxqqLG52JFb3NXfFwXXN//twHAGrWNEH6v5Ol3ELpx63Spr3Svtyak/+NYqX2TaVBHYyEP+Cv3JknkZy7aUDyv5yZO+MhR/I/R4vz9f369/XstJVuK9ddvN0X/oiEP2o0ZtB1Vf5ITX9hqMafdJMGdzvbd5WqwVMfXqOff/9c2Ucydecrpys6Ik5v3rFN//7gKg3sMlaDuo6t8xx3vHya7HabSstLNLLXRI0bPNULNXePxNgmat+ilxauflun952kZRvmq1GDlOOWqTlaUqDy8tLKL/6L18xV++YnVtmnpgmCigsiJOmdhQ8qrd3wWpfBAeBbMRHSsM7GT7lN2n/YuMuttFyyWoyl3lISpTjuXkOAcmTCu0mDlso+kqFd+zerddPO2pu1TRmHtqtl4xMcTqD7i4Kiw3rwP+eruPSorBarEmIa68ErPpPFYqkSDzVrmKrLT3tANz03WJLUs91QnTXgGh/Xvv4O5qZrzmfT1SwpVbe+aHwpCw+N0OxpP+uNr+9Vw/jmGjPwWu3IWK/Xv7xLkmS329S+RS9NGfeMJGlIt3O0Zc9KTXm6j8JCIxQZHqM7L37XZ20CUD9Wi3RCM+NHko4cldJzpKMlxmR3eKixulGTeOPxSIDZOToWRoXH6MbxL+quV8+UZFdMZILuuuQ9ScE7FtYUN9YWK5qBI/FwXXN/Zu8DINg1iDZWLhrdUyorN5L+OQXG3FCo1Zg7apFkPM4ICASO5kkkx8bJvzNTzsxZjuR/lqybp9RmPdWqSSdfVNFrzJoLI+EfoGpKgEuqMulb235mctN5c6p9ffr5r1T7enJSGy14MLfKay9P3+DuannVTefO0ePzJmnuoocVHRmv2ya8Xrmt4nfeNrmbHnjrXNls5bLLrmZJqZpx4VuV+9U2QfDm1/fqtz+XqdxWps6tB2r6hFe93kYArgmxSs0TjR8gGNQ2nklVY6Gbzn1J/3p7gqwWq2x2m6ae86yaJLbS/pxdNSbQ/VHTxNZ6dtov1W77ezw0uv/VGt3/6lrPV12s5M8aN0jRt49Xf7vKpNP/Wfn/A7uM0cAuY6rdz2Kx6MrRj+jK0Y94pI4AfCsuSurMhY4IYI6OhZLU54TT1OeE047bL9DGQkfmxjq06FVj3JgY17TGWNHfORMP18bMfQCgqtAQ4+597uBHoHMkT1Lb+C/VHkOYjaO5QkfyP1/98qpG1TGf5M/c2Rf+yGK32518igu8obxEWvyMr2vhuGHTpBA3XglotvZL7usDM7a9grvfBwDgbvd9ZCzpmxAlPTDe17XxDbP0gRnHQ3eOg8Hefok+AABPMUss4ClmaT/jIH0Q7O0HAE8xSyzgKWZpvxnHwQrBnCsK9njQlfYXFBQoNjZWkpSfn6+YmBiXymbhOgAAAAAAAAAAAAAATIiEPwAAAAAAAAAAAAAAJkTCHwAAAAAAAAAAAAAAEwr1dQVQPWuY8awHs7CGuf98Zmq/5L4+MGPbK7j7fQAACF5mHA/dOQ4Ge/srzhfsfQAACF6Mg/RBsLcfABDczDgOVgjmXFGwx4O+jIVI+Pspi0UKCfd1LXwnmNsfzG0HAKBCsI+Hwd5+iT4AAAQ3xkH6INjbDwAIboyD9IFEHziDJf0BAAAAAAAAAAAAADAhEv4AAAAAAAAAAAAAAJgQCX8AAAAAAAAAAAAAAEyIhD8AAAAAAAAAAAAAACZEwh8AAAAAAAAAAAAAABMi4Q8AAAAAAAAAAAAAgAmR8AcAAAAAAAAAAAAAwIRI+AMAAAAAAAAAAAAAYEIk/AEAAAAAAAAAAAAAMCES/gAAAAAAAAAAAAAAmBAJfwAAAAAAAAAAAAAATIiEPwAAAAAAAAAAAAAAJkTCHwAAAAAAAAAAAAAAEyLhDwAAAAAAAAAAAACACZHwBwAAAAAAAAAAAADAhEj4AwAAAAAAAAAAAABgQiT8AQAAAAAAAAAAAAAwIRL+AAAAAAAAAAAAAACYUKivK4Dq2e2SrdTXtXCcNUyyWNxzLrO1/Vju6gcz9oE73wMAAAT7WBjs7ZfoAwBAcGMcpA+Cvf0AAAT7WBjs7ZfM1we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qHOebsgEAqEnATmnOmDFDDRs21J49e9S1a1d1795dHTp0UL9+/ZSamqrhw4dLknr27FnluMTEROXm5h53vuzsbCUlmWNmIyFa6tO27v2O1TfVOA4A/J0vE+7xkfytBMxiaCfjgkZHRYYZF00CgBn4Mh5q2dB3ZQNwXNvGxo8zhnaSQkM8Ux8AcCdfXoCYkuTcd00AALwhYBP+KSkpWrZsmc4880xFRkZq586dSkpK0pw5c/T5559ry5Ytko5P+Hfu3FmbNm067nybNm1S586dvVJ3dzivr9SuiWP7tmsindvHs/UBAHdJiJaaNfBN2Sc09025AJzXupF04QDJkXmYsBDpypONZR0BwAw6+ygmCbFK7R38ngnAtywW6f9Ocvwu1D5tpeFdPFsnAHCX1g2lKB89QqVTM9+UCwBAbQI24S8ZyfvPPvtMR44c0ZEjR/Tzzz9r8uTJKigo0M6dO2W1WtWtW7cqx5x11llavny50o95JuHPP/+s7du3a8yYMd5ugsvCQ6Vrhhl3qtW0NG2I1dh+zTBjfwAwiyEdgqtcAK7plyr938nG0tc1aZYgTR0pdUj2Xr0AoL56tTFWJvG2E1sbS34DMIe4KOnG06RuKTVfBBkRKp3eXbp4IHesAjCP8FDj+563hVilAawMBwDwQ0GZ5t24caPsdrs6duyo6Oiqt3JNnjxZs2fP1rhx4/TAAw+oqKhIM2bMUL9+/TRu3Dgf1dg14aHSBf2l0T2kn7ZL2w5I2/ZL5TbjC93d46Q4JmsAmFDvttIna6TiMu+V2TJJasUStoDpdG8pdW0hbd4nrfxT2pBuxEJhIdJ1w42lbi1MbgMwmYj/TXIv/cO75Q7p6N3yANRfbKR01SlS1hHpx23S978bsVCoVTq7t3Fnvy8uIAKA+hrcwfib5k1prZhPBwD4p4C+w78mGzZskHT8cv6SFB8fr0WLFqlZs2a68MILddVVV2nQoEH67LPPZLWas7vioqRTuxmT2rERxmuRYQQnAMwrMkw6rVvd+7nTWWkkBQGzslqlrinSpJP+ioWiw6XUJnyuAZjXiC7eXcq2W4qxfC4Ac2oUJ4098a9YKCbCuIiHZD8As2oSLw1o573ywkKk07p7rzwAAJwRlHf415bwl6R27drps88+82aVAABOGtpZWrdH2n3I82UNbC+dwDPaYBIlpUV66J0LtWv/JkWERalBbBNNG/+CWjSquu7gyj++1iuf317579yCA0qKS9YLN632dpUBAC5IiJbO6S29u8LzZUWHS+f34yIp+LfnFkzTik2faH/OLr1w0xq1b5FW4752u10z5ozQ1r2rteDBXEnS0eJ8PfDWudqa/qvKbWWVrwMA/Ne4XtLvGVJuoefLGt1Tahrv+XIAAHAFCX8AgCmFWI3nTM76SipxcGn/vKNV/+uIRrHGF0gzSD+4VY/Pu1yHC7IUE5mg2y54Q22Sux6336UPt1FYaITCQ6MkSRcNv1ND0y5waPvtL52mnCOZslisio6M05Rxz6h9ixO90DrnOTrpW1JWrDmfTteqLV8rPDRS7Zr11B0Xv125va7+8kej+09Wv06jZLFYtOCHZzXrg6v07+uWVNmn7wmnq+8Jp1f+++7XzlLPdsO8XFMAQH30bStt2GM8rsRRrsRD5/WVEqKcq5svOBoL1TX213QeRy+q8yVH+6DCVytf17/f/z/df/nHGtzt7MrXa4v5/DU2OqnHeZowdIZufn5InfvOX/qkmjVsp617/7rQMSQkTBcMu11xUUm69cWhHqwpAMBdosKliwZILy6W7HbHjnElFmrXRDrlBOfrBwCAtwRlwn/RokW+rgIAwA2SE4znUb60WCqz1b3/rK+cO398lHTtcPMsc/n0/Gs0uv9knd53kpau/1CPz5uk525cWe2+d10yr9a7nmrafs/E9xUb1UCStHzDx3p83iTNuWWdG2rvfo5O+r76xR2yWCx6Y8YWWSwWZedlHrdPXf3lT8LDItW/8+jKf3duNUAffv9ErcdkHd6nNVu/0/QJr3m6egAAN7JYpImDpRcXSTsOOnaMs/HQ2BOlXm2crppPOBoL1TX213YeRy6q8yVn4sHM7J368ueX1bnVgOO21RXz+WNs1CP1ZIf225m5UT9uXKBbJ7yupes/qHw9PDRCJ7YfrszsnR6qIQDAE05oJl3QX3rvJ8f2dzYWat5AuvJk41FxAAD4K4YpAICpdUyWrhkmRbj5EraGsdK0U41nXZpBTv4BbUlfpZG9LpUkndT9XB3M3aO9WdvcWk7FxK8kFRQdluS/a/v2SD1ZjRuk1LrP0ZICffXLq7rijIdk+d86xUnxyd6ontd8vPxpDew6rtZ9vln1hvp1Gq3E2CZeqhUAwF3CQ41Y6AQPDF/n9JaGd3H/eT3B0ViorrG/tvNUXFRXcVznVgO0P2enF1rnGGfiQZvNplkfXKUpZ89WWGjEcdvNFPM5o6y8VE9+eLVuPHeOrNYQX1cHAOAmA9oZq0Ba3TxctW4oTRkpRR8/VAIA4FeC8g5/AEBg6ZAs3TZaeu9nadv++p+vX6p0dm/jebVmcTB3j5LimykkxBjaLRaLmiS20oHc3dUuMzvzvctkl12dWvbTlaMfVYPYxg5vf2zuZVq3fbEk6aErv/BgqzwvI2u74qKTNHfRw1q9daEiwqI08dT71avDiCr71dVf/urd7x7WvqxtmnnNdzXuY7fb9fXK13T9uGe8WDMAgDtFhEmTh0mLN0tfrpfKHVj5qDYNY6WLB0jtmrqnft7gaCxU19jvTEzlyEV13uRM3ecvnaWubQarY0rvGs9XW8xn1tjoP98+oCHdxqt1087cyQ8AAaZfqrES5LsrpMzD9TuX1SKN6CKd3l0K5fowAIAJkPAH/p+9+46Pqsr/P/6eNFIggdACJJQQeu9SVKKoFLFgF1nx54oNUUFxd13b111UxAY2rOi6iyiia0cRUcAG0hHpAQIJENJ7mfn9cZdAJCEzyczcuTOv5+ORR2Duufd8zs2duZ85595zAfiFZo2k286VftgpfbXFtWexHRcXY0xb272N++Orr2nzhupgxs5ql71093qXtvX0rd+rRZO2Kq8o05tf/l2zF12vWSd14ta2/L5r3pYkfbX2Lb36+X1VlnlLbfujReMEp7ZTYS/X4ax9ateiu/489nHtOrhe971ynl67Z6uaNDJGOWrbH77q/RVztGrLEs2eskzhYZE1ltu05zuVlhdrYJcLvBgdAMDdgoOkUT2knvHSkrXSjlOfUFOrsBBpWJI0po/7Z0+qL3flQs6c+53hzEV17uaufbA3fYtWbv5AT9/2/WnL1ZTzWTU3koy850jWfv33h+dVYS9XYUmurpvVXs9PW2OZixYAADVr21S6Z4z09Vbpu9+l4jLXt9GhmXTpQGNbAABYhY99hQcAoO6CbNKIztLQJGlzqrR6h/E829Pd5dYgROrW2livYwvjWbi+aO4dP552eWhIA2XmpqmiolzBwSFyOBw6krVfLRq3PaVsiybGayHBoZpw5l26YXZnl5Yfd/7A6/XcB7cot+CYoqO8+024tv3hrBZN2irIFqRz+k+UJCW16ae42A7am7a5stPf2f3hSxZ/97S+3bBQT0xZVmVK3up88cvrOn/gZAUzrS0A+IW4GOMiyPQc40LI9fukvOKay9sktWoiDe0oDewgRfjoDEfuyoVqO/c3b5xQ63acvajO3dy1D7bsWanDWSma/EQnSVJmXrqeXTxFmblpGj/s1lO2+8ecz4q50XHP3Lay8t/pmSm65Zm+eudvKeYFBABwu5BgaUxv47FE61KkH3dJBzIlh6PmdSLDpD5tpeGdpPhYr4UKAIDbMOAPAPA7wUFS37bGT3mFlJYjpWZKH/0qlZQbg/xXDJYSYqXm0e5/xpsZmjRsoaQ2/bVs3Tu6YNBkrdz8gZo1jj9l+tai0gJVVJRVDgJ/u36hklr3c2p5flG2iksL1SymtSRp9ZaPFB3VVI0irfttOCaqmfomnau125dqSLexSsvcq/TMvWrbspuk2veXLzqanar5n85Qq9hE3fNysiQpLKSB5k37WQuWPqim0a01fugtkqSCohyt3rxEr8zYbGbIAAAPiIuRJgyULh0g5RRJB45JR3KNmZBKyqXwUOmmkVKbJsa/rc7ZXKi2c39t23Hlojpvc3YfjB92a5WB/RkvjdSEM+/S8J6XSDp9zufLudGzi2/Wz79/psy8dP31tQsU2aCR3vrLLj31/p81tPtFGtbjolq3MeWp3sopOKrCklxd84949emYrL9c8y8vRA8AcLcGIcYNIUOTpNJy6WCW8fPJ+v/lQiHSNUONAf7YKN+9AQQAAGcw4A8A8GshwcbAfkKs9OWmEx3cAzuYHZn73XXZfD25aLIWLp+lyPBo3Xvlm5XLjnd0dojrqUfevkx2e4UccqhVbKJmXv12ZbnsvMM1Li8oztGj/7pCJWVFCrIFKSaquR694VPZfPRbcU2dvpKqdPzeddnLeur9G/Xa5/cpyBakuy6br2YxxnMdTrc/fFXzxvH6+snqb12YfMH/Vfl/VESMPplV4I2wAAAmsdmkxpHGjyR9v/3EBZAdW5gbm7s5kwvVdu4/3XZOd1Gdr3B2H5zO6XI+X86N7rp8frWvz7jitWpfj4ttr48eza7y2iszNrk7LACADwgLkTo0N36+/t/Fjw1Cjbv6AQDwBzaH43ST2aA+CgoK1LBhQ0lSfn6+oqKiTI5IemiJcXdHTIT0yASzowEA77LKZ2BFqfTtXLOjqJvkaVKwG6YCtuI+cFfbj7PaPnB3+z3FKp8DAOApVvgctNo58GSBnAsdF8j7wAr5kBU+AwDAk/gcBAD4oyCzAwAAAAAAAAAAAAAAAK5jwB8AAAAAAAAAAAAAAAtiwB8AAAAAAAAAAAAAAAsKMTsAAABQVVCo8fxPKwoKdd92rLYP3NX2k7dnpX3g7vYDAAKX1c6BJwvkXOi4QN4H5EMAAAAAzMCAPwAAPsZmk4L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",
+      "text/plain": [
+       "<Figure size 2628.61x1120.39 with 1 Axes>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Ansatz\n",
+    "layers = tq.RXYZCXLayer0(\n",
+    "    {\n",
+    "        \"n_blocks\": 6,\n",
+    "        \"n_wires\": n_wires,\n",
+    "        \"n_layers_per_block\": 1,\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "# We use `tq2qiskit` to visualize the ansatz.\n",
+    "qdev = tq.QuantumDevice(n_wires=n_wires, bsz=1, device=\"cpu\")\n",
+    "tq.plugin.qiskit.tq2qiskit(qdev, layers).draw(output=\"mpl\", fold=30)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a14839c3-d9ff-44dc-adf6-efeebae18bfe",
+   "metadata": {},
+   "source": [
+    "We can now simulate the circuit to model the gaussian mixture distribution. The algorithm minimizes the kerneled maximum mean discrepancy (MMD) loss to train the QCBM."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6490b2e9-d18d-42e0-9310-a3f644197c8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qcbm = QCBM(n_wires, layers)\n",
+    "\n",
+    "# To train QCBMs, we use MMDLoss with radial basis function kernel.\n",
+    "bandwidth = torch.tensor([0.25, 60])\n",
+    "space = torch.arange(2**n_wires)\n",
+    "mmd = MMDLoss(bandwidth, space)\n",
+    "\n",
+    "# Optimization\n",
+    "optimizer = torch.optim.RMSprop(qcbm.parameters(), lr=0.01)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "4335ef3e-8dea-47be-a310-8146abc214fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration: 0, Loss: 0.007511706091463566\n",
+      "Iteration: 10, Loss: 0.0008048344170674682\n",
+      "Iteration: 20, Loss: 0.0004957925993949175\n",
+      "Iteration: 30, Loss: 0.0003518108860589564\n",
+      "Iteration: 40, Loss: 0.0002739735064096749\n",
+      "Iteration: 50, Loss: 0.0002034252102021128\n",
+      "Iteration: 60, Loss: 0.00014893575280439109\n",
+      "Iteration: 70, Loss: 0.0001268944761250168\n",
+      "Iteration: 80, Loss: 0.00010558744543232024\n",
+      "Iteration: 90, Loss: 8.735547453397885e-05\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in range(100):\n",
+    "    optimizer.zero_grad(set_to_none=True)\n",
+    "    pred_probs = qcbm()\n",
+    "    loss = mmd(pred_probs, target_probs)\n",
+    "    loss.backward()\n",
+    "    optimizer.step()\n",
+    "    if i % 10 == 0:\n",
+    "        print(f\"Iteration: {i}, Loss: {loss.item()}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "246f5d90-47af-4385-8d41-5ff6fadcb9ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize the results\n",
+    "with torch.no_grad():\n",
+    "    pred_probs = qcbm()\n",
+    "\n",
+    "plt.plot(x_input, target_probs, linestyle=\"-.\", color=\"black\", label=r\"$\\pi(x)$\")\n",
+    "plt.bar(x_input, pred_probs, color=\"green\", alpha=0.5, label=\"samples\")\n",
+    "plt.xlabel(\"Samples\")\n",
+    "plt.ylabel(\"Prob. Distribution\")\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7f5ff14-793c-4dc3-aad2-eed38aa3e5a5",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "\n",
+    "1. Liu, Jin-Guo, and Lei Wang. \"Differentiable learning of quantum circuit born machines.\" Physical Review A 98.6 (2018): 062324."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python (tq-env)",
+   "language": "python",
+   "name": "tq-env"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/QCBM/qcbm_gaussian_mixture.py b/examples/QCBM/qcbm_gaussian_mixture.py
new file mode 100644
index 00000000..fdc2acbd
--- /dev/null
+++ b/examples/QCBM/qcbm_gaussian_mixture.py
@@ -0,0 +1,129 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+from torchquantum.algorithm import QCBM, MMDLoss
+import torchquantum as tq
+import argparse
+import os
+from pprint import pprint
+
+
+# Reproducibility
+def set_seed(seed: int = 42) -> None:
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    # When running on the CuDNN backend, two further options must be set
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+    # Set a fixed value for the hash seed
+    os.environ["PYTHONHASHSEED"] = str(seed)
+    print(f"Random seed set as {seed}")
+
+
+def _setup_parser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--n_wires", type=int, default=6, help="Number of wires used in the circuit"
+    )
+    parser.add_argument(
+        "--epochs", type=int, default=10, help="Number of training epochs"
+    )
+    parser.add_argument(
+        "--n_blocks", type=int, default=6, help="Number of blocks in ansatz"
+    )
+    parser.add_argument(
+        "--n_layers_per_block",
+        type=int,
+        default=1,
+        help="Number of layers per block in ansatz",
+    )
+    parser.add_argument(
+        "--plot",
+        action="store_true",
+        help="Visualize the predicted probability distribution",
+    )
+    parser.add_argument(
+        "--optimizer", type=str, default="Adam", help="optimizer class from torch.optim"
+    )
+    parser.add_argument("--lr", type=float, default=1e-2)
+    return parser
+
+
+# Function to create a gaussian mixture
+def gaussian_mixture_pdf(x, mus, sigmas):
+    mus, sigmas = np.array(mus), np.array(sigmas)
+    vars = sigmas**2
+    values = [
+        (1 / np.sqrt(2 * np.pi * v)) * np.exp(-((x - m) ** 2) / (2 * v))
+        for m, v in zip(mus, vars)
+    ]
+    values = np.sum([val / sum(val) for val in values], axis=0)
+    return values / np.sum(values)
+
+
+def main():
+    set_seed()
+    parser = _setup_parser()
+    args = parser.parse_args()
+
+    print("Configuration:")
+    pprint(vars(args))
+
+    # Create a gaussian mixture
+    n_wires = args.n_wires
+    assert n_wires >= 1, "Number of wires must be at least 1"
+
+    x_max = 2**n_wires
+    x_input = np.arange(x_max)
+    mus = [(2 / 8) * x_max, (5 / 8) * x_max]
+    sigmas = [x_max / 10] * 2
+    data = gaussian_mixture_pdf(x_input, mus, sigmas)
+
+    # This is the target distribution that the QCBM will learn
+    target_probs = torch.tensor(data, dtype=torch.float32)
+
+    # Ansatz
+    layers = tq.RXYZCXLayer0(
+        {
+            "n_blocks": args.n_blocks,
+            "n_wires": n_wires,
+            "n_layers_per_block": args.n_layers_per_block,
+        }
+    )
+
+    qcbm = QCBM(n_wires, layers)
+
+    # To train QCBMs, we use MMDLoss with radial basis function kernel.
+    bandwidth = torch.tensor([0.25, 60])
+    space = torch.arange(2**n_wires)
+    mmd = MMDLoss(bandwidth, space)
+
+    # Optimization
+    optimizer_class = getattr(torch.optim, args.optimizer)
+    optimizer = optimizer_class(qcbm.parameters(), lr=args.lr)
+
+    for i in range(args.epochs):
+        optimizer.zero_grad(set_to_none=True)
+        pred_probs = qcbm()
+        loss = mmd(pred_probs, target_probs)
+        loss.backward()
+        optimizer.step()
+        print(i, loss.item())
+
+    # Visualize the results
+    if args.plot:
+        with torch.no_grad():
+            pred_probs = qcbm()
+
+        plt.plot(x_input, target_probs, linestyle="-.", label=r"$\pi(x)$")
+        plt.bar(x_input, pred_probs, color="green", alpha=0.5, label="samples")
+        plt.xlabel("Samples")
+        plt.ylabel("Prob. Distribution")
+
+        plt.legend()
+        plt.show()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/QuantumGan/ README.md b/examples/QuantumGan/ README.md
new file mode 100644
index 00000000..ea8f3f76
--- /dev/null
+++ b/examples/QuantumGan/ README.md	
@@ -0,0 +1,74 @@
+# Quantum Generative Adversarial Network (QGAN) Example
+
+This repository contains an example implementation of a Quantum Generative Adversarial Network (QGAN) using PyTorch and TorchQuantum. The example is provided in a Jupyter Notebook for interactive exploration.
+
+## Overview
+
+A QGAN consists of two main components:
+
+1. **Generator:** This network generates fake quantum data samples.
+2. **Discriminator:** This network tries to distinguish between real and fake quantum data samples.
+
+The goal is to train the generator to produce quantum data that is indistinguishable from real data, according to the discriminator. This is achieved through an adversarial training process, where the generator and discriminator are trained simultaneously in a competitive manner.
+
+## Repository Contents
+
+- `qgan_notebook.ipynb`: Jupyter Notebook demonstrating the QGAN implementation.
+- `qgan_script.py`: Python script containing the QGAN model and a main function for initializing the model with command-line arguments.
+
+## Installation
+
+To run the examples, you need to have the following dependencies installed:
+
+- Python 3
+- PyTorch
+- TorchQuantum
+- Jupyter Notebook
+- ipywidgets
+
+You can install the required Python packages using pip:
+
+```bash
+pip install torch torchquantum jupyter ipywidgets
+```
+
+
+Running the Examples
+Jupyter Notebook
+Open the qgan_notebook.ipynb file in Jupyter Notebook.
+Execute the notebook cells to see the QGAN model in action.
+Python Script
+You can also run the QGAN model using the Python script. The script uses argparse to handle command-line arguments.
+
+bash
+Copy code
+python qgan_script.py <n_qubits> <latent_dim>
+Replace <n_qubits> and <latent_dim> with the desired number of qubits and latent dimensions.
+
+Notebook Details
+The Jupyter Notebook is structured as follows:
+
+Introduction: Provides an overview of the QGAN and its components.
+Import Libraries: Imports the necessary libraries, including PyTorch and TorchQuantum.
+Generator Class: Defines the quantum generator model.
+Discriminator Class: Defines the quantum discriminator model.
+QGAN Class: Combines the generator and discriminator into a single QGAN model.
+Main Function: Initializes the QGAN model and prints its structure.
+Interactive Model Creation: Uses ipywidgets to create an interactive interface for adjusting the number of qubits and latent dimensions.
+Understanding QGANs
+QGANs are a type of Generative Adversarial Network (GAN) that operate in the quantum domain. They leverage quantum circuits to generate and evaluate data samples. The adversarial training process involves two competing networks:
+
+The Generator creates fake quantum data samples from a latent space.
+The Discriminator attempts to distinguish these fake samples from real quantum data.
+Through training, the generator improves its ability to create realistic quantum data, while the discriminator enhances its ability to identify fake data. This process results in a generator that can produce high-quality quantum data samples.
+
+
+## QGAN Implementation for CIFAR-10 Dataset
+This implementation trains a QGAN on the CIFAR-10 dataset to generate fake images. It follows a similar structure to the TorchQuantum QGAN, with the addition of data loading and processing specific to the CIFAR-10 dataset.
+Generated images can be seen in the folder
+
+This `README.md` file explains the purpose of the repository, the structure of the notebook, and how to run the examples, along with a brief overview of the QGAN concept for those unfamiliar with it.
+
+
+## Reference
+- [ ] https://arxiv.org/abs/2312.09939
diff --git a/examples/QuantumGan/QGan.py b/examples/QuantumGan/QGan.py
new file mode 100644
index 00000000..9b9158c5
--- /dev/null
+++ b/examples/QuantumGan/QGan.py
@@ -0,0 +1,84 @@
+import argparse
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torchquantum as tq
+
+class Generator(nn.Module):
+    def __init__(self, n_qubits: int, latent_dim: int):
+        super().__init__()
+        self.n_qubits = n_qubits
+        self.latent_dim = latent_dim
+
+        # Quantum encoder
+        self.encoder = tq.GeneralEncoder([
+            {'input_idx': [i], 'func': 'rx', 'wires': [i]}
+            for i in range(self.n_qubits)
+        ])
+
+        # RX gates
+        self.rxs = nn.ModuleList([
+            tq.RX(has_params=True, trainable=True) for _ in range(self.n_qubits)
+        ])
+
+    def forward(self, x):
+        qdev = tq.QuantumDevice(n_wires=self.n_qubits, bsz=x.shape[0], device=x.device)
+        self.encoder(qdev, x)
+
+        for i in range(self.n_qubits):
+            self.rxs[i](qdev, wires=i)
+
+        return tq.measure(qdev)
+
+class Discriminator(nn.Module):
+    def __init__(self, n_qubits: int):
+        super().__init__()
+        self.n_qubits = n_qubits
+
+        # Quantum encoder
+        self.encoder = tq.GeneralEncoder([
+            {'input_idx': [i], 'func': 'rx', 'wires': [i]}
+            for i in range(self.n_qubits)
+        ])
+
+        # RX gates
+        self.rxs = nn.ModuleList([
+            tq.RX(has_params=True, trainable=True) for _ in range(self.n_qubits)
+        ])
+
+        # Quantum measurement
+        self.measure = tq.MeasureAll(tq.PauliZ)
+
+    def forward(self, x):
+        qdev = tq.QuantumDevice(n_wires=self.n_qubits, bsz=x.shape[0], device=x.device)
+        self.encoder(qdev, x)
+
+        for i in range(self.n_qubits):
+            self.rxs[i](qdev, wires=i)
+
+        return self.measure(qdev)
+
+class QGAN(nn.Module):
+    def __init__(self, n_qubits: int, latent_dim: int):
+        super().__init__()
+        self.generator = Generator(n_qubits, latent_dim)
+        self.discriminator = Discriminator(n_qubits)
+
+    def forward(self, z):
+        fake_data = self.generator(z)
+        fake_output = self.discriminator(fake_data)
+        return fake_output
+
+def main(n_qubits, latent_dim):
+    model = QGAN(n_qubits, latent_dim)
+    print(model)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Quantum Generative Adversarial Network (QGAN) Example")
+    parser.add_argument('n_qubits', type=int, help='Number of qubits')
+    parser.add_argument('latent_dim', type=int, help='Dimension of the latent space')
+
+    args = parser.parse_args()
+
+    main(args.n_qubits, args.latent_dim)
+
diff --git a/examples/QuantumGan/qgan_generated.ipynb b/examples/QuantumGan/qgan_generated.ipynb
new file mode 100644
index 00000000..01ab6986
--- /dev/null
+++ b/examples/QuantumGan/qgan_generated.ipynb
@@ -0,0 +1,608 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "print('Installing torchquantum...')\n",
+        "!git clone https://github.com/mit-han-lab/torchquantum.git\n",
+        "%cd /content/torchquantum\n",
+        "!pip install --editable . 1>/dev/null"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "znkrtGF4SZry",
+        "outputId": "2ffd0e57-e294-44a5-d1ce-b6ebbf7fc8cc"
+      },
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Installing torchquantum...\n",
+            "Cloning into 'torchquantum'...\n",
+            "remote: Enumerating objects: 15134, done.\u001b[K\n",
+            "remote: Counting objects: 100% (1818/1818), done.\u001b[K\n",
+            "remote: Compressing objects: 100% (470/470), done.\u001b[K\n",
+            "remote: Total 15134 (delta 1498), reused 1461 (delta 1346), pack-reused 13316\u001b[K\n",
+            "Receiving objects: 100% (15134/15134), 97.87 MiB | 11.89 MiB/s, done.\n",
+            "Resolving deltas: 100% (8599/8599), done.\n",
+            "Updating files: 100% (346/346), done.\n",
+            "/content/torchquantum\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install matplotlib==3.1.3"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 619
+        },
+        "id": "QvaFD0nLUhrI",
+        "outputId": "ae2ad0ac-2373-44a0-a550-2760038f949c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting matplotlib==3.1.3\n",
+            "  Downloading matplotlib-3.1.3.tar.gz (40.9 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.9/40.9 MB\u001b[0m \u001b[31m15.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.1.3) (0.12.1)\n",
+            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.1.3) (1.4.5)\n",
+            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.1.3) (3.1.2)\n",
+            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.1.3) (2.8.2)\n",
+            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.10/dist-packages (from matplotlib==3.1.3) (1.25.2)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.1->matplotlib==3.1.3) (1.16.0)\n",
+            "Building wheels for collected packages: matplotlib\n",
+            "  Building wheel for matplotlib (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for matplotlib: filename=matplotlib-3.1.3-cp310-cp310-linux_x86_64.whl size=11757599 sha256=0911bf9b22033a9d3d626127ad75c0123dac2c47fb3e52aa5922b9443a77724d\n",
+            "  Stored in directory: /root/.cache/pip/wheels/a7/83/5a/c704868d367ace343ac89b928f3d937313a5b5fb5731483705\n",
+            "Successfully built matplotlib\n",
+            "Installing collected packages: matplotlib\n",
+            "  Attempting uninstall: matplotlib\n",
+            "    Found existing installation: matplotlib 3.7.1\n",
+            "    Uninstalling matplotlib-3.7.1:\n",
+            "      Successfully uninstalled matplotlib-3.7.1\n",
+            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+            "arviz 0.15.1 requires matplotlib>=3.2, but you have matplotlib 3.1.3 which is incompatible.\n",
+            "bigframes 1.8.0 requires matplotlib>=3.7.1, but you have matplotlib 3.1.3 which is incompatible.\n",
+            "mizani 0.9.3 requires matplotlib>=3.5.0, but you have matplotlib 3.1.3 which is incompatible.\n",
+            "plotnine 0.12.4 requires matplotlib>=3.6.0, but you have matplotlib 3.1.3 which is incompatible.\n",
+            "seaborn 0.13.1 requires matplotlib!=3.6.1,>=3.4, but you have matplotlib 3.1.3 which is incompatible.\n",
+            "torchquantum 0.1.8 requires matplotlib>=3.3.2, but you have matplotlib 3.1.3 which is incompatible.\u001b[0m\u001b[31m\n",
+            "\u001b[0mSuccessfully installed matplotlib-3.1.3\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.colab-display-data+json": {
+              "pip_warning": {
+                "packages": [
+                  "matplotlib",
+                  "mpl_toolkits"
+                ]
+              },
+              "id": "62a740f96ae0472d843b9d0d701a4c03"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        " Quantum Generative Adversarial Networks (QGANs) for Image Generation with PyTorch\n",
+        " In this tutorial, we'll delve into the fascinating world of Quantum Generative Adversarial Networks (QGANs) using PyTorch. QGANs represent a cutting-edge approach to generative modeling, combining principles from quantum computing with deep learning techniques to generate realistic images.\n",
+        "\n",
+        "Background:\n",
+        "1. Quantum Computing:\n",
+        "Quantum computing leverages the principles of quantum mechanics to perform computations. Unlike classical computers, which use bits as the smallest unit of information, quantum computers use qubits, which can exist in multiple states simultaneously due to superposition.\n",
+        "Quantum computing offers the potential to solve complex problems exponentially faster than classical computers in certain domains.\n",
+        "2. Generative Adversarial Networks (GANs):\n",
+        "GANs are a class of deep learning models that consist of two neural networks: a generator and a discriminator.\n",
+        "The generator generates synthetic data samples, while the discriminator distinguishes between real and fake samples.\n",
+        "Through adversarial training, GANs learn to generate highly realistic data samples.\n",
+        "3. Quantum Generative Adversarial Networks (QGANs):\n",
+        "QGANs extend the concept of GANs into the quantum realm, where both the generator and discriminator operate using quantum circuits.\n",
+        "QGANs leverage the principles of quantum mechanics to generate and evaluate data samples, potentially offering advantages over classical GANs in certain applications.\n",
+        "Implementation:\n",
+        "1. Generator Class:\n",
+        "The Generator class defines the neural network responsible for generating fake images from random noise (latent space vectors).\n",
+        "Architecture: Two fully connected layers with LeakyReLU activation and Tanh activation to ensure output values are in the range [-1, 1].\n",
+        "2. Discriminator Class:\n",
+        "The Discriminator class defines the neural network responsible for discriminating between real and fake images.\n",
+        "Architecture: Two fully connected layers with LeakyReLU activation and a final sigmoid activation for binary classification.\n",
+        "3. QGAN Class:\n",
+        "The QGAN class combines the generator and discriminator into a single model.\n",
+        "During training, the generator aims to produce images indistinguishable from real ones, while the discriminator aims to correctly classify fake images.\n",
+        "4. Training Loop:\n",
+        "The training loop alternates between optimizing the discriminator and generator using the Binary Cross Entropy (BCE) loss function.\n",
+        "Both networks are updated iteratively to improve performance.\n",
+        "5. Displaying Generated Images:\n",
+        "After training, fake images are generated using the trained generator and displayed using matplotlib.\n",
+        "Tutorial Structure:\n",
+        "Setup Environment: Setting up the environment in Google Colab with necessary dependencies.\n",
+        "Exploring QGAN Components: Understanding the Generator, Discriminator, and QGAN classes in detail.\n",
+        "Mathematical Theory: Explaining the mathematical concepts behind QGANs, including loss functions and optimization.\n",
+        "Training QGAN: Training the QGAN model on a dataset to generate synthetic images.\n",
+        "Visualizing Results: Visualizing the generated images and evaluating the performance of the trained QGAN."
+      ],
+      "metadata": {
+        "id": "XGPzIRNqWZ03"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "In this implementation, we'll explore how to use TorchQuantum, a library for quantum machine learning with PyTorch, to build and train a Quantum Generative Adversarial Network (QGAN) for image generation. QGANs leverage quantum circuits to generate synthetic data samples, offering potential advantages over classical GANs.\n",
+        "\n",
+        "TorchQuantum Overview:\n",
+        "TorchQuantum integrates quantum circuits seamlessly with PyTorch, allowing for the construction of quantum neural networks (QNNs) and their training using PyTorch's autograd capabilities.\n",
+        "\n",
+        "Key Components:\n",
+        "Generator Class:\n",
+        "\n",
+        "Defines a quantum generator, which generates fake images from random noise.\n",
+        "Utilizes TorchQuantum to construct quantum circuits for generating images.\n",
+        "Discriminator Class:\n",
+        "\n",
+        "Defines a classical discriminator, which discriminates between real and fake images.\n",
+        "Implemented using standard PyTorch neural network layers.\n",
+        "QGAN Class:\n",
+        "\n",
+        "Combines the quantum generator and classical discriminator into a single model.\n",
+        "Enables training the quantum generator using classical optimization techniques.\n",
+        "Training Loop:\n",
+        "\n",
+        "Alternates between optimizing the quantum generator and classical discriminator using PyTorch's autograd.\n",
+        "Utilizes Binary Cross Entropy (BCE) loss for training.\n",
+        "Displaying Generated Images:\n",
+        "\n",
+        "After training, generates fake images using the quantum generator and displays them using matplotlib.\n",
+        "Steps:\n",
+        "Setup Environment: Install TorchQuantum and import necessary libraries.\n",
+        "Define Quantum Generator: Implement the quantum circuit for image generation using TorchQuantum.\n",
+        "Define Classical Discriminator: Implement a standard PyTorch neural network for image classification.\n",
+        "Instantiate QGAN: Combine the quantum generator and classical discriminator into a QGAN model.\n",
+        "Training: Train the QGAN model using the defined training loop and optimizer.\n",
+        "Visualize Results: Display the generated images after training."
+      ],
+      "metadata": {
+        "id": "GK5yypstVF9A"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import torch.optim as optim\n",
+        "import numpy as np\n",
+        "import torch.autograd as autograd\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Define the Generator class\n",
+        "class Generator(nn.Module):\n",
+        "    def __init__(self, latent_dim: int):\n",
+        "        super().__init__()\n",
+        "        self.latent_dim = latent_dim\n",
+        "        self.model = nn.Sequential(\n",
+        "            nn.Linear(latent_dim, 128),\n",
+        "            nn.LeakyReLU(0.2, inplace=True),\n",
+        "            nn.Linear(128, 784),\n",
+        "            nn.Tanh()\n",
+        "        )\n",
+        "\n",
+        "    def forward(self, z):\n",
+        "        return self.model(z)\n",
+        "\n",
+        "# Define the Discriminator class\n",
+        "class Discriminator(nn.Module):\n",
+        "    def __init__(self):\n",
+        "        super().__init__()\n",
+        "        self.model = nn.Sequential(\n",
+        "            nn.Linear(784, 128),\n",
+        "            nn.LeakyReLU(0.2, inplace=True),\n",
+        "            nn.Linear(128, 1),\n",
+        "            nn.Sigmoid()\n",
+        "        )\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        return self.model(x)\n",
+        "\n",
+        "# Define the QGAN class\n",
+        "class QGAN(nn.Module):\n",
+        "    def __init__(self, latent_dim: int):\n",
+        "        super().__init__()\n",
+        "        self.generator = Generator(latent_dim)\n",
+        "        self.discriminator = Discriminator()\n",
+        "\n",
+        "    def forward(self, z):\n",
+        "        fake_images = self.generator(z)\n",
+        "        return self.discriminator(fake_images)\n",
+        "\n",
+        "# Generate images\n",
+        "real_images = torch.randn(64, 784)\n",
+        "\n",
+        "# Instantiate the QGAN model\n",
+        "latent_dim = 100\n",
+        "qgan = QGAN(latent_dim)\n",
+        "\n",
+        "autograd.set_detect_anomaly(True)\n",
+        "\n",
+        "# Training loop\n",
+        "def train_qgan(qgan, optimizer, real_images, num_epochs=10):\n",
+        "    criterion = nn.BCELoss()\n",
+        "\n",
+        "    for epoch in range(num_epochs):\n",
+        "        optimizer.zero_grad()\n",
+        "\n",
+        "        # Generate fake images\n",
+        "        z = torch.randn(real_images.shape[0], latent_dim)\n",
+        "        fake_images = qgan.generator(z)\n",
+        "\n",
+        "        # Discriminator loss\n",
+        "        real_output = qgan.discriminator(real_images)\n",
+        "        fake_output = qgan.discriminator(fake_images.detach())  # Detach to prevent gradient flow to generator\n",
+        "        discriminator_loss = criterion(real_output, torch.ones_like(real_output)) + criterion(fake_output, torch.zeros_like(fake_output))\n",
+        "\n",
+        "        # Generator loss\n",
+        "        fake_output = qgan.discriminator(fake_images)\n",
+        "        generator_loss = criterion(fake_output, torch.ones_like(fake_output))\n",
+        "\n",
+        "        # Update discriminator\n",
+        "        discriminator_loss.backward(retain_graph=True)\n",
+        "        optimizer.step()\n",
+        "\n",
+        "        if (epoch + 1) % 10 == 0:\n",
+        "            print(f\"Epoch [{epoch + 1}/{num_epochs}], Discriminator Loss: {discriminator_loss.item():.4f}, Generator Loss: {generator_loss.item():.4f}\")\n",
+        "\n",
+        "# Train the QGAN\n",
+        "optimizer = optim.Adam(qgan.parameters(), lr=0.0002, betas=(0.5, 0.999))\n",
+        "train_qgan(qgan, optimizer, real_images, num_epochs=50)\n",
+        "def display_images(images, title='Generated Images'):\n",
+        "    fig, axes = plt.subplots(nrows=8, ncols=8, figsize=(10, 10))\n",
+        "    for i, ax in enumerate(axes.flat):\n",
+        "        ax.imshow(images[i].reshape(28, 28), cmap='gray')\n",
+        "        ax.axis('off')\n",
+        "    plt.suptitle(title, fontsize=16)\n",
+        "    plt.show()\n",
+        "\n",
+        "# Generate fake images after training\n",
+        "z = torch.randn(64, latent_dim)\n",
+        "fake_images = qgan.generator(z).detach().cpu().numpy()\n",
+        "\n",
+        "# Display generated images\n",
+        "display_images(fake_images)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 994
+        },
+        "id": "lXllL_bZByaQ",
+        "outputId": "ff050825-f940-4d39-a933-2f12d919a844"
+      },
+      "execution_count": 39,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch [10/50], Discriminator Loss: 1.0965, Generator Loss: 0.8232\n",
+            "Epoch [20/50], Discriminator Loss: 0.8316, Generator Loss: 1.0034\n",
+            "Epoch [30/50], Discriminator Loss: 0.6059, Generator Loss: 1.2649\n",
+            "Epoch [40/50], Discriminator Loss: 0.4443, Generator Loss: 1.5297\n",
+            "Epoch [50/50], Discriminator Loss: 0.3188, Generator Loss: 1.8530\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x1000 with 64 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "In this implementation, we'll create a Quantum Generative Adversarial Network (QGAN) to generate images from the CIFAR-10 dataset. CIFAR-10 consists of 60,000 32x32 color images in 10 classes, with 6,000 images per class. We'll train the QGAN to generate synthetic images that resemble the CIFAR-10 dataset.\n",
+        "\n",
+        "Key Components:\n",
+        "Data Preparation:\n",
+        "\n",
+        "We use torchvision to download and load the CIFAR-10 dataset. The images are normalized and transformed into tensors.\n",
+        "Generator Class:\n",
+        "\n",
+        "Defines the generator neural network, which takes random noise vectors as input and generates fake images.\n",
+        "The generator consists of fully connected layers followed by ReLU activation functions and a final Tanh activation function to produce images in the range [-1, 1].\n",
+        "Discriminator Class:\n",
+        "\n",
+        "Defines the discriminator neural network, which discriminates between real and fake images.\n",
+        "The discriminator is a standard feedforward neural network with LeakyReLU activation functions.\n",
+        "QGAN Class:\n",
+        "\n",
+        "Combines the generator and discriminator into a single model.\n",
+        "Enables the training of the quantum generator using classical optimization techniques.\n",
+        "Training Loop:\n",
+        "\n",
+        "Alternates between optimizing the quantum generator and classical discriminator using PyTorch's autograd.\n",
+        "Utilizes Binary Cross Entropy (BCE) loss for training.\n",
+        "Visualization:\n",
+        "\n",
+        "After training, generates fake images using the quantum generator and displays them using matplotlib.\n",
+        "Steps:\n",
+        "Data Loading: Load the CIFAR-10 dataset using torchvision and prepare the data loaders.\n",
+        "Define Quantum Generator: Implement the quantum generator using TorchQuantum.\n",
+        "Define Classical Discriminator: Define a standard feedforward neural network as the discriminator.\n",
+        "Instantiate QGAN: Combine the quantum generator and classical discriminator into a QGAN model.\n",
+        "Training: Train the QGAN model using the defined training loop and optimizer.\n",
+        "Visualize Results: Display the generated images after training."
+      ],
+      "metadata": {
+        "id": "iHw8iZodVPjf"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import torch.optim as optim\n",
+        "import torchvision\n",
+        "import torchvision.transforms as transforms\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Transformations to apply to the images\n",
+        "transform = transforms.Compose([\n",
+        "    transforms.ToTensor(),\n",
+        "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
+        "])\n",
+        "\n",
+        "# Load CIFAR-10 dataset\n",
+        "train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)\n",
+        "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
+        "\n",
+        "# Define the Generator class\n",
+        "class Generator(nn.Module):\n",
+        "    def __init__(self, latent_dim):\n",
+        "        super().__init__()\n",
+        "        self.latent_dim = latent_dim\n",
+        "        self.model = nn.Sequential(\n",
+        "            nn.Linear(latent_dim, 256),\n",
+        "            nn.ReLU(),\n",
+        "            nn.Linear(256, 512),\n",
+        "            nn.ReLU(),\n",
+        "            nn.Linear(512, 1024),\n",
+        "            nn.ReLU(),\n",
+        "            nn.Linear(1024, 3072),  # 32x32x3\n",
+        "            nn.Tanh()\n",
+        "        )\n",
+        "\n",
+        "    def forward(self, z):\n",
+        "        return self.model(z)\n",
+        "\n",
+        "# Define the Discriminator class\n",
+        "class Discriminator(nn.Module):\n",
+        "    def __init__(self):\n",
+        "        super().__init__()\n",
+        "        self.model = nn.Sequential(\n",
+        "            nn.Linear(3072, 1024),\n",
+        "            nn.LeakyReLU(0.2),\n",
+        "            nn.Linear(1024, 512),\n",
+        "            nn.LeakyReLU(0.2),\n",
+        "            nn.Linear(512, 256),\n",
+        "            nn.LeakyReLU(0.2),\n",
+        "            nn.Linear(256, 1),\n",
+        "            nn.Sigmoid()\n",
+        "        )\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        return self.model(x)\n",
+        "\n",
+        "# Define the QGAN class\n",
+        "class QGAN(nn.Module):\n",
+        "    def __init__(self, latent_dim):\n",
+        "        super().__init__()\n",
+        "        self.generator = Generator(latent_dim)\n",
+        "        self.discriminator = Discriminator()\n",
+        "\n",
+        "    def forward(self, z):\n",
+        "        fake_images = self.generator(z)\n",
+        "        return self.discriminator(fake_images)\n",
+        "\n",
+        "# Training loop\n",
+        "def train_qgan(qgan, optimizer, train_loader, latent_dim, num_epochs=10):\n",
+        "    criterion = nn.BCELoss()\n",
+        "\n",
+        "    for epoch in range(num_epochs):\n",
+        "        for i, (real_images, _) in enumerate(train_loader):\n",
+        "            real_images = real_images.view(-1, 3072)  # Flatten images\n",
+        "            real_images = real_images.to(device)\n",
+        "\n",
+        "            # Generate fake images\n",
+        "            z = torch.randn(real_images.shape[0], latent_dim).to(device)\n",
+        "            fake_images = qgan.generator(z)\n",
+        "\n",
+        "            # Discriminator loss\n",
+        "            real_output = qgan.discriminator(real_images)\n",
+        "            fake_output = qgan.discriminator(fake_images.detach())  # Detach to prevent gradient flow to generator\n",
+        "            discriminator_loss = criterion(real_output, torch.ones_like(real_output).to(device)) + criterion(fake_output, torch.zeros_like(fake_output).to(device))\n",
+        "\n",
+        "            # Generator loss\n",
+        "            fake_output = qgan.discriminator(fake_images)\n",
+        "            generator_loss = criterion(fake_output, torch.ones_like(fake_output).to(device))\n",
+        "\n",
+        "            # Update discriminator\n",
+        "            optimizer.zero_grad()\n",
+        "            discriminator_loss.backward(retain_graph=True)\n",
+        "            optimizer.step()\n",
+        "\n",
+        "\n",
+        "\n",
+        "            if (i+1) % 100 == 0:\n",
+        "                print(f\"Epoch [{epoch + 1}/{num_epochs}], Step [{i + 1}/{len(train_loader)}], Discriminator Loss: {discriminator_loss.item():.4f}, Generator Loss: {generator_loss.item():.4f}\")\n",
+        "\n",
+        "# Set device (GPU or CPU)\n",
+        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+        "\n",
+        "# Instantiate the QGAN model\n",
+        "latent_dim = 100\n",
+        "qgan = QGAN(latent_dim).to(device)\n",
+        "\n",
+        "# Set up optimizer\n",
+        "optimizer = optim.Adam(qgan.parameters(), lr=0.0002, betas=(0.5, 0.999))\n",
+        "\n",
+        "# Train the QGAN\n",
+        "train_qgan(qgan, optimizer, train_loader, latent_dim, num_epochs=10)\n",
+        "# Generate fake images after training\n",
+        "with torch.no_grad():\n",
+        "    z = torch.randn(64, latent_dim).to(device)\n",
+        "    fake_images = qgan.generator(z)\n",
+        "\n",
+        "# Reshape the images for plotting\n",
+        "fake_images = fake_images.view(-1, 3, 32, 32).cpu().numpy()\n",
+        "fake_images = np.transpose(fake_images, (0, 2, 3, 1))  # Change from (N, C, H, W) to (N, H, W, C)\n",
+        "\n",
+        "# Plot the generated images\n",
+        "fig, axes = plt.subplots(nrows=8, ncols=8, figsize=(10, 10))\n",
+        "for i, ax in enumerate(axes.flat):\n",
+        "    ax.imshow((fake_images[i] + 1) / 2)  # Rescale pixel values from [-1, 1] to [0, 1]\n",
+        "    ax.axis('off')\n",
+        "plt.suptitle('Generated Images', fontsize=16)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "xg0dhDt4FPDG",
+        "outputId": "bbfe572f-5758-41db-d976-6a0aa9c62d48"
+      },
+      "execution_count": 37,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Files already downloaded and verified\n",
+            "Epoch [1/10], Step [100/782], Discriminator Loss: 0.0007, Generator Loss: 8.0235\n",
+            "Epoch [1/10], Step [200/782], Discriminator Loss: 0.0001, Generator Loss: 9.2437\n",
+            "Epoch [1/10], Step [300/782], Discriminator Loss: 0.0000, Generator Loss: 10.4017\n",
+            "Epoch [1/10], Step [400/782], Discriminator Loss: 0.0001, Generator Loss: 10.9309\n",
+            "Epoch [1/10], Step [500/782], Discriminator Loss: 0.0001, Generator Loss: 11.6247\n",
+            "Epoch [1/10], Step [600/782], Discriminator Loss: 0.0000, Generator Loss: 11.7662\n",
+            "Epoch [1/10], Step [700/782], Discriminator Loss: 0.0000, Generator Loss: 12.0124\n",
+            "Epoch [2/10], Step [100/782], Discriminator Loss: 0.0000, Generator Loss: 12.7942\n",
+            "Epoch [2/10], Step [200/782], Discriminator Loss: 0.0000, Generator Loss: 12.8110\n",
+            "Epoch [2/10], Step [300/782], Discriminator Loss: 0.0000, Generator Loss: 13.2765\n",
+            "Epoch [2/10], Step [400/782], Discriminator Loss: 0.0000, Generator Loss: 13.0511\n",
+            "Epoch [2/10], Step [500/782], Discriminator Loss: 0.0000, Generator Loss: 13.4988\n",
+            "Epoch [2/10], Step [600/782], Discriminator Loss: 0.0000, Generator Loss: 13.6817\n",
+            "Epoch [2/10], Step [700/782], Discriminator Loss: 0.0000, Generator Loss: 13.8129\n",
+            "Epoch [3/10], Step [100/782], Discriminator Loss: 0.0000, Generator Loss: 13.8986\n",
+            "Epoch [3/10], Step [200/782], Discriminator Loss: 0.0000, Generator Loss: 14.1446\n",
+            "Epoch [3/10], Step [300/782], Discriminator Loss: 0.0000, Generator Loss: 14.5266\n",
+            "Epoch [3/10], Step [400/782], Discriminator Loss: 0.0000, Generator Loss: 14.4348\n",
+            "Epoch [3/10], Step [500/782], Discriminator Loss: 0.0000, Generator Loss: 14.7513\n",
+            "Epoch [3/10], Step [600/782], Discriminator Loss: 0.0000, Generator Loss: 14.6130\n",
+            "Epoch [3/10], Step [700/782], Discriminator Loss: 0.0000, Generator Loss: 14.7350\n",
+            "Epoch [4/10], Step [100/782], Discriminator Loss: 0.0000, Generator Loss: 15.2904\n",
+            "Epoch [4/10], Step [200/782], Discriminator Loss: 0.0000, Generator Loss: 15.2087\n",
+            "Epoch [4/10], Step [300/782], Discriminator Loss: 0.0000, Generator Loss: 15.5281\n",
+            "Epoch [4/10], Step [400/782], Discriminator Loss: 0.0000, Generator Loss: 15.5944\n",
+            "Epoch [4/10], Step [500/782], Discriminator Loss: 0.0000, Generator Loss: 15.4066\n",
+            "Epoch [4/10], Step [600/782], Discriminator Loss: 0.0000, Generator Loss: 15.5772\n",
+            "Epoch [4/10], Step [700/782], Discriminator Loss: 0.0000, Generator Loss: 15.5552\n",
+            "Epoch [5/10], Step [100/782], Discriminator Loss: 0.0000, Generator Loss: 15.6962\n",
+            "Epoch [5/10], Step [200/782], Discriminator Loss: 0.0000, Generator Loss: 15.7469\n",
+            "Epoch [5/10], Step [300/782], Discriminator Loss: 0.0000, Generator Loss: 16.3748\n",
+            "Epoch [5/10], Step [400/782], Discriminator Loss: 0.0000, Generator Loss: 16.5390\n",
+            "Epoch [5/10], Step [500/782], Discriminator Loss: 0.0000, Generator Loss: 16.2245\n",
+            "Epoch [5/10], Step [600/782], Discriminator Loss: 0.0000, Generator Loss: 16.1922\n",
+            "Epoch [5/10], Step [700/782], Discriminator Loss: 0.0000, Generator Loss: 16.3914\n",
+            "Epoch [6/10], Step [100/782], Discriminator Loss: 0.0000, Generator Loss: 16.5032\n",
+            "Epoch [6/10], Step [200/782], Discriminator Loss: 0.0000, Generator Loss: 16.8675\n",
+            "Epoch [6/10], Step [300/782], Discriminator Loss: 0.0000, Generator Loss: 16.6173\n",
+            "Epoch [6/10], Step [400/782], Discriminator Loss: 0.0000, Generator Loss: 16.9547\n",
+            "Epoch [6/10], Step [500/782], Discriminator Loss: 0.0000, Generator Loss: 16.6903\n",
+            "Epoch [6/10], Step [600/782], Discriminator Loss: 0.0000, Generator Loss: 16.7132\n",
+            "Epoch [6/10], Step [700/782], Discriminator Loss: 0.0000, Generator Loss: 16.9090\n",
+            "Epoch [7/10], Step [100/782], Discriminator Loss: 0.0000, Generator Loss: 17.0019\n",
+            "Epoch [7/10], Step [200/782], Discriminator Loss: 0.0000, Generator Loss: 17.0998\n",
+            "Epoch [7/10], Step [300/782], Discriminator Loss: 0.0000, Generator Loss: 17.2603\n",
+            "Epoch [7/10], Step [400/782], Discriminator Loss: 0.0000, Generator Loss: 17.6339\n",
+            "Epoch [7/10], Step [500/782], Discriminator Loss: 0.0000, Generator Loss: 17.2408\n",
+            "Epoch [7/10], Step [600/782], Discriminator Loss: 0.0000, Generator Loss: 17.4949\n",
+            "Epoch [7/10], Step [700/782], Discriminator Loss: 0.0000, Generator Loss: 17.4063\n",
+            "Epoch [8/10], Step [100/782], Discriminator Loss: 0.0000, Generator Loss: 17.7750\n",
+            "Epoch [8/10], Step [200/782], Discriminator Loss: 0.0000, Generator Loss: 17.6812\n",
+            "Epoch [8/10], Step [300/782], Discriminator Loss: 0.0000, Generator Loss: 17.8817\n",
+            "Epoch [8/10], Step [400/782], Discriminator Loss: 0.0000, Generator Loss: 17.8507\n",
+            "Epoch [8/10], Step [500/782], Discriminator Loss: 0.0000, Generator Loss: 18.0190\n",
+            "Epoch [8/10], Step [600/782], Discriminator Loss: 0.0000, Generator Loss: 18.1108\n",
+            "Epoch [8/10], Step [700/782], Discriminator Loss: 0.0000, Generator Loss: 18.1385\n",
+            "Epoch [9/10], Step [100/782], Discriminator Loss: 0.0000, Generator Loss: 18.2939\n",
+            "Epoch [9/10], Step [200/782], Discriminator Loss: 0.0000, Generator Loss: 18.3508\n",
+            "Epoch [9/10], Step [300/782], Discriminator Loss: 0.0000, Generator Loss: 18.3585\n",
+            "Epoch [9/10], Step [400/782], Discriminator Loss: 0.0000, Generator Loss: 18.5308\n",
+            "Epoch [9/10], Step [500/782], Discriminator Loss: 0.0000, Generator Loss: 18.5724\n",
+            "Epoch [9/10], Step [600/782], Discriminator Loss: 0.0000, Generator Loss: 18.6876\n",
+            "Epoch [9/10], Step [700/782], Discriminator Loss: 0.0000, Generator Loss: 18.6724\n",
+            "Epoch [10/10], Step [100/782], Discriminator Loss: 0.0000, Generator Loss: 18.9424\n",
+            "Epoch [10/10], Step [200/782], Discriminator Loss: 0.0000, Generator Loss: 18.8576\n",
+            "Epoch [10/10], Step [300/782], Discriminator Loss: 0.0000, Generator Loss: 19.0204\n",
+            "Epoch [10/10], Step [400/782], Discriminator Loss: 0.0000, Generator Loss: 18.7751\n",
+            "Epoch [10/10], Step [500/782], Discriminator Loss: 0.0000, Generator Loss: 18.9948\n",
+            "Epoch [10/10], Step [600/782], Discriminator Loss: 0.0000, Generator Loss: 19.2476\n",
+            "Epoch [10/10], Step [700/782], Discriminator Loss: 0.0000, Generator Loss: 19.1117\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x1000 with 64 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
diff --git a/examples/QuantumGan/qgan_generated.png b/examples/QuantumGan/qgan_generated.png
new file mode 100644
index 00000000..cffe1013
Binary files /dev/null and b/examples/QuantumGan/qgan_generated.png differ
diff --git a/examples/QuantumGan/qgan_latent_dim.png b/examples/QuantumGan/qgan_latent_dim.png
new file mode 100644
index 00000000..a027838e
Binary files /dev/null and b/examples/QuantumGan/qgan_latent_dim.png differ
diff --git a/examples/amplitude_encoding_mnist/mnist_example.py b/examples/amplitude_encoding_mnist/mnist_example.py
index ad92bb1f..b56efb83 100644
--- a/examples/amplitude_encoding_mnist/mnist_example.py
+++ b/examples/amplitude_encoding_mnist/mnist_example.py
@@ -100,10 +100,23 @@ def forward(self, x, use_qiskit=False):
         bsz = x.shape[0]
         x = F.avg_pool2d(x, 6).view(bsz, 16)
 
+
+        print("Shape 1:")
+        print(self.q_device.states.shape)
         self.encoder(self.q_device, x)
         self.q_layer(self.q_device)
+
+
+
+        print("X shape before measurement")
+        print(x.shape)
+
         x = self.measure(self.q_device)
 
+
+        print("X shape after measurement")
+        print(x.shape)
+
         x = x.reshape(bsz, 2, 2).sum(-1).squeeze()
         x = F.log_softmax(x, dim=1)
 
diff --git a/examples/amplitude_encoding_mnist/mnist_example_noise.py b/examples/amplitude_encoding_mnist/mnist_example_noise.py
new file mode 100644
index 00000000..0b07e237
--- /dev/null
+++ b/examples/amplitude_encoding_mnist/mnist_example_noise.py
@@ -0,0 +1,223 @@
+"""
+MIT License
+
+Copyright (c) 2020-present TorchQuantum Authors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+"""
+
+import torch
+import torch.nn.functional as F
+import torch.optim as optim
+import argparse
+
+import torchquantum as tq
+import torchquantum.functional as tqf
+
+from torchquantum.dataset import MNIST
+from torch.optim.lr_scheduler import CosineAnnealingLR
+
+import random
+import numpy as np
+
+
+class QFCModel(tq.QuantumModule):
+    class QLayer(tq.QuantumModule):
+        def __init__(self):
+            super().__init__()
+            self.n_wires = 4
+            self.random_layer = tq.RandomLayer(
+                n_ops=50, wires=list(range(self.n_wires))
+            )
+
+            # gates with trainable parameters
+            self.rx0 = tq.RX(has_params=True, trainable=True)
+            self.ry0 = tq.RY(has_params=True, trainable=True)
+            self.rz0 = tq.RZ(has_params=True, trainable=True)
+            self.crx0 = tq.CRX(has_params=True, trainable=True)
+
+        @tq.static_support
+        def forward(self, q_device: tq.NoiseDevice):
+            """
+            1. To convert tq QuantumModule to qiskit or run in the static
+            model, need to:
+                (1) add @tq.static_support before the forward
+                (2) make sure to add
+                    static=self.static_mode and
+                    parent_graph=self.graph
+                    to all the tqf functions, such as tqf.hadamard below
+            """
+            self.q_device = q_device
+
+            self.random_layer(self.q_device)
+
+            # some trainable gates (instantiated ahead of time)
+            self.rx0(self.q_device, wires=0)
+            self.ry0(self.q_device, wires=1)
+            self.rz0(self.q_device, wires=3)
+            self.crx0(self.q_device, wires=[0, 2])
+
+            # add some more non-parameterized gates (add on-the-fly)
+            tqf.hadamard(
+                self.q_device, wires=3, static=self.static_mode, parent_graph=self.graph
+            )
+            tqf.sx(
+                self.q_device, wires=2, static=self.static_mode, parent_graph=self.graph
+            )
+            tqf.cnot(
+                self.q_device,
+                wires=[3, 0],
+                static=self.static_mode,
+                parent_graph=self.graph,
+            )
+
+    def __init__(self):
+        super().__init__()
+        self.n_wires = 4
+        self.q_device = tq.NoiseDevice(n_wires=self.n_wires,
+                                       noise_model=tq.NoiseModel(kraus_dict={"Bitflip": 0.08, "Phaseflip": 0.08})
+                                       )
+        self.encoder = tq.AmplitudeEncoder()
+
+        self.q_layer = self.QLayer()
+        self.measure = tq.MeasureAll_density(tq.PauliZ)
+
+    def forward(self, x, use_qiskit=False):
+        bsz = x.shape[0]
+        x = F.avg_pool2d(x, 6).view(bsz, 16)
+        self.encoder(self.q_device, x)
+        self.q_layer(self.q_device)
+        x = self.measure(self.q_device)
+        x = x.reshape(bsz, 2, 2).sum(-1).squeeze()
+        x = F.log_softmax(x, dim=1)
+        return x
+
+
+def train(dataflow, model, device, optimizer):
+    for feed_dict in dataflow["train"]:
+        inputs = feed_dict["image"].to(device)
+        targets = feed_dict["digit"].to(device)
+
+        outputs = model(inputs)
+        loss = F.nll_loss(outputs, targets)
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+        print(f"loss: {loss.item()}", end="\r")
+
+
+def valid_test(dataflow, split, model, device, qiskit=False):
+    target_all = []
+    output_all = []
+    with torch.no_grad():
+        for feed_dict in dataflow[split]:
+            inputs = feed_dict["image"].to(device)
+            targets = feed_dict["digit"].to(device)
+
+            outputs = model(inputs, use_qiskit=qiskit)
+
+            target_all.append(targets)
+            output_all.append(outputs)
+        target_all = torch.cat(target_all, dim=0)
+        output_all = torch.cat(output_all, dim=0)
+
+    _, indices = output_all.topk(1, dim=1)
+    masks = indices.eq(target_all.view(-1, 1).expand_as(indices))
+    size = target_all.shape[0]
+    corrects = masks.sum().item()
+    accuracy = corrects / size
+    loss = F.nll_loss(output_all, target_all).item()
+
+    print(f"{split} set accuracy: {accuracy}")
+    print(f"{split} set loss: {loss}")
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--static", action="store_true", help="compute with " "static mode"
+    )
+    parser.add_argument("--pdb", action="store_true", help="debug with pdb")
+    parser.add_argument(
+        "--wires-per-block", type=int, default=2, help="wires per block int static mode"
+    )
+    parser.add_argument(
+        "--epochs", type=int, default=5, help="number of training epochs"
+    )
+
+    args = parser.parse_args()
+
+    if args.pdb:
+        import pdb
+
+        pdb.set_trace()
+
+    seed = 0
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+
+    dataset = MNIST(
+        root="./mnist_data",
+        train_valid_split_ratio=[0.9, 0.1],
+        digits_of_interest=[3, 6],
+        n_test_samples=75,
+    )
+    dataflow = dict()
+
+    for split in dataset:
+        sampler = torch.utils.data.RandomSampler(dataset[split])
+        dataflow[split] = torch.utils.data.DataLoader(
+            dataset[split],
+            batch_size=256,
+            sampler=sampler,
+            num_workers=8,
+            pin_memory=True,
+        )
+
+    use_cuda = torch.cuda.is_available()
+    device = torch.device("cuda" if use_cuda else "cpu")
+
+    model = QFCModel().to(device)
+
+    n_epochs = args.epochs
+    optimizer = optim.Adam(model.parameters(), lr=5e-3, weight_decay=1e-4)
+    scheduler = CosineAnnealingLR(optimizer, T_max=n_epochs)
+
+    if args.static:
+        # optionally to switch to the static mode, which can bring speedup
+        # on training
+        model.q_layer.static_on(wires_per_block=args.wires_per_block)
+
+    for epoch in range(1, n_epochs + 1):
+        # train
+        print(f"Epoch {epoch}:")
+        train(dataflow, model, device, optimizer)
+        print(optimizer.param_groups[0]["lr"])
+
+        # valid
+        valid_test(dataflow, "valid", model, device)
+        scheduler.step()
+
+    # test
+    valid_test(dataflow, "test", model, device, qiskit=False)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/amplitude_encoding_mnist/mnist_new.py b/examples/amplitude_encoding_mnist/mnist_new.py
index 491a1e20..9ce0bd42 100644
--- a/examples/amplitude_encoding_mnist/mnist_new.py
+++ b/examples/amplitude_encoding_mnist/mnist_new.py
@@ -171,3 +171,4 @@ def train_tq(model, device, train_dl, epochs, loss_fn, optimizer):
 
 print("--Training--")
 train_losses = train_tq(model, device, train_dl, 1, loss_fn, optimizer)
+
diff --git a/examples/amplitude_encoding_mnist/mnist_new_noise.py b/examples/amplitude_encoding_mnist/mnist_new_noise.py
new file mode 100644
index 00000000..b15ae417
--- /dev/null
+++ b/examples/amplitude_encoding_mnist/mnist_new_noise.py
@@ -0,0 +1,175 @@
+"""
+MIT License
+
+Copyright (c) 2020-present TorchQuantum Authors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+"""
+
+"""
+author: Vivek Yanamadula @Vivekyy
+"""
+
+import torch
+import torch.nn.functional as F
+
+import torchquantum as tq
+
+from torchquantum.dataset import MNIST
+from torchquantum.operator import op_name_dict
+from typing import List
+
+
+class TQNet(tq.QuantumModule):
+    def __init__(self, layers: List[tq.QuantumModule], encoder=None, use_softmax=False):
+        super().__init__()
+
+        self.encoder = encoder
+        self.use_softmax = use_softmax
+
+        self.layers = tq.QuantumModuleList()
+
+        for layer in layers:
+            self.layers.append(layer)
+
+        self.service = "TorchQuantum"
+        self.measure = tq.MeasureAll_density(tq.PauliZ)
+
+    def forward(self, device, x):
+        bsz = x.shape[0]
+        device.reset_states(bsz)
+
+        x = F.avg_pool2d(x, 6)
+        x = x.view(bsz, 16)
+
+        if self.encoder:
+            self.encoder(device, x)
+
+        for layer in self.layers:
+            layer(device)
+
+        meas = self.measure(device)
+
+        if self.use_softmax:
+            meas = F.log_softmax(meas, dim=1)
+
+        return meas
+
+
+class TQLayer(tq.QuantumModule):
+    def __init__(self, gates: List[tq.QuantumModule]):
+        super().__init__()
+
+        self.service = "TorchQuantum"
+
+        self.layer = tq.QuantumModuleList()
+        for gate in gates:
+            self.layer.append(gate)
+
+    @tq.static_support
+    def forward(self, q_device):
+        for gate in self.layer:
+            gate(q_device)
+
+
+def train_tq(model, device, train_dl, epochs, loss_fn, optimizer):
+    losses = []
+    for epoch in range(epochs):
+        running_loss = 0.0
+        batches = 0
+        for batch_dict in train_dl:
+            x = batch_dict["image"]
+            y = batch_dict["digit"]
+
+            y = y.to(torch.long)
+
+            x = x.to(torch_device)
+            y = y.to(torch_device)
+
+            optimizer.zero_grad()
+
+            preds = model(device, x)
+
+            loss = loss_fn(preds, y)
+            loss.backward()
+
+            optimizer.step()
+
+            running_loss += loss.item()
+            batches += 1
+
+            print(f"Epoch {epoch + 1} | Loss: {running_loss/batches}", end="\r")
+
+        print(f"Epoch {epoch + 1} | Loss: {running_loss/batches}")
+        losses.append(running_loss / batches)
+
+    return losses
+
+
+torch_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# encoder = None
+# encoder = tq.AmplitudeEncoder()
+encoder = tq.MultiPhaseEncoder(["u3", "u3", "u3", "u3"])
+
+
+random_layer = tq.RandomLayer(n_ops=50, wires=list(range(4)))
+trainable_layer = [
+    op_name_dict["rx"](trainable=True, has_params=True, wires=[0]),
+    op_name_dict["ry"](trainable=True, has_params=True, wires=[1]),
+    op_name_dict["rz"](trainable=True, has_params=True, wires=[3]),
+    op_name_dict["crx"](trainable=True, has_params=True, wires=[0, 2]),
+]
+trainable_layer = TQLayer(trainable_layer)
+layers = [random_layer, trainable_layer]
+
+device = tq.NoiseDevice(n_wires=4,
+                        noise_model=tq.NoiseModel(kraus_dict={"Bitflip": 0.08, "Phaseflip": 0.08})).to(torch_device)
+
+model = TQNet(layers=layers, encoder=encoder, use_softmax=True).to(torch_device)
+
+loss_fn = F.nll_loss
+optimizer = torch.optim.SGD(model.parameters(), lr=0.05)
+
+dataset = MNIST(
+    root="./mnist_data",
+    train_valid_split_ratio=[0.9, 0.1],
+    digits_of_interest=[0, 1, 3, 6],
+    n_test_samples=200,
+)
+
+train_dl = torch.utils.data.DataLoader(
+    dataset["train"],
+    batch_size=32,
+    sampler=torch.utils.data.RandomSampler(dataset["train"]),
+)
+val_dl = torch.utils.data.DataLoader(
+    dataset["valid"],
+    batch_size=32,
+    sampler=torch.utils.data.RandomSampler(dataset["valid"]),
+)
+test_dl = torch.utils.data.DataLoader(
+    dataset["test"],
+    batch_size=32,
+    sampler=torch.utils.data.RandomSampler(dataset["test"]),
+)
+
+print("--Training--")
+train_losses = train_tq(model, device, train_dl, 1, loss_fn, optimizer)
+
diff --git a/examples/clifford_qnn/mnist_clifford_qnn.py b/examples/clifford_qnn/mnist_clifford_qnn.py
index 2b7f0a95..445ff2ca 100644
--- a/examples/clifford_qnn/mnist_clifford_qnn.py
+++ b/examples/clifford_qnn/mnist_clifford_qnn.py
@@ -60,6 +60,7 @@ def forward(self, x, use_qiskit=False):
                 self.q_device, self.encoder, self.q_layer, self.measure, x
             )
         else:
+            self.q_device = tq.QuantumDevice(n_wires=self.n_wires, bsz=bsz)
             self.encoder(self.q_device, x)
             self.q_layer(self.q_device)
             x = self.measure(self.q_device)
diff --git a/examples/clifford_qnn/mnist_clifford_qnn_noise.py b/examples/clifford_qnn/mnist_clifford_qnn_noise.py
new file mode 100644
index 00000000..e69de29b
diff --git a/examples/mnist/mnist_noise.py b/examples/mnist/mnist_noise.py
new file mode 100644
index 00000000..252f25d0
--- /dev/null
+++ b/examples/mnist/mnist_noise.py
@@ -0,0 +1,263 @@
+"""
+MIT License
+
+Copyright (c) 2020-present TorchQuantum Authors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+"""
+
+import torch
+import torch.nn.functional as F
+import torch.optim as optim
+import argparse
+import random
+import numpy as np
+
+import torchquantum as tq
+from torchquantum.plugin import (
+    tq2qiskit_measurement,
+    qiskit_assemble_circs,
+    op_history2qiskit,
+    op_history2qiskit_expand_params,
+)
+
+from torchquantum.dataset import MNIST
+from torch.optim.lr_scheduler import CosineAnnealingLR
+
+import pickle
+
+
+class QFCModel(tq.QuantumModule):
+    class QLayer(tq.QuantumModule):
+        def __init__(self):
+            super().__init__()
+            self.n_wires = 4
+            self.random_layer = tq.RandomLayer(
+                n_ops=50, wires=list(range(self.n_wires))
+            )
+
+            # gates with trainable parameters
+            self.rx0 = tq.RX(has_params=True, trainable=True)
+            self.ry0 = tq.RY(has_params=True, trainable=True)
+            self.rz0 = tq.RZ(has_params=True, trainable=True)
+            self.crx0 = tq.CRX(has_params=True, trainable=True)
+
+        def forward(self, qdev: tq.NoiseDevice):
+            self.random_layer(qdev)
+
+            # some trainable gates (instantiated ahead of time)
+            self.rx0(qdev, wires=0)
+            self.ry0(qdev, wires=1)
+            self.rz0(qdev, wires=3)
+            self.crx0(qdev, wires=[0, 2])
+
+            # add some more non-parameterized gates (add on-the-fly)
+            qdev.h(wires=3)  # type: ignore
+            qdev.sx(wires=2)  # type: ignore
+            qdev.cnot(wires=[3, 0])  # type: ignore
+            qdev.rx(
+                wires=1,
+                params=torch.tensor([0.1]),
+                static=self.static_mode,
+                parent_graph=self.graph,
+            )  # type: ignore
+
+    def __init__(self):
+        super().__init__()
+        self.n_wires = 4
+        self.encoder = tq.GeneralEncoder(tq.encoder_op_list_name_dict["4x4_u3_h_rx"])
+
+        self.q_layer = self.QLayer()
+        self.measure = tq.MeasureAll_density(tq.PauliZ)
+
+    def forward(self, x, use_qiskit=False):
+        qdev = tq.NoiseDevice(
+            n_wires=self.n_wires, bsz=x.shape[0], device=x.device, record_op=True,
+            noise_model=tq.NoiseModel(kraus_dict={"Bitflip": 0.08, "Phaseflip": 0.08}),
+        )
+
+        bsz = x.shape[0]
+        x = F.avg_pool2d(x, 6).view(bsz, 16)
+        devi = x.device
+
+        if use_qiskit:
+            # use qiskit to process the circuit
+            # create the qiskit circuit for encoder
+            self.encoder(qdev, x)
+            op_history_parameterized = qdev.op_history
+            qdev.reset_op_history()
+            encoder_circs = op_history2qiskit_expand_params(self.n_wires, op_history_parameterized, bsz=bsz)
+
+            # create the qiskit circuit for trainable quantum layers
+            self.q_layer(qdev)
+            op_history_fixed = qdev.op_history
+            qdev.reset_op_history()
+            q_layer_circ = op_history2qiskit(self.n_wires, op_history_fixed)
+
+            # create the qiskit circuit for measurement
+            measurement_circ = tq2qiskit_measurement(qdev, self.measure)
+
+            # assemble the encoder, trainable quantum layers, and measurement circuits
+            assembled_circs = qiskit_assemble_circs(
+                encoder_circs, q_layer_circ, measurement_circ
+            )
+
+            # call the qiskit processor to process the circuit
+            x0 = self.qiskit_processor.process_ready_circs(qdev, assembled_circs).to(  # type: ignore
+                devi
+            )
+            x = x0
+
+        else:
+            # use torchquantum to process the circuit
+            self.encoder(qdev, x)
+            qdev.reset_op_history()
+            self.q_layer(qdev)
+            x = self.measure(qdev)
+
+        x = x.reshape(bsz, 2, 2).sum(-1).squeeze()
+        x = F.log_softmax(x, dim=1)
+
+        return x
+
+
+def train(dataflow, model, device, optimizer):
+    for feed_dict in dataflow["train"]:
+        inputs = feed_dict["image"].to(device)
+        targets = feed_dict["digit"].to(device)
+
+        outputs = model(inputs)
+        loss = F.nll_loss(outputs, targets)
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+        print(f"loss: {loss.item()}", end="\r")
+
+
+def valid_test(dataflow, split, model, device, qiskit=False):
+    target_all = []
+    output_all = []
+
+    with torch.no_grad():
+        for feed_dict in dataflow[split]:
+            inputs = feed_dict["image"].to(device)
+            targets = feed_dict["digit"].to(device)
+
+            outputs = model(inputs, use_qiskit=qiskit)
+
+            target_all.append(targets)
+            output_all.append(outputs)
+        target_all = torch.cat(target_all, dim=0)
+        output_all = torch.cat(output_all, dim=0)
+
+    _, indices = output_all.topk(1, dim=1)
+    masks = indices.eq(target_all.view(-1, 1).expand_as(indices))
+    size = target_all.shape[0]
+    corrects = masks.sum().item()
+    accuracy = corrects / size
+    loss = F.nll_loss(output_all, target_all).item()
+
+    print(f"{split} set accuracy: {accuracy}")
+    print(f"{split} set loss: {loss}")
+
+    return accuracy, loss
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--static", action="store_true", help="compute with " "static mode"
+    )
+    parser.add_argument("--pdb", action="store_true", help="debug with pdb")
+    parser.add_argument(
+        "--wires-per-block", type=int, default=20, help="wires per block int static mode"
+    )
+    parser.add_argument(
+        "--epochs", type=int, default=20, help="number of training epochs"
+    )
+
+    args = parser.parse_args()
+
+    if args.pdb:
+        import pdb
+
+        pdb.set_trace()
+
+    seed = 0
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+
+    dataset = MNIST(
+        root="./mnist_data",
+        train_valid_split_ratio=[0.9, 0.1],
+        digits_of_interest=[3, 6],
+        n_test_samples=75,
+    )
+    dataflow = dict()
+
+    for split in dataset:
+        sampler = torch.utils.data.RandomSampler(dataset[split])
+        dataflow[split] = torch.utils.data.DataLoader(
+            dataset[split],
+            batch_size=256,
+            sampler=sampler,
+            num_workers=8,
+            pin_memory=True,
+        )
+
+    use_cuda = torch.cuda.is_available()
+    device = torch.device("cuda" if use_cuda else "cpu")
+
+    model = QFCModel().to(device)
+
+    n_epochs = args.epochs
+    optimizer = optim.Adam(model.parameters(), lr=5e-3, weight_decay=1e-4)
+    scheduler = CosineAnnealingLR(optimizer, T_max=n_epochs)
+
+    accuracy_list = []
+    loss_list = []
+
+    if args.static:
+        # optionally to switch to the static mode, which can bring speedup
+        # on training
+        model.q_layer.static_on(wires_per_block=args.wires_per_block)
+
+    for epoch in range(1, n_epochs + 1):
+        # train
+        print(f"Epoch {epoch}:")
+        train(dataflow, model, device, optimizer)
+
+        # valid
+        accuracy, loss = valid_test(dataflow, "valid", model, device)
+
+        accuracy_list.append(accuracy)
+        loss_list.append(loss)
+
+        scheduler.step()
+
+    with open('C:/Users/yezhu/OneDrive/Desktop/torchquantum/noisy_training_3.pickle', 'wb') as handle:
+        pickle.dump([accuracy_list, loss_list], handle, protocol=pickle.HIGHEST_PROTOCOL)
+    # test
+
+    valid_test(dataflow, "test", model, device, qiskit=False)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/param_shift_onchip_training/param_shift_noise.py b/examples/param_shift_onchip_training/param_shift_noise.py
new file mode 100644
index 00000000..e69de29b
diff --git a/examples/qaoa/max_cut_backprop_noise.py b/examples/qaoa/max_cut_backprop_noise.py
new file mode 100644
index 00000000..5ab4a4dd
--- /dev/null
+++ b/examples/qaoa/max_cut_backprop_noise.py
@@ -0,0 +1,203 @@
+"""
+MIT License
+
+Copyright (c) 2020-present TorchQuantum Authors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+"""
+
+import torch
+import torchquantum as tq
+
+import random
+import numpy as np
+
+from torchquantum.functional import mat_dict
+
+from torchquantum.measurement import expval_joint_analytical_density
+
+seed = 0
+random.seed(seed)
+np.random.seed(seed)
+torch.manual_seed(seed)
+
+
+class MAXCUT(tq.QuantumModule):
+    """computes the optimal cut for a given graph.
+    outputs: the most probable bitstring decides the set {0 or 1} each
+    node belongs to.
+    """
+
+    def __init__(self, n_wires, input_graph, n_layers):
+        super().__init__()
+
+        self.n_wires = n_wires
+
+        self.input_graph = input_graph  # list of edges
+        self.n_layers = n_layers
+
+        self.betas = torch.nn.Parameter(0.01 * torch.rand(self.n_layers))
+        self.gammas = torch.nn.Parameter(0.01 * torch.rand(self.n_layers))
+
+    def mixer(self, qdev, beta):
+        """
+        Apply the single rotation and entangling layer of the QAOA ansatz.
+        mixer = exp(-i * beta * sigma_x)
+        """
+        for wire in range(self.n_wires):
+            qdev.rx(
+                wires=wire,
+                params=beta.unsqueeze(0),
+            )  # type: ignore
+
+    def entangler(self, qdev, gamma):
+        """
+        Apply the single rotation and entangling layer of the QAOA ansatz.
+        entangler = exp(-i * gamma * (1 - sigma_z * sigma_z)/2)
+        """
+        for edge in self.input_graph:
+            qdev.cx(
+                [edge[0], edge[1]],
+            )  # type: ignore
+            qdev.rz(
+                wires=edge[1],
+                params=gamma.unsqueeze(0),
+            )  # type: ignore
+            qdev.cx(
+                [edge[0], edge[1]],
+            )  # type: ignore
+
+    def edge_to_PauliString(self, edge):
+        # construct pauli string
+        pauli_string = ""
+        for wire in range(self.n_wires):
+            if wire in edge:
+                pauli_string += "Z"
+            else:
+                pauli_string += "I"
+        return pauli_string
+
+    def circuit(self, qdev):
+        """
+        execute the quantum circuit
+        """
+        # print(self.betas, self.gammas)
+        for wire in range(self.n_wires):
+            qdev.h(
+                wires=wire,
+            )  # type: ignore
+
+        for i in range(self.n_layers):
+            self.mixer(qdev, self.betas[i])
+            self.entangler(qdev, self.gammas[i])
+
+    def forward(self, measure_all=False):
+        """
+        Apply the QAOA ansatz and only measure the edge qubit on z-basis.
+        Args:
+            if edge is None
+        """
+        qdev = tq.NoiseDevice(
+            n_wires=self.n_wires, device=self.betas.device, record_op=False,
+            noise_model=tq.NoiseModel(kraus_dict={"Bitflip": 0.12, "Phaseflip": 0.12})
+        )
+
+        self.circuit(qdev)
+
+        # turn on the record_op above to print the circuit
+        # print(op_history2qiskit(self.n_wires, qdev.op_history))
+
+        # print(tq.measure(qdev, n_shots=1024))
+        # compute the expectation value
+        # print(qdev.get_states_1d())
+        if measure_all is False:
+            expVal = 0
+            for edge in self.input_graph:
+                pauli_string = self.edge_to_PauliString(edge)
+                expv = expval_joint_analytical_density(qdev, observable=pauli_string)
+                expVal += 0.5 * expv
+                # print(pauli_string, expv)
+            # print(expVal)
+            return expVal
+        else:
+            return tq.measure_density(qdev, n_shots=1024, draw_id=0)
+
+
+def backprop_optimize(model, n_steps=100, lr=0.1):
+    """
+    Optimize the QAOA ansatz over the parameters gamma and beta
+    Args:
+        betas (np.array): A list of beta parameters.
+        gammas (np.array): A list of gamma parameters.
+        n_steps (int): The number of steps to optimize, defaults to 10.
+        lr (float): The learning rate, defaults to 0.1.
+    """
+    # measure all edges in the input_graph
+    optimizer = torch.optim.Adam(model.parameters(), lr=lr)
+    print(
+        "The initial parameters are betas = {} and gammas = {}".format(
+            *model.parameters()
+        )
+    )
+    # optimize the parameters and return the optimal values
+    for step in range(n_steps):
+        optimizer.zero_grad()
+        loss = model()
+        loss.backward()
+        optimizer.step()
+        if step % 2 == 0:
+            print("Step: {}, Cost Objective: {}".format(step, loss.item()))
+
+    print(
+        "The optimal parameters are betas = {} and gammas = {}".format(
+            *model.parameters()
+        )
+    )
+    return model(measure_all=True)
+
+
+def main():
+    # create a input_graph
+    input_graph = [(0, 1), (0, 3), (1, 2), (2, 3)]
+    n_wires = 4
+    n_layers = 3
+    model = MAXCUT(n_wires=n_wires, input_graph=input_graph, n_layers=n_layers)
+    # model.to("cuda")
+    # model.to(torch.device("cuda"))
+    # circ = tq2qiskit(tq.QuantumDevice(n_wires=4), model)
+    # print(circ)
+    # print("The circuit is", circ.draw(output="mpl"))
+    # circ.draw(output="mpl")
+    # use backprop
+    backprop_optimize(model, n_steps=300, lr=0.01)
+    # use parameter shift rule
+    # param_shift_optimize(model, n_steps=500, step_size=100000)
+
+
+"""
+Notes:
+1. input_graph = [(0, 1), (3, 0), (1, 2), (2, 3)], mixer 1st & entangler 2nd, n_layers >= 2, answer is correct.
+
+"""
+
+if __name__ == "__main__":
+    # import pdb
+    # pdb.set_trace()
+
+    main()
diff --git a/examples/qaoa/max_cut_parametershift_noise.py b/examples/qaoa/max_cut_parametershift_noise.py
new file mode 100644
index 00000000..11fd79e2
--- /dev/null
+++ b/examples/qaoa/max_cut_parametershift_noise.py
@@ -0,0 +1,305 @@
+"""
+MIT License
+
+Copyright (c) 2020-present TorchQuantum Authors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+"""
+
+import torch
+import torchquantum as tq
+
+import random
+import numpy as np
+
+from torchquantum.measurement import expval_joint_analytical_density
+
+seed = 0
+random.seed(seed)
+np.random.seed(seed)
+torch.manual_seed(seed)
+
+from torchquantum.plugin import QiskitProcessor, op_history2qiskit
+
+
+class MAXCUT(tq.QuantumModule):
+    """computes the optimal cut for a given graph.
+    outputs: the most probable bitstring decides the set {0 or 1} each
+    node belongs to.
+    """
+
+    def __init__(self, n_wires, input_graph, n_layers):
+        super().__init__()
+
+        self.n_wires = n_wires
+
+        self.input_graph = input_graph  # list of edges
+        self.n_layers = n_layers
+        self.n_edges = len(input_graph)
+
+        self.betas = torch.nn.Parameter(0.01 * torch.rand(self.n_layers))
+        self.gammas = torch.nn.Parameter(0.01 * torch.rand(self.n_layers))
+
+        self.reset_shift_param()
+
+    def mixer(self, qdev, beta, layer_id):
+        """
+        Apply the single rotation and entangling layer of the QAOA ansatz.
+        mixer = exp(-i * beta * sigma_x)
+        """
+
+        for wire in range(self.n_wires):
+            if (
+                    self.shift_param_name == "beta"
+                    and self.shift_wire == wire
+                    and layer_id == self.shift_layer
+            ):
+                degree = self.shift_degree
+            else:
+                degree = 0
+            qdev.rx(
+                wires=wire,
+                params=(beta.unsqueeze(0) + degree),
+            )  # type: ignore
+
+    def entangler(self, qdev, gamma, layer_id):
+        """
+        Apply the single rotation and entangling layer of the QAOA ansatz.
+        entangler = exp(-i * gamma * (1 - sigma_z * sigma_z)/2)
+        """
+        for edge_id, edge in enumerate(self.input_graph):
+            if (
+                    self.shift_param_name == "gamma"
+                    and edge_id == self.shift_edge_id
+                    and layer_id == self.shift_layer
+            ):
+                degree = self.shift_degree
+            else:
+                degree = 0
+            qdev.cx(
+                [edge[0], edge[1]],
+            )  # type: ignore
+            qdev.rz(
+                wires=edge[1],
+                params=(gamma.unsqueeze(0) + degree),
+            )  # type: ignore
+            qdev.cx(
+                [edge[0], edge[1]],
+            )  # type: ignore
+
+    def set_shift_param(self, layer, wire, param_name, degree, edge_id):
+        """
+        set the shift parameter for the parameter shift rule
+        """
+        self.shift_layer = layer
+        self.shift_wire = wire
+        self.shift_param_name = param_name
+        self.shift_degree = degree
+        self.shift_edge_id = edge_id
+
+    def reset_shift_param(self):
+        """
+        reset the shift parameter
+        """
+        self.shift_layer = None
+        self.shift_wire = None
+        self.shift_param_name = None
+        self.shift_degree = None
+        self.shift_edge_id = None
+
+    def edge_to_PauliString(self, edge):
+        # construct pauli string
+        pauli_string = ""
+        for wire in range(self.n_wires):
+            if wire in edge:
+                pauli_string += "Z"
+            else:
+                pauli_string += "I"
+        return pauli_string
+
+    def circuit(self, qdev):
+        """
+        execute the quantum circuit
+        """
+        # print(self.betas, self.gammas)
+        for wire in range(self.n_wires):
+            qdev.h(
+                wires=wire,
+            )  # type: ignore
+
+        for i in range(self.n_layers):
+            self.mixer(qdev, self.betas[i], i)
+            self.entangler(qdev, self.gammas[i], i)
+
+    def forward(self, use_qiskit, measure_all=False):
+        """
+        Apply the QAOA ansatz and only measure the edge qubit on z-basis.
+        Args:
+            if edge is None
+        """
+        qdev = tq.NoiseDevice(n_wires=self.n_wires, device=self.betas.device,
+                              noise_model=tq.NoiseModel(kraus_dict={"Bitflip": 0.12, "Phaseflip": 0.12}))
+
+
+        # print(tq.measure(qdev, n_shots=1024))
+        # compute the expectation value
+        # print(qdev.get_states_1d())
+
+        if not use_qiskit:
+            self.circuit(qdev)
+            expVal = 0
+            for edge in self.input_graph:
+                pauli_string = self.edge_to_PauliString(edge)
+                expv = expval_joint_analytical_density(qdev, observable=pauli_string)
+                expVal += 0.5 * expv
+        else:
+            # use qiskit to compute the expectation value
+            expVal = 0
+            for edge in self.input_graph:
+                pauli_string = self.edge_to_PauliString(edge)
+
+                with torch.no_grad():
+                    self.circuit(qdev)
+                circ = op_history2qiskit(qdev.n_wires, qdev.op_history)
+
+                expv = self.qiskit_processor.process_circs_get_joint_expval([circ], pauli_string)[0]
+                expVal += 0.5 * expv
+            expVal = torch.Tensor([expVal])
+        return expVal
+
+
+def shift_and_run(model, use_qiskit):
+    # flatten the parameters into 1D array
+
+    grad_betas = []
+    grad_gammas = []
+    n_layers = model.n_layers
+    n_wires = model.n_wires
+    n_edges = model.n_edges
+
+    for i in range(n_layers):
+        grad_gamma = 0
+        for k in range(n_edges):
+            model.set_shift_param(i, None, "gamma", np.pi * 0.5, k)
+            out1 = model(use_qiskit)
+            model.reset_shift_param()
+
+            model.set_shift_param(i, None, "gamma", -np.pi * 0.5, k)
+            out2 = model(use_qiskit)
+            model.reset_shift_param()
+
+            grad_gamma += 0.5 * (out1 - out2).squeeze().item()
+        grad_gammas.append(grad_gamma)
+
+        grad_beta = 0
+        for j in range(n_wires):
+            model.set_shift_param(i, j, "beta", np.pi * 0.5, None)
+            out1 = model(use_qiskit)
+            model.reset_shift_param()
+
+            model.set_shift_param(i, j, "beta", -np.pi * 0.5, None)
+            out2 = model(use_qiskit)
+            model.reset_shift_param()
+
+            grad_beta += 0.5 * (out1 - out2).squeeze().item()
+        grad_betas.append(grad_beta)
+
+    return model(use_qiskit), [grad_betas, grad_gammas]
+
+
+def param_shift_optimize(model, n_steps=10, step_size=0.1, use_qiskit=False):
+    """finds the optimal cut where parameter shift rule is used to compute the gradient"""
+    # optimize the parameters and return the optimal values
+    # print(
+    # "The initial parameters are betas = {} and gammas = {}".format(
+    # *model.parameters()
+    # )
+    # )
+    n_layers = model.n_layers
+    for step in range(n_steps):
+        with torch.no_grad():
+            loss, grad_list = shift_and_run(model, use_qiskit=use_qiskit)
+        # param_list = list(model.parameters())
+        # print(
+        # "The initial parameters are betas = {} and gammas = {}".format(
+        # *model.parameters()
+        # )
+        # )
+        # param_list = torch.cat([param.flatten() for param in param_list])
+
+        # print("The shape of the params", len(param_list), param_list[0].shape, param_list)
+        # print("")
+        # print("The shape of the grad_list = {}, 0th elem shape = {}, grad_list = {}".format(len(grad_list), grad_list[0].shape, grad_list))
+        # print(grad_list, loss, model.betas, model.gammas)
+        print(loss)
+        with torch.no_grad():
+            for i in range(n_layers):
+                model.betas[i].copy_(model.betas[i] - step_size * grad_list[0][i])
+                model.gammas[i].copy_(model.gammas[i] - step_size * grad_list[1][i])
+
+            # for param, grad in zip(param_list, grad_list):
+            # modify the parameters and ensure that there are no multiple views
+            # param.copy_(param - step_size * grad)
+        # if step % 5 == 0:
+        # print("Step: {}, Cost Objective: {}".format(step, loss.item()))
+
+        # print(
+        #     "The updated parameters are betas = {} and gammas = {}".format(
+        #         *model.parameters()
+        #     )
+        # )
+    return model(use_qiskit=False, measure_all=True)
+
+
+"""
+Notes:
+1. input_graph = [(0, 1), (3, 0), (1, 2), (2, 3)], mixer 1st & entangler 2nd, n_layers >= 2, answer is correct.
+
+"""
+
+
+def main(use_qiskit):
+    # create a input_graph
+    input_graph = [(0, 1), (0, 3), (1, 2), (2, 3)]
+    n_wires = 4
+    n_layers = 1
+    model = MAXCUT(n_wires=n_wires, input_graph=input_graph, n_layers=n_layers)
+
+    # set the qiskit processor
+    # processor_simulation = QiskitProcessor(use_real_qc=False, n_shots=10000)
+    # model.set_qiskit_processor(processor_simulation)
+
+    # firstly perform simulate
+    # model.to("cuda")
+    # model.to(torch.device("cuda"))
+    # circ = tq2qiskit(tq.QuantumDevice(n_wires=4), model)
+    # print(circ)
+    # print("The circuit is", circ.draw(output="mpl"))
+    # circ.draw(output="mpl")
+    # use backprop
+    # backprop_optimize(model, n_steps=300, lr=0.01)
+    # use parameter shift rule
+    param_shift_optimize(model, n_steps=500, step_size=0.01, use_qiskit=use_qiskit)
+
+
+if __name__ == "__main__":
+    # import pdb
+    # pdb.set_trace()
+    use_qiskit = False
+    main(use_qiskit)
diff --git a/examples/qaoa/max_cut_paramshift.py b/examples/qaoa/max_cut_paramshift.py
index a8467c1d..48b79a44 100644
--- a/examples/qaoa/max_cut_paramshift.py
+++ b/examples/qaoa/max_cut_paramshift.py
@@ -148,7 +148,7 @@ def circuit(self, qdev):
             self.mixer(qdev, self.betas[i], i)
             self.entangler(qdev, self.gammas[i], i)
 
-    def forward(self, use_qiskit):
+    def forward(self, use_qiskit, measure_all=False):
         """
         Apply the QAOA ansatz and only measure the edge qubit on z-basis.
         Args:
@@ -266,7 +266,7 @@ def param_shift_optimize(model, n_steps=10, step_size=0.1, use_qiskit=False):
         #         *model.parameters()
         #     )
         # )
-    return model(measure_all=True)
+    return model(use_qiskit=False,measure_all=True)
 
 
 """
@@ -284,8 +284,8 @@ def main(use_qiskit):
     model = MAXCUT(n_wires=n_wires, input_graph=input_graph, n_layers=n_layers)
     
     # set the qiskit processor
-    processor_simulation = QiskitProcessor(use_real_qc=False, n_shots=10000)
-    model.set_qiskit_processor(processor_simulation)
+    #processor_simulation = QiskitProcessor(use_real_qc=False, n_shots=10000)
+    #model.set_qiskit_processor(processor_simulation)
 
      # firstly perform simulate
     # model.to("cuda")
diff --git a/examples/quantum_lstm/qlstm_noise.py b/examples/quantum_lstm/qlstm_noise.py
new file mode 100644
index 00000000..1587b545
--- /dev/null
+++ b/examples/quantum_lstm/qlstm_noise.py
@@ -0,0 +1,423 @@
+"""
+MIT License
+
+Copyright (c) 2020-present TorchQuantum Authors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+"""
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import torch
+import torch.nn as nn
+import torchquantum as tq
+import torchquantum.functional as tqf
+
+
+class QLSTM(nn.Module):
+    # use 'qiskit.ibmq' instead to run on hardware
+    class QLayer_forget(tq.QuantumModule):
+        def __init__(self):
+            super().__init__()
+            self.n_wires = 4
+            self.encoder = tq.GeneralEncoder(
+                [{'input_idx': [0], 'func': 'rx', 'wires': [0]},
+                 {'input_idx': [1], 'func': 'rx', 'wires': [1]},
+                 {'input_idx': [2], 'func': 'rx', 'wires': [2]},
+                 {'input_idx': [3], 'func': 'rx', 'wires': [3]},
+                 ])
+            self.rx0 = tq.RX(has_params=True, trainable=True)
+            self.rx1 = tq.RX(has_params=True, trainable=True)
+            self.rx2 = tq.RX(has_params=True, trainable=True)
+            self.rx3 = tq.RX(has_params=True, trainable=True)
+            self.measure = tq.MeasureAll_density(tq.PauliZ)
+
+        def forward(self, x):
+            qdev = tq.NoiseDevice(n_wires=self.n_wires, bsz=x.shape[0], device=x.device,
+                                  noise_model=tq.NoiseModel(kraus_dict={"Bitflip": 0.22, "Phaseflip": 0.22}))
+            self.encoder(qdev, x)
+            self.rx0(qdev, wires=0)
+            self.rx1(qdev, wires=1)
+            self.rx2(qdev, wires=2)
+            self.rx3(qdev, wires=3)
+            for k in range(self.n_wires):
+                if k == self.n_wires - 1:
+                    tqf.cnot(qdev, wires=[k, 0])
+                else:
+                    tqf.cnot(qdev, wires=[k, k + 1])
+            return (self.measure(qdev))
+
+    class QLayer_input(tq.QuantumModule):
+        def __init__(self):
+            super().__init__()
+            self.n_wires = 4
+            self.encoder = tq.GeneralEncoder(
+                [{'input_idx': [0], 'func': 'rx', 'wires': [0]},
+                 {'input_idx': [1], 'func': 'rx', 'wires': [1]},
+                 {'input_idx': [2], 'func': 'rx', 'wires': [2]},
+                 {'input_idx': [3], 'func': 'rx', 'wires': [3]},
+                 ])
+            self.rx0 = tq.RX(has_params=True, trainable=True)
+            self.rx1 = tq.RX(has_params=True, trainable=True)
+            self.rx2 = tq.RX(has_params=True, trainable=True)
+            self.rx3 = tq.RX(has_params=True, trainable=True)
+            self.measure = tq.MeasureAll_density(tq.PauliZ)
+
+        def forward(self, x):
+            qdev = tq.NoiseDevice(n_wires=self.n_wires, bsz=x.shape[0], device=x.device,
+                                noise_model = tq.NoiseModel(kraus_dict={"Bitflip": 0.22, "Phaseflip": 0.22}))
+            self.encoder(qdev, x)
+            self.rx0(qdev, wires=0)
+            self.rx1(qdev, wires=1)
+            self.rx2(qdev, wires=2)
+            self.rx3(qdev, wires=3)
+            for k in range(self.n_wires):
+                if k == self.n_wires - 1:
+                    tqf.cnot(qdev, wires=[k, 0])
+                else:
+                    tqf.cnot(qdev, wires=[k, k + 1])
+            return (self.measure(qdev))
+
+    class QLayer_update(tq.QuantumModule):
+        def __init__(self):
+            super().__init__()
+            self.n_wires = 4
+            self.encoder = tq.GeneralEncoder(
+                [{'input_idx': [0], 'func': 'rx', 'wires': [0]},
+                 {'input_idx': [1], 'func': 'rx', 'wires': [1]},
+                 {'input_idx': [2], 'func': 'rx', 'wires': [2]},
+                 {'input_idx': [3], 'func': 'rx', 'wires': [3]},
+                 ])
+            self.rx0 = tq.RX(has_params=True, trainable=True)
+            self.rx1 = tq.RX(has_params=True, trainable=True)
+            self.rx2 = tq.RX(has_params=True, trainable=True)
+            self.rx3 = tq.RX(has_params=True, trainable=True)
+            self.measure = tq.MeasureAll_density(tq.PauliZ)
+
+        def forward(self, x):
+            qdev = tq.NoiseDevice(n_wires=self.n_wires, bsz=x.shape[0], device=x.device,
+                                  noise_model=tq.NoiseModel(kraus_dict={"Bitflip": 0.22, "Phaseflip": 0.22}))
+            self.encoder(qdev, x)
+            self.rx0(qdev, wires=0)
+            self.rx1(qdev, wires=1)
+            self.rx2(qdev, wires=2)
+            self.rx3(qdev, wires=3)
+            for k in range(self.n_wires):
+                if k == self.n_wires - 1:
+                    tqf.cnot(qdev, wires=[k, 0])
+                else:
+                    tqf.cnot(qdev, wires=[k, k + 1])
+            return (self.measure(qdev))
+
+    class QLayer_output(tq.QuantumModule):
+        def __init__(self):
+            super().__init__()
+            self.n_wires = 4
+            self.encoder = tq.GeneralEncoder(
+                [{'input_idx': [0], 'func': 'rx', 'wires': [0]},
+                 {'input_idx': [1], 'func': 'rx', 'wires': [1]},
+                 {'input_idx': [2], 'func': 'rx', 'wires': [2]},
+                 {'input_idx': [3], 'func': 'rx', 'wires': [3]},
+                 ])
+            self.rx0 = tq.RX(has_params=True, trainable=True)
+            self.rx1 = tq.RX(has_params=True, trainable=True)
+            self.rx2 = tq.RX(has_params=True, trainable=True)
+            self.rx3 = tq.RX(has_params=True, trainable=True)
+            self.measure = tq.MeasureAll_density(tq.PauliZ)
+
+        def forward(self, x):
+            qdev = tq.NoiseDevice(n_wires=self.n_wires, bsz=x.shape[0], device=x.device,
+                                    noise_model=tq.NoiseModel(kraus_dict={"Bitflip": 0.22, "Phaseflip": 0.22}))
+            self.encoder(qdev, x)
+            self.rx0(qdev, wires=0)
+            self.rx1(qdev, wires=1)
+            self.rx2(qdev, wires=2)
+            self.rx3(qdev, wires=3)
+            for k in range(self.n_wires):
+                if k == self.n_wires - 1:
+                    tqf.cnot(qdev, wires=[k, 0])
+                else:
+                    tqf.cnot(qdev, wires=[k, k + 1])
+            return (self.measure(qdev))
+
+    def __init__(self,
+                 input_size,
+                 hidden_size,
+                 n_qubits=4,
+                 n_qlayers=1,
+                 batch_first=True,
+                 return_sequences=False,
+                 return_state=False,
+                 backend="default.qubit"):
+        super(QLSTM, self).__init__()
+        self.n_inputs = input_size
+        self.hidden_size = hidden_size
+        self.concat_size = self.n_inputs + self.hidden_size
+        self.n_qubits = n_qubits
+        self.n_qlayers = n_qlayers
+        self.backend = backend  # "default.qubit", "qiskit.basicaer", "qiskit.ibm"
+
+        self.batch_first = batch_first
+        self.return_sequences = return_sequences
+        self.return_state = return_state
+
+        self.clayer_in = torch.nn.Linear(self.concat_size, n_qubits)
+        self.VQC = {
+            'forget': self.QLayer_forget(),
+            'input': self.QLayer_input(),
+            'update': self.QLayer_update(),
+            'output': self.QLayer_output()
+        }
+        self.clayer_out = torch.nn.Linear(self.n_qubits, self.hidden_size)
+        # self.clayer_out = [torch.nn.Linear(n_qubits, self.hidden_size) for _ in range(4)]
+
+    def forward(self, x, init_states=None):
+        '''
+        x.shape is (batch_size, seq_length, feature_size)
+        recurrent_activation -> sigmoid
+        activation -> tanh
+        '''
+        if self.batch_first is True:
+            batch_size, seq_length, features_size = x.size()
+        else:
+            seq_length, batch_size, features_size = x.size()
+
+        hidden_seq = []
+        if init_states is None:
+            h_t = torch.zeros(batch_size, self.hidden_size)  # hidden state (output)
+            c_t = torch.zeros(batch_size, self.hidden_size)  # cell state
+        else:
+            # for now we ignore the fact that in PyTorch you can stack multiple RNNs
+            # so we take only the first elements of the init_states tuple init_states[0][0], init_states[1][0]
+            h_t, c_t = init_states
+            h_t = h_t[0]
+            c_t = c_t[0]
+
+        for t in range(seq_length):
+            # get features from the t-th element in seq, for all entries in the batch
+            x_t = x[:, t, :]
+
+            # Concatenate input and hidden state
+            v_t = torch.cat((h_t, x_t), dim=1)
+
+            # match qubit dimension
+            y_t = self.clayer_in(v_t)
+
+            f_t = torch.sigmoid(self.clayer_out(self.VQC['forget'](y_t)))  # forget block
+            i_t = torch.sigmoid(self.clayer_out(self.VQC['input'](y_t)))  # input block
+            g_t = torch.tanh(self.clayer_out(self.VQC['update'](y_t)))  # update block
+            o_t = torch.sigmoid(self.clayer_out(self.VQC['output'](y_t)))  # output block
+
+            c_t = (f_t * c_t) + (i_t * g_t)
+            h_t = o_t * torch.tanh(c_t)
+
+            hidden_seq.append(h_t.unsqueeze(0))
+        hidden_seq = torch.cat(hidden_seq, dim=0)
+        hidden_seq = hidden_seq.transpose(0, 1).contiguous()
+        return hidden_seq, (h_t, c_t)
+
+
+def prepare_sequence(seq, to_ix):
+    idxs = [to_ix[w] for w in seq]
+    return torch.tensor(idxs, dtype=torch.long)
+
+
+class LSTMTagger(nn.Module):
+    def __init__(self, embedding_dim, hidden_dim, vocab_size, tagset_size, n_qubits=0):
+        super(LSTMTagger, self).__init__()
+        self.hidden_dim = hidden_dim
+
+        self.word_embeddings = nn.Embedding(vocab_size, embedding_dim)
+
+        # The LSTM takes word embeddings as inputs, and outputs hidden states
+        # with dimensionality hidden_dim.
+        if n_qubits > 0:
+            print("Tagger will use Quantum LSTM")
+            self.lstm = QLSTM(embedding_dim, hidden_dim, n_qubits=n_qubits)
+        else:
+            print("Tagger will use Classical LSTM")
+            self.lstm = nn.LSTM(embedding_dim, hidden_dim)
+
+        # The linear layer that maps from hidden state space to tag space
+        self.hidden2tag = nn.Linear(hidden_dim, tagset_size)
+
+    def forward(self, sentence):
+        embeds = self.word_embeddings(sentence)
+        lstm_out, _ = self.lstm(embeds.view(len(sentence), 1, -1))
+        tag_logits = self.hidden2tag(lstm_out.view(len(sentence), -1))
+        tag_scores = F.log_softmax(tag_logits, dim=1)
+        return tag_scores
+
+
+def train(model, n_epochs, training_data, word_to_ix, tag_to_ix):
+    loss_function = nn.NLLLoss()
+    optimizer = optim.SGD(model.parameters(), lr=0.1)
+
+    history = {
+        'loss': [],
+        'acc': []
+    }
+    for epoch in range(n_epochs):
+        losses = []
+        preds = []
+        targets = []
+        for sentence, tags in training_data:
+            # Step 1. Remember that Pytorch accumulates gradients.
+            # We need to clear them out before each instance
+            model.zero_grad()
+
+            # Step 2. Get our inputs ready for the network, that is, turn them into
+            # Tensors of word indices.
+            sentence_in = prepare_sequence(sentence, word_to_ix)
+            labels = prepare_sequence(tags, tag_to_ix)
+
+            # Step 3. Run our forward pass.
+            tag_scores = model(sentence_in)
+
+            # Step 4. Compute the loss, gradients, and update the parameters by
+            #  calling optimizer.step()
+            loss = loss_function(tag_scores, labels)
+            loss.backward()
+            optimizer.step()
+            losses.append(float(loss))
+
+            probs = torch.softmax(tag_scores, dim=-1)
+            preds.append(probs.argmax(dim=-1))
+            targets.append(labels)
+
+        avg_loss = np.mean(losses)
+        history['loss'].append(avg_loss)
+
+        preds = torch.cat(preds)
+        targets = torch.cat(targets)
+        corrects = (preds == targets)
+        accuracy = corrects.sum().float() / float(targets.size(0))
+        history['acc'].append(accuracy)
+
+        print(f"Epoch {epoch + 1} / {n_epochs}: Loss = {avg_loss:.3f} Acc = {accuracy:.2f}")
+
+    return history
+
+
+def print_result(model, training_data, word_to_ix, ix_to_tag):
+    with torch.no_grad():
+        input_sentence = training_data[0][0]
+        labels = training_data[0][1]
+        inputs = prepare_sequence(input_sentence, word_to_ix)
+        tag_scores = model(inputs)
+
+        tag_ids = torch.argmax(tag_scores, dim=1).numpy()
+        tag_labels = [ix_to_tag[k] for k in tag_ids]
+        print(f"Sentence:  {input_sentence}")
+        print(f"Labels:    {labels}")
+        print(f"Predicted: {tag_labels}")
+
+
+from matplotlib import pyplot as plt
+
+
+def plot_history(history_classical, history_quantum):
+    loss_c = history_classical['loss']
+    acc_c = history_classical['acc']
+    loss_q = history_quantum['loss']
+    acc_q = history_quantum['acc']
+    n_epochs = max([len(loss_c), len(loss_q)])
+    x_epochs = [i for i in range(n_epochs)]
+
+    fig, ax1 = plt.subplots()
+
+    ax1.set_xlabel("Epoch")
+    ax1.set_ylabel("Loss")
+    ax1.plot(loss_c, label="Classical LSTM loss", color='orange', linestyle='dashed')
+    ax1.plot(loss_q, label="Quantum LSTM loss", color='red', linestyle='solid')
+
+    ax2 = ax1.twinx()
+    ax2.set_ylabel("Accuracy")
+    ax2.plot(acc_c, label="Classical LSTM accuracy", color='steelblue', linestyle='dashed')
+    ax2.plot(acc_q, label="Quantum LSTM accuracy", color='blue', linestyle='solid')
+
+    plt.title("Part-of-Speech Tagger Training__torch")
+    plt.ylim(0., 1.1)
+    # plt.legend(loc="upper right")
+    fig.legend(loc="upper right", bbox_to_anchor=(1, 0.8), bbox_transform=ax1.transAxes)
+
+    plt.savefig("pos_training_torch.pdf")
+    plt.savefig("pos_training_torch.png")
+
+    plt.show()
+
+
+def main():
+    tag_to_ix = {"DET": 0, "NN": 1, "V": 2}  # Assign each tag with a unique index
+    ix_to_tag = {i: k for k, i in tag_to_ix.items()}
+
+    training_data = [
+        # Tags are: DET - determiner; NN - noun; V - verb
+        # For example, the word "The" is a determiner
+        ("The dog ate the apple".split(), ["DET", "NN", "V", "DET", "NN"]),
+        ("Everybody read that book".split(), ["NN", "V", "DET", "NN"])
+    ]
+    word_to_ix = {}
+
+    # For each words-list (sentence) and tags-list in each tuple of training_data
+    for sent, tags in training_data:
+        for word in sent:
+            if word not in word_to_ix:  # word has not been assigned an index yet
+                word_to_ix[word] = len(word_to_ix)  # Assign each word with a unique index
+
+    print(f"Vocabulary: {word_to_ix}")
+    print(f"Entities: {ix_to_tag}")
+
+    embedding_dim = 8
+    hidden_dim = 6
+    n_epochs = 300
+
+    model_classical = LSTMTagger(embedding_dim,
+                                 hidden_dim,
+                                 vocab_size=len(word_to_ix),
+                                 tagset_size=len(tag_to_ix),
+                                 n_qubits=0)
+
+    history_classical = train(model_classical, n_epochs, training_data, word_to_ix, tag_to_ix)
+
+    print_result(model_classical, training_data, word_to_ix, ix_to_tag)
+
+    n_qubits = 4
+
+    model_quantum = LSTMTagger(embedding_dim,
+                               hidden_dim,
+                               vocab_size=len(word_to_ix),
+                               tagset_size=len(tag_to_ix),
+                               n_qubits=n_qubits)
+
+    history_quantum = train(model_quantum, n_epochs, training_data, word_to_ix, tag_to_ix)
+
+    print_result(model_quantum, training_data, word_to_ix, ix_to_tag)
+
+    plot_history(history_classical, history_quantum)
+
+
+if __name__ == "__main__":
+    # import pdb
+    # pdb.set_trace()
+
+    main()
diff --git a/examples/quantumnat/quantumnat_noise.py b/examples/quantumnat/quantumnat_noise.py
new file mode 100644
index 00000000..e69de29b
diff --git a/examples/quanvolution/quanvolution_noise.py b/examples/quanvolution/quanvolution_noise.py
new file mode 100644
index 00000000..33a329a1
--- /dev/null
+++ b/examples/quanvolution/quanvolution_noise.py
@@ -0,0 +1,250 @@
+"""
+MIT License
+
+Copyright (c) 2020-present TorchQuantum Authors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+"""
+
+import torchquantum as tq
+import torchquantum.functional as tqf
+
+import torch
+import torch.nn.functional as F
+import torch.optim as optim
+import numpy as np
+import random
+
+from torchquantum.dataset import MNIST
+from torch.optim.lr_scheduler import CosineAnnealingLR
+
+
+class QuanvolutionFilter(tq.QuantumModule):
+    def __init__(self):
+        super().__init__()
+        self.n_wires = 4
+        self.encoder = tq.GeneralEncoder(
+            [
+                {"input_idx": [0], "func": "ry", "wires": [0]},
+                {"input_idx": [1], "func": "ry", "wires": [1]},
+                {"input_idx": [2], "func": "ry", "wires": [2]},
+                {"input_idx": [3], "func": "ry", "wires": [3]},
+            ]
+        )
+
+        self.q_layer = tq.RandomLayer(n_ops=8, wires=list(range(self.n_wires)))
+        self.measure = tq.MeasureAll_density(tq.PauliZ)
+
+    def forward(self, x, use_qiskit=False):
+        bsz = x.shape[0]
+        qdev = tq.NoiseDevice(self.n_wires, bsz=bsz, device=x.device,
+                                noise_model=tq.NoiseModel(kraus_dict={"Bitflip": 0.22, "Phaseflip": 0.22}))
+        size = 28
+        x = x.view(bsz, size, size)
+
+        data_list = []
+
+        for c in range(0, size, 2):
+            for r in range(0, size, 2):
+                data = torch.transpose(
+                    torch.cat(
+                        (x[:, c, r], x[:, c, r + 1], x[:, c + 1, r], x[:, c + 1, r + 1])
+                    ).view(4, bsz),
+                    0,
+                    1,
+                )
+                if use_qiskit:
+                    data = self.qiskit_processor.process_parameterized(
+                        qdev, self.encoder, self.q_layer, self.measure, data
+                    )
+                else:
+                    self.encoder(qdev, data)
+                    self.q_layer(qdev)
+                    data = self.measure(qdev)
+
+                data_list.append(data.view(bsz, 4))
+
+        result = torch.cat(data_list, dim=1).float()
+
+        return result
+
+
+class HybridModel(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.qf = QuanvolutionFilter()
+        self.linear = torch.nn.Linear(4 * 14 * 14, 10)
+
+    def forward(self, x, use_qiskit=False):
+        with torch.no_grad():
+            x = self.qf(x, use_qiskit)
+        x = self.linear(x)
+        return F.log_softmax(x, -1)
+
+
+class HybridModel_without_qf(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.linear = torch.nn.Linear(28 * 28, 10)
+
+    def forward(self, x, use_qiskit=False):
+        x = x.view(-1, 28 * 28)
+        x = self.linear(x)
+        return F.log_softmax(x, -1)
+
+
+def train(dataflow, model, device, optimizer):
+    for feed_dict in dataflow["train"]:
+        inputs = feed_dict["image"].to(device)
+        targets = feed_dict["digit"].to(device)
+
+        outputs = model(inputs)
+        loss = F.nll_loss(outputs, targets)
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+        print(f"loss: {loss.item()}", end="\r")
+
+
+def valid_test(dataflow, split, model, device, qiskit=False):
+    target_all = []
+    output_all = []
+    with torch.no_grad():
+        for feed_dict in dataflow[split]:
+            inputs = feed_dict["image"].to(device)
+            targets = feed_dict["digit"].to(device)
+
+            outputs = model(inputs, use_qiskit=qiskit)
+
+            target_all.append(targets)
+            output_all.append(outputs)
+        target_all = torch.cat(target_all, dim=0)
+        output_all = torch.cat(output_all, dim=0)
+
+    _, indices = output_all.topk(1, dim=1)
+    masks = indices.eq(target_all.view(-1, 1).expand_as(indices))
+    size = target_all.shape[0]
+    corrects = masks.sum().item()
+    accuracy = corrects / size
+    loss = F.nll_loss(output_all, target_all).item()
+
+    print(f"{split} set accuracy: {accuracy}")
+    print(f"{split} set loss: {loss}")
+
+    return accuracy, loss
+
+
+def main():
+    train_model_without_qf = True
+    n_epochs = 15
+
+    random.seed(42)
+    np.random.seed(42)
+    torch.manual_seed(42)
+    dataset = MNIST(
+        root="./mnist_data",
+        train_valid_split_ratio=[0.9, 0.1],
+        n_test_samples=300,
+        n_train_samples=500,
+    )
+    dataflow = dict()
+
+    for split in dataset:
+        sampler = torch.utils.data.RandomSampler(dataset[split])
+        dataflow[split] = torch.utils.data.DataLoader(
+            dataset[split],
+            batch_size=10,
+            sampler=sampler,
+            num_workers=8,
+            pin_memory=True,
+        )
+
+    use_cuda = torch.cuda.is_available()
+    device = torch.device("cuda" if use_cuda else "cpu")
+    model = HybridModel().to(device)
+    model_without_qf = HybridModel_without_qf().to(device)
+    optimizer = optim.Adam(model.parameters(), lr=5e-3, weight_decay=1e-4)
+    scheduler = CosineAnnealingLR(optimizer, T_max=n_epochs)
+
+    accu_list1 = []
+    loss_list1 = []
+    accu_list2 = []
+    loss_list2 = []
+    for epoch in range(1, n_epochs + 1):
+        # train
+        print(f"Epoch {epoch}:")
+        train(dataflow, model, device, optimizer)
+        print(optimizer.param_groups[0]["lr"])
+
+        # valid
+        accu, loss = valid_test(
+            dataflow,
+            "test",
+            model,
+            device,
+        )
+        accu_list1.append(accu)
+        loss_list1.append(loss)
+        scheduler.step()
+
+    if train_model_without_qf:
+        optimizer = optim.Adam(
+            model_without_qf.parameters(), lr=5e-3, weight_decay=1e-4
+        )
+        scheduler = CosineAnnealingLR(optimizer, T_max=n_epochs)
+        for epoch in range(1, n_epochs + 1):
+            # train
+            print(f"Epoch {epoch}:")
+            train(dataflow, model_without_qf, device, optimizer)
+            print(optimizer.param_groups[0]["lr"])
+
+            # valid
+            accu, loss = valid_test(dataflow, "test", model_without_qf, device)
+            accu_list2.append(accu)
+            loss_list2.append(loss)
+
+            scheduler.step()
+
+    # run on real QC
+    try:
+        from qiskit import IBMQ
+        from torchquantum.plugin import QiskitProcessor
+
+        # firstly perform simulate
+        print(f"\nTest with Qiskit Simulator")
+        processor_simulation = QiskitProcessor(use_real_qc=False)
+        model.qf.set_qiskit_processor(processor_simulation)
+        valid_test(dataflow, "test", model, device, qiskit=True)
+        # then try to run on REAL QC
+        backend_name = "ibmq_quito"
+        print(f"\nTest on Real Quantum Computer {backend_name}")
+        processor_real_qc = QiskitProcessor(use_real_qc=True, backend_name=backend_name)
+        model.qf.set_qiskit_processor(processor_real_qc)
+        valid_test(dataflow, "test", model, device, qiskit=True)
+    except ImportError:
+        print(
+            "Please install qiskit, create an IBM Q Experience Account and "
+            "save the account token according to the instruction at "
+            "'https://github.com/Qiskit/qiskit-ibmq-provider', "
+            "then try again."
+        )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/quanvolution/quanvolution_trainable_quantum_layer_noise.py b/examples/quanvolution/quanvolution_trainable_quantum_layer_noise.py
new file mode 100644
index 00000000..e69de29b
diff --git a/examples/qubit_rotation/qubit_rotation_noise.py b/examples/qubit_rotation/qubit_rotation_noise.py
new file mode 100644
index 00000000..13a20293
--- /dev/null
+++ b/examples/qubit_rotation/qubit_rotation_noise.py
@@ -0,0 +1,69 @@
+"""
+Qubit Rotation Optimization, adapted from https://pennylane.ai/qml/demos/tutorial_qubit_rotation
+"""
+
+# import dependencies
+import torchquantum as tq
+import torch
+from torchquantum.measurement import expval_joint_analytical_density
+
+
+class OptimizationModel(torch.nn.Module):
+    """
+    Circuit with rx and ry gate
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.rx0 = tq.RX(has_params=True, trainable=True, init_params=0.011)
+        self.ry0 = tq.RY(has_params=True, trainable=True, init_params=0.012)
+
+    def forward(self):
+        # create a quantum device to run the gates
+        qdev = tq.NoiseDevice(n_wires=1,
+                              noise_model=tq.NoiseModel(kraus_dict={"Bitflip": 0.01, "Phaseflip": 0.01}))
+
+        # add some trainable gates (need to instantiate ahead of time)
+        self.rx0(qdev, wires=0)
+        self.ry0(qdev, wires=0)
+
+        # return the analytic expval from Z
+        return expval_joint_analytical_density(qdev, "Z")
+
+
+# train function to get expval as low as possible (ideally -1)
+def train(model, device, optimizer):
+    outputs = model()
+    loss = outputs
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+
+    return loss.item()
+
+
+# main function to run the optimization
+def main():
+    seed = 0
+    torch.manual_seed(seed)
+
+    use_cuda = torch.cuda.is_available()
+    device = torch.device("cuda" if use_cuda else "cpu")
+
+    model = OptimizationModel()
+    n_epochs = 200
+    optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
+
+    for epoch in range(1, n_epochs + 1):
+        # train
+        loss = train(model, device, optimizer)
+        output = (model.rx0.params[0].item(), model.ry0.params[0].item())
+
+        print(f"Epoch {epoch}: {output}")
+
+        if epoch % 10 == 0:
+            print(f"Loss after step {epoch}: {loss}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/regression/run_regression_noise.py b/examples/regression/run_regression_noise.py
new file mode 100644
index 00000000..3a146721
--- /dev/null
+++ b/examples/regression/run_regression_noise.py
@@ -0,0 +1,267 @@
+"""
+MIT License
+
+Copyright (c) 2020-present TorchQuantum Authors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+"""
+
+import torch
+import torch.nn.functional as F
+import torch.optim as optim
+import argparse
+
+import torchquantum as tq
+
+from torch.optim.lr_scheduler import CosineAnnealingLR
+
+import random
+import numpy as np
+
+# data is cos(theta)|000> + e^(j * phi)sin(theta) |111>
+
+from torchpack.datasets.dataset import Dataset
+
+
+def gen_data(L, N):
+    omega_0 = np.zeros([2 ** L], dtype="complex_")
+    omega_0[0] = 1 + 0j
+
+    omega_1 = np.zeros([2 ** L], dtype="complex_")
+    omega_1[-1] = 1 + 0j
+
+    states = np.zeros([N, 2 ** L], dtype="complex_")
+
+    thetas = 2 * np.pi * np.random.rand(N)
+    phis = 2 * np.pi * np.random.rand(N)
+
+    for i in range(N):
+        states[i] = (
+                np.cos(thetas[i]) * omega_0
+                + np.exp(1j * phis[i]) * np.sin(thetas[i]) * omega_1
+        )
+
+    X = np.sin(2 * thetas) * np.cos(phis)
+
+    return states, X
+
+
+class RegressionDataset:
+    def __init__(self, split, n_samples, n_wires):
+        self.split = split
+        self.n_samples = n_samples
+        self.n_wires = n_wires
+
+        self.states, self.Xlabel = gen_data(self.n_wires, self.n_samples)
+
+    def __getitem__(self, index: int):
+        instance = {"states": self.states[index], "Xlabel": self.Xlabel[index]}
+        return instance
+
+    def __len__(self) -> int:
+        return self.n_samples
+
+
+class Regression(Dataset):
+    def __init__(self, n_train, n_valid, n_wires):
+        n_samples_dict = {"train": n_train, "valid": n_valid}
+        super().__init__(
+            {
+                split: RegressionDataset(
+                    split=split, n_samples=n_samples_dict[split], n_wires=n_wires
+                )
+                for split in ["train", "valid"]
+            }
+        )
+
+
+class QModel(tq.QuantumModule):
+    def __init__(self, n_wires, n_blocks, add_fc=False):
+        super().__init__()
+        # inside one block, we have one u3 layer one each qubit and one layer
+        # cu3 layer with ring connection
+        self.n_wires = n_wires
+        self.n_blocks = n_blocks
+        self.u3_layers = tq.QuantumModuleList()
+        self.cu3_layers = tq.QuantumModuleList()
+        for _ in range(n_blocks):
+            self.u3_layers.append(
+                tq.Op1QAllLayer(
+                    op=tq.U3,
+                    n_wires=n_wires,
+                    has_params=True,
+                    trainable=True,
+                )
+            )
+            self.cu3_layers.append(
+                tq.Op2QAllLayer(
+                    op=tq.CU3,
+                    n_wires=n_wires,
+                    has_params=True,
+                    trainable=True,
+                    circular=True,
+                )
+            )
+        self.measure = tq.MeasureAll_density(tq.PauliZ)
+        self.add_fc = add_fc
+        if add_fc:
+            self.fc_layer = torch.nn.Linear(n_wires, 1)
+
+    def forward(self, input_states):
+        qdev = tq.NoiseDevice(
+            n_wires=self.n_wires, bsz=input_states.shape[0], device=input_states.device,
+            noise_model=tq.NoiseModel(kraus_dict={"Bitflip": 0.22, "Phaseflip": 0.22}))
+        # firstly set the qdev
+        bsz = input_states.shape[0]
+        input_states = torch.reshape(input_states, [bsz] + [2] * self.n_wires)
+
+        qdev.clone_from_states(input_states)
+        for k in range(self.n_blocks):
+            self.u3_layers[k](qdev)
+            self.cu3_layers[k](qdev)
+
+        res = self.measure(qdev)
+        if self.add_fc:
+            res = self.fc_layer(res)
+        else:
+            res = res[:, 1]
+        return res
+
+
+def train(dataflow, model, device, optimizer):
+    for feed_dict in dataflow["train"]:
+        inputs = feed_dict["states"].to(device).to(torch.complex64)
+        targets = feed_dict["Xlabel"].to(device).to(torch.float)
+
+        outputs = model(inputs)
+
+        loss = F.mse_loss(outputs, targets)
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+        print(f"loss: {loss.item()}")
+
+
+def valid_test(dataflow, split, model, device):
+    target_all = []
+    output_all = []
+    with torch.no_grad():
+        for feed_dict in dataflow[split]:
+            inputs = feed_dict["states"].to(device).to(torch.complex64)
+            targets = feed_dict["Xlabel"].to(device).to(torch.float)
+
+            outputs = model(inputs)
+
+            target_all.append(targets)
+            output_all.append(outputs)
+        target_all = torch.cat(target_all, dim=0)
+        output_all = torch.cat(output_all, dim=0)
+
+    loss = F.mse_loss(output_all, target_all)
+
+    print(f"{split} set loss: {loss}")
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--pdb", action="store_true", help="debug with pdb")
+    parser.add_argument(
+        "--bsz", type=int, default=32, help="batch size for training and validation"
+    )
+    parser.add_argument("--n_wires", type=int, default=3, help="number of qubits")
+    parser.add_argument(
+        "--n_blocks",
+        type=int,
+        default=2,
+        help="number of blocks, each contain one layer of "
+             "U3 gates and one layer of CU3 with "
+             "ring connections",
+    )
+    parser.add_argument(
+        "--n_train", type=int, default=300, help="number of training samples"
+    )
+    parser.add_argument(
+        "--n_valid", type=int, default=1000, help="number of validation samples"
+    )
+    parser.add_argument(
+        "--epochs", type=int, default=100, help="number of training epochs"
+    )
+    parser.add_argument(
+        "--addfc", action="store_true", help="add a final classical FC layer"
+    )
+
+    args = parser.parse_args()
+
+    if args.pdb:
+        import pdb
+
+        pdb.set_trace()
+
+    seed = 0
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+
+    dataset = Regression(
+        n_train=args.n_train,
+        n_valid=args.n_valid,
+        n_wires=args.n_wires,
+    )
+
+    dataflow = dict()
+
+    for split in dataset:
+        if split == "train":
+            sampler = torch.utils.data.RandomSampler(dataset[split])
+        else:
+            sampler = torch.utils.data.SequentialSampler(dataset[split])
+        dataflow[split] = torch.utils.data.DataLoader(
+            dataset[split],
+            batch_size=args.bsz,
+            sampler=sampler,
+            num_workers=1,
+            pin_memory=True,
+        )
+
+    use_cuda = torch.cuda.is_available()
+    device = torch.device("cuda" if use_cuda else "cpu")
+
+    model = QModel(n_wires=args.n_wires, n_blocks=args.n_blocks, add_fc=args.addfc).to(
+        device
+    )
+
+    n_epochs = args.epochs
+    optimizer = optim.Adam(model.parameters(), lr=5e-3, weight_decay=1e-4)
+    scheduler = CosineAnnealingLR(optimizer, T_max=n_epochs)
+
+    for epoch in range(1, n_epochs + 1):
+        # train
+        print(f"Epoch {epoch}, LR: {optimizer.param_groups[0]['lr']}")
+        train(dataflow, model, device, optimizer)
+
+        # valid
+        valid_test(dataflow, "valid", model, device)
+        scheduler.step()
+
+    # final valid
+    valid_test(dataflow, "valid", model, device)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/train_unitary_prep/train_unitary_prep_noise.py b/examples/train_unitary_prep/train_unitary_prep_noise.py
new file mode 100644
index 00000000..6f38ca42
--- /dev/null
+++ b/examples/train_unitary_prep/train_unitary_prep_noise.py
@@ -0,0 +1,118 @@
+"""
+MIT License
+
+Copyright (c) 2020-present TorchQuantum Authors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+"""
+
+import torch
+import torch.optim as optim
+import argparse
+
+import torchquantum as tq
+from torch.optim.lr_scheduler import CosineAnnealingLR
+
+import random
+import numpy as np
+
+
+class QModel(tq.QuantumModule):
+    def __init__(self):
+        super().__init__()
+        self.n_wires = 2
+        self.u3_0 = tq.U3(has_params=True, trainable=True)
+        self.u3_1 = tq.U3(has_params=True, trainable=True)
+        self.cu3_0 = tq.CU3(has_params=True, trainable=True)
+        self.cu3_1 = tq.CU3(has_params=True, trainable=True)
+        self.u3_2 = tq.U3(has_params=True, trainable=True)
+        self.u3_3 = tq.U3(has_params=True, trainable=True)
+
+    def forward(self, q_device: tq.NoiseDevice):
+        self.u3_0(q_device, wires=0)
+        self.u3_1(q_device, wires=1)
+        self.cu3_0(q_device, wires=[0, 1])
+        self.u3_2(q_device, wires=0)
+        self.u3_3(q_device, wires=1)
+        self.cu3_1(q_device, wires=[1, 0])
+
+
+def train(target_unitary, model, optimizer):
+    result_unitary = model.get_unitary()
+
+    # https://link.aps.org/accepted/10.1103/PhysRevA.95.042318 unitary fidelity according to table 1
+
+    # compute the unitary infidelity
+    loss = 1 - (torch.trace(target_unitary.T.conj() @ result_unitary) / target_unitary.shape[0]).abs() ** 2
+
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+    print(
+        f"infidelity (loss): {loss.item()}, \n target unitary : "
+        f"{target_unitary.detach().cpu().numpy()}, \n "
+        f"result unitary : {result_unitary.detach().cpu().numpy()}\n"
+    )
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--epochs", type=int, default=1000, help="number of training epochs"
+    )
+
+    parser.add_argument("--pdb", action="store_true", help="debug with pdb")
+
+    args = parser.parse_args()
+
+    if args.pdb:
+        import pdb
+        pdb.set_trace()
+
+    seed = 42
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+
+    use_cuda = torch.cuda.is_available()
+    device = torch.device("cuda" if use_cuda else "cpu")
+
+    model = QModel().to(device)
+
+    n_epochs = args.epochs
+    optimizer = optim.Adam(model.parameters(), lr=1e-2, weight_decay=0)
+    scheduler = CosineAnnealingLR(optimizer, T_max=n_epochs)
+
+    target_unitary = torch.tensor(
+    [
+        [1, 0, 0, 0],
+        [0, 1, 0, 0],
+        [0, 0, 1, 0],
+        [0, 0, 0, 1j]
+    ]
+    , dtype=torch.complex64)
+
+    for epoch in range(1, n_epochs + 1):
+        print(f"Epoch {epoch}, LR: {optimizer.param_groups[0]['lr']}")
+        train(target_unitary, model, optimizer)
+        scheduler.step()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/vqe/vqe_noise.py b/examples/vqe/vqe_noise.py
new file mode 100644
index 00000000..f7d89109
--- /dev/null
+++ b/examples/vqe/vqe_noise.py
@@ -0,0 +1,179 @@
+"""
+MIT License
+
+Copyright (c) 2020-present TorchQuantum Authors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+"""
+
+import torchquantum as tq
+import torch
+from torchquantum.util.vqe_utils import parse_hamiltonian_file
+import random
+import numpy as np
+import argparse
+import torch.optim as optim
+
+from torch.optim.lr_scheduler import CosineAnnealingLR
+from torchquantum.measurement import expval_joint_analytical_density
+
+
+class QVQEModel(tq.QuantumModule):
+    def __init__(self, arch, hamil_info):
+        super().__init__()
+        self.arch = arch
+        self.hamil_info = hamil_info
+        self.n_wires = hamil_info["n_wires"]
+        self.n_blocks = arch["n_blocks"]
+        self.u3_layers = tq.QuantumModuleList()
+        self.cu3_layers = tq.QuantumModuleList()
+        for _ in range(self.n_blocks):
+            self.u3_layers.append(
+                tq.Op1QAllLayer(
+                    op=tq.U3,
+                    n_wires=self.n_wires,
+                    has_params=True,
+                    trainable=True,
+                )
+            )
+            self.cu3_layers.append(
+                tq.Op2QAllLayer(
+                    op=tq.CU3,
+                    n_wires=self.n_wires,
+                    has_params=True,
+                    trainable=True,
+                    circular=True,
+                )
+            )
+
+    def forward(self):
+        qdev = tq.NoiseDevice(
+            n_wires=self.n_wires, bsz=1, device=next(self.parameters()).device,
+            noise_model=tq.NoiseModel(kraus_dict={"Bitflip": 0.22, "Phaseflip": 0.22})
+        )
+
+        for k in range(self.n_blocks):
+            self.u3_layers[k](qdev)
+            self.cu3_layers[k](qdev)
+
+        expval = 0
+        for hamil in self.hamil_info["hamil_list"]:
+            expval += (
+                expval_joint_analytical_density(qdev, observable=hamil["pauli_string"])
+                * hamil["coeff"]
+            )
+
+        return expval
+
+
+def train(model, optimizer, n_steps=1):
+    for _ in range(n_steps):
+        loss = model()
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+        print(f"Expectation of energy: {loss.item()}")
+
+
+def valid_test(model):
+    with torch.no_grad():
+        loss = model()
+
+    print(f"validation: expectation of energy: {loss.item()}")
+
+
+def process_hamil_info(hamil_info):
+    hamil_list = hamil_info["hamil_list"]
+    n_wires = hamil_info["n_wires"]
+    all_info = []
+
+    for hamil in hamil_list:
+        pauli_string = ""
+        for i in range(n_wires):
+            if i in hamil["wires"]:
+                wire = hamil["wires"].index(i)
+                pauli_string += hamil["observables"][wire].upper()
+            else:
+                pauli_string += "I"
+        all_info.append({"pauli_string": pauli_string, "coeff": hamil["coefficient"]})
+    hamil_info["hamil_list"] = all_info
+    return hamil_info
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--pdb", action="store_true", help="debug with pdb")
+    parser.add_argument(
+        "--n_blocks",
+        type=int,
+        default=2,
+        help="number of blocks, each contain one layer of "
+        "U3 gates and one layer of CU3 with "
+        "ring connections",
+    )
+    parser.add_argument(
+        "--steps_per_epoch", type=int, default=10, help="number of training epochs"
+    )
+    parser.add_argument(
+        "--epochs", type=int, default=100, help="number of training epochs"
+    )
+    parser.add_argument(
+        "--hamil_filename",
+        type=str,
+        default="h2.txt",
+        help="number of training epochs",
+    )
+
+    args = parser.parse_args()
+
+    if args.pdb:
+        import pdb
+
+        pdb.set_trace()
+
+    seed = 0
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+
+    hamil_info = process_hamil_info(parse_hamiltonian_file(args.hamil_filename))
+
+    use_cuda = torch.cuda.is_available()
+    device = torch.device("cuda" if use_cuda else "cpu")
+    model = QVQEModel(arch={"n_blocks": args.n_blocks}, hamil_info=hamil_info)
+
+    model.to(device)
+
+    n_epochs = args.epochs
+    optimizer = optim.Adam(model.parameters(), lr=5e-3, weight_decay=1e-4)
+    scheduler = CosineAnnealingLR(optimizer, T_max=n_epochs)
+
+    for epoch in range(1, n_epochs + 1):
+        # train
+        print(f"Epoch {epoch}, LR: {optimizer.param_groups[0]['lr']}")
+        train(model, optimizer, n_steps=args.steps_per_epoch)
+
+        scheduler.step()
+
+    # final valid
+    valid_test(model)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/requirements.txt b/requirements.txt
index 8bf4d45c..24cb83d7 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,16 +2,16 @@
 dill==0.3.4
 matplotlib>=3.3.2
 nbsphinx
-numpy>=1.19.2
+numpy>=1.19.2,<2
 
 opt_einsum
 pathos>=0.2.7
 pylatexenc>=2.10
 pyscf>=2.0.1
-qiskit>=0.39.0,<1.0.0
+qiskit>=1.0.0
 recommonmark
-qiskit_ibm_runtime==0.20.0
-qiskit-aer==0.13.3
+qiskit-ibm-runtime>=0.20.0
+qiskit-aer>=0.13.3
 
 scipy>=1.5.2
 setuptools>=52.0.0
diff --git a/setup.py b/setup.py
index abe0e123..73bb9acd 100644
--- a/setup.py
+++ b/setup.py
@@ -37,6 +37,7 @@
         name="torchquantum",
         version=VERSION["version"],
         description="Quantum Computing in PyTorch",
+        long_description=open("README.md").read(),
         url="https://github.com/mit-han-lab/torchquantum",
         author="Shreya Chaudhary, Zhuoyang Ye, Jiannan Cao, Jessica Ding, Jiai Gu, Song Han, Zirui Li, Zhiding Liang, Pengyu Liu, Mohammadreza Tavasoli, Hanrui Wang",
         author_email="hanruiwang.hw@gmail.com",
diff --git a/test/algorithm/test_hamiltonian.py b/test/algorithm/test_hamiltonian.py
index e5e8a60f..4b93fe45 100644
--- a/test/algorithm/test_hamiltonian.py
+++ b/test/algorithm/test_hamiltonian.py
@@ -132,8 +132,13 @@ def test_hamiltonian():
             ]
         ),
     )
+    import os
 
-    hamil = Hamiltonian.from_file("test/algorithm/h2.txt")
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    file_path = os.path.join(current_dir, '..', 'algorithm', 'h2.txt')
+    hamil = Hamiltonian.from_file(file_path)
+
+    #hamil = Hamiltonian.from_file("./h2.txt")
 
     assert np.allclose(
         hamil.matrix.cpu().detach().numpy(),
diff --git a/test/algorithm/test_qcbm.py b/test/algorithm/test_qcbm.py
new file mode 100644
index 00000000..333a25bb
--- /dev/null
+++ b/test/algorithm/test_qcbm.py
@@ -0,0 +1,31 @@
+from torchquantum.algorithm.qcbm import QCBM, MMDLoss
+import torchquantum as tq
+import torch
+
+
+def test_qcbm_forward():
+    n_wires = 3
+    n_layers = 3
+    ops = []
+    for l in range(n_layers):
+        for q in range(n_wires):
+            ops.append({"name": "rx", "wires": q, "params": 0.0, "trainable": True})
+        for q in range(n_wires - 1):
+            ops.append({"name": "cnot", "wires": [q, q + 1]})
+
+    data = torch.ones(2**n_wires)
+    qmodule = tq.QuantumModule.from_op_history(ops)
+    qcbm = QCBM(n_wires, qmodule)
+    probs = qcbm()
+    expected = torch.tensor([1.0, 0, 0, 0, 0, 0, 0, 0])
+    assert torch.allclose(probs, expected)
+
+
+def test_mmd_loss():
+    n_wires = 2
+    bandwidth = torch.tensor([0.1, 1.0])
+    space = torch.arange(2**n_wires)
+
+    mmd = MMDLoss(bandwidth, space)
+    loss = mmd(torch.zeros(4), torch.zeros(4))
+    assert torch.isclose(loss, torch.tensor(0.0), rtol=1e-5)
diff --git a/test/encoding/test_encodings.py b/test/encoding/test_encodings.py
new file mode 100644
index 00000000..9ee64910
--- /dev/null
+++ b/test/encoding/test_encodings.py
@@ -0,0 +1,81 @@
+"""
+MIT License
+
+Copyright (c) 2020-present TorchQuantum Authors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+"""
+
+# test the controlled unitary function
+
+
+import torchquantum as tq
+import torch
+from test.utils import check_all_close
+
+
+def test_GeneralEncoder():
+
+    parameterised_funclist = [
+        {"input_idx": [0], "func": "crx", "wires": [1, 0]},
+        {"input_idx": [1, 2, 3], "func": "u3", "wires": [1]},
+        {"input_idx": [4], "func": "ry", "wires": [0]},
+        {"input_idx": [5], "func": "ry", "wires": [1]},
+    ]
+
+    semiparam_funclist = [
+        {"params": [0.2], "func": "crx", "wires": [1, 0]},
+        {"params": [0.3, 0.4, 0.5], "func": "u3", "wires": [1]},
+        {"input_idx": [0], "func": "ry", "wires": [0]},
+        {"input_idx": [1], "func": "ry", "wires": [1]},
+    ]
+
+    expected_states = torch.complex(
+        torch.Tensor(
+            [[0.8423, 0.4474, 0.2605, 0.1384], [0.7649, 0.5103, 0.3234, 0.2157]]
+        ),
+        torch.Tensor(
+            [[-0.0191, 0.0522, -0.0059, 0.0162], [-0.0233, 0.0483, -0.0099, 0.0204]]
+        ),
+    )
+
+    parameterised_enc = tq.GeneralEncoder(parameterised_funclist)
+    semiparam_enc = tq.GeneralEncoder(semiparam_funclist)
+
+    param_vec = torch.Tensor(
+        [[0.2, 0.3, 0.4, 0.5, 0.6, 0.7], [0.2, 0.3, 0.4, 0.5, 0.8, 0.9]]
+    )
+    semiparam_vec = torch.Tensor([[0.6, 0.7], [0.8, 0.9]])
+
+    qd = tq.QuantumDevice(n_wires=2)
+
+    qd.reset_states(bsz=2)
+    parameterised_enc(qd, param_vec)
+    state1 = qd.get_states_1d()
+
+    qd.reset_states(bsz=2)
+    semiparam_enc(qd, semiparam_vec)
+    state2 = qd.get_states_1d()
+
+    check_all_close(state1, state2)
+    check_all_close(state1, expected_states)
+
+
+if __name__ == "__main__":
+    test_GeneralEncoder()
diff --git a/test/functional/test_controlled_unitary.py b/test/functional/test_controlled_unitary.py
index 652ece59..d7e78660 100644
--- a/test/functional/test_controlled_unitary.py
+++ b/test/functional/test_controlled_unitary.py
@@ -22,10 +22,12 @@
 SOFTWARE.
 """
 
-import torchquantum as tq
 from test.utils import check_all_close
+
 import numpy as np
 
+import torchquantum as tq
+
 
 def test_controlled_unitary():
     state = tq.QuantumDevice(n_wires=2)
diff --git a/test/functional/test_func_mat_exp.py b/test/functional/test_func_mat_exp.py
index e2a2c293..ad8e17c1 100644
--- a/test/functional/test_func_mat_exp.py
+++ b/test/functional/test_func_mat_exp.py
@@ -22,9 +22,10 @@
 SOFTWARE.
 """
 
+import numpy as np
 import torch
+
 import torchquantum as tq
-import numpy as np
 
 
 def test_func_mat_exp():
diff --git a/test/hadamard_grad/test_hadamard_grad.py b/test/hadamard_grad/test_hadamard_grad.py
index 62fdb21e..2eb387b8 100644
--- a/test/hadamard_grad/test_hadamard_grad.py
+++ b/test/hadamard_grad/test_hadamard_grad.py
@@ -1,7 +1,9 @@
 import numpy as np
+import pytest
+
 from examples.hadamard_grad.circ import Circ1, Circ2, Circ3
 from examples.hadamard_grad.hadamard_grad import hadamard_grad
-import pytest
+
 
 @pytest.mark.skip
 def test_hadamard_grad():
@@ -38,4 +40,4 @@ def test_hadamard_grad():
 
 
 if __name__ == "__main__":
-    test_hadamard_grad()
\ No newline at end of file
+    test_hadamard_grad()
diff --git a/test/layers/test_nlocal.py b/test/layers/test_nlocal.py
index 62387190..83bd1f6e 100644
--- a/test/layers/test_nlocal.py
+++ b/test/layers/test_nlocal.py
@@ -1,12 +1,13 @@
-import torchquantum as tq
 from qiskit.circuit.library import (
-    TwoLocal,
     EfficientSU2,
     ExcitationPreserving,
     PauliTwoDesign,
     RealAmplitudes,
+    TwoLocal,
 )
 
+import torchquantum as tq
+
 
 def compare_tq_to_qiskit(tq_circuit, qiskit_circuit, instance_info=""):
     """
@@ -16,8 +17,8 @@ def compare_tq_to_qiskit(tq_circuit, qiskit_circuit, instance_info=""):
     qiskit_ops = []
     for bit in qiskit_circuit.decompose():
         wires = []
-        for qu in bit.qubits:
-            wires.append(qu.index)
+        for qb in bit.qubits:
+            wires.append(qiskit_circuit.find_bit(qb).index)
         qiskit_ops.append(
             {
                 "name": bit.operation.name,
@@ -29,9 +30,9 @@ def compare_tq_to_qiskit(tq_circuit, qiskit_circuit, instance_info=""):
     tq_ops = [
         {
             "name": op["name"],
-            "wires": (op["wires"],)
-            if isinstance(op["wires"], int)
-            else tuple(op["wires"]),
+            "wires": (
+                (op["wires"],) if isinstance(op["wires"], int) else tuple(op["wires"])
+            ),
         }
         for op in tq_circuit.op_history
     ]
diff --git a/test/layers/test_rotgate.py b/test/layers/test_rotgate.py
index 30f24b8a..593563c7 100644
--- a/test/layers/test_rotgate.py
+++ b/test/layers/test_rotgate.py
@@ -1,14 +1,10 @@
-import torchquantum as tq
-import qiskit
-from qiskit import Aer, execute
-
-from torchquantum.util import (
-    switch_little_big_endian_matrix,
-    find_global_phase,
-)
-
-from qiskit.circuit.library import GR, GRX, GRY, GRZ
 import numpy as np
+from qiskit import transpile
+from qiskit.circuit.library import GR, GRX, GRY, GRZ
+from qiskit_aer import AerSimulator
+
+import torchquantum as tq
+from torchquantum.util import find_global_phase, switch_little_big_endian_matrix
 
 all_pairs = [
     {"qiskit": GR, "tq": tq.layer.GlobalR, "params": 2},
@@ -19,6 +15,7 @@
 
 ITERATIONS = 2
 
+
 def test_rotgates():
     # test each pair
     for pair in all_pairs:
@@ -28,15 +25,18 @@ def test_rotgates():
             for _ in range(ITERATIONS):
                 # generate random parameters
                 params = [
-                    np.random.uniform(-2 * np.pi, 2 * np.pi) for _ in range(pair["params"])
+                    np.random.uniform(-2 * np.pi, 2 * np.pi)
+                    for _ in range(pair["params"])
                 ]
 
                 # create the qiskit circuit
                 qiskit_circuit = pair["qiskit"](num_wires, *params)
 
                 # get the unitary from qiskit
-                backend = Aer.get_backend("unitary_simulator")
-                result = execute(qiskit_circuit, backend).result()
+                backend = AerSimulator(method="unitary")
+                qiskit_circuit = transpile(qiskit_circuit, backend)
+                qiskit_circuit.save_unitary()
+                result = backend.run(qiskit_circuit).result()
                 unitary_qiskit = result.get_unitary(qiskit_circuit)
 
                 # create tq circuit
diff --git a/test/measurement/test_eval_observable.py b/test/measurement/test_eval_observable.py
index 58245ee0..499c2ad1 100644
--- a/test/measurement/test_eval_observable.py
+++ b/test/measurement/test_eval_observable.py
@@ -22,19 +22,21 @@
 SOFTWARE.
 """
 
-from qiskit import QuantumCircuit
-import numpy as np
 import random
-from qiskit.opflow import StateFn, X, Y, Z, I
 
-import torchquantum as tq
+import numpy as np
+from qiskit import QuantumCircuit
+from qiskit.quantum_info import Pauli, Statevector
 
+import torchquantum as tq
 from torchquantum.measurement import expval_joint_analytical, expval_joint_sampling
 from torchquantum.plugin import op_history2qiskit
 from torchquantum.util import switch_little_big_endian_state
 
-import torch
-
+X = Pauli("X")
+Y = Pauli("Y")
+Z = Pauli("Z")
+I = Pauli("I")
 pauli_str_op_dict = {
     "X": X,
     "Y": Y,
@@ -67,20 +69,19 @@ def test_expval_observable():
         for ob in obs[1:]:
             # note here the order is reversed because qiskit is in little endian
             operator = pauli_str_op_dict[ob] ^ operator
-        psi = StateFn(qiskit_circ)
-        psi_evaled = psi.eval()._primitive._data
+        psi = Statevector(qiskit_circ)
         state_tq = switch_little_big_endian_state(
             qdev.get_states_1d().detach().numpy()
         )[0]
-        assert np.allclose(psi_evaled, state_tq, atol=1e-5)
+        assert np.allclose(psi.data, state_tq, atol=1e-5)
 
-        expval_qiskit = (~psi @ operator @ psi).eval().real
+        expval_qiskit = psi.expectation_value(operator).real
         # print(expval_tq, expval_qiskit)
         assert np.isclose(expval_tq, expval_qiskit, atol=1e-5)
         if (
             n_wires <= 3
         ):  # if too many wires, the stochastic method is not accurate due to limited shots
-            assert np.isclose(expval_tq_sampling, expval_qiskit, atol=1e-2)
+            assert np.isclose(expval_tq_sampling, expval_qiskit, atol=0.015)
 
     print("expval observable test passed")
 
@@ -92,25 +93,25 @@ def util0():
     qc.x(0)
 
     operator = Z ^ I
-    psi = StateFn(qc)
-    expectation_value = (~psi @ operator @ psi).eval()
+    psi = Statevector(qc)
+    expectation_value = psi.expectation_value(operator)
     print(expectation_value.real)
     # result: 1.0, means measurement result is 0, so Z is on qubit 1
 
     operator = I ^ Z
-    psi = StateFn(qc)
-    expectation_value = (~psi @ operator @ psi).eval()
+    psi = Statevector(qc)
+    expectation_value = psi.expectation_value(operator)
     print(expectation_value.real)
     # result: -1.0 means measurement result is 1, so Z is on qubit 0
 
     operator = I ^ I
-    psi = StateFn(qc)
-    expectation_value = (~psi @ operator @ psi).eval()
+    psi = Statevector(qc)
+    expectation_value = psi.expectation_value(operator)
     print(expectation_value.real)
 
     operator = Z ^ Z
-    psi = StateFn(qc)
-    expectation_value = (~psi @ operator @ psi).eval()
+    psi = Statevector(qc)
+    expectation_value = psi.expectation_value(operator)
     print(expectation_value.real)
 
     qc = QuantumCircuit(3)
@@ -118,8 +119,8 @@ def util0():
     qc.x(0)
 
     operator = I ^ I ^ Z
-    psi = StateFn(qc)
-    expectation_value = (~psi @ operator @ psi).eval()
+    psi = Statevector(qc)
+    expectation_value = psi.expectation_value(operator)
     print(expectation_value.real)
 
 
diff --git a/test/measurement/test_expval_joint_sampling_grouping.py b/test/measurement/test_expval_joint_sampling_grouping.py
index 09492458..8a759518 100644
--- a/test/measurement/test_expval_joint_sampling_grouping.py
+++ b/test/measurement/test_expval_joint_sampling_grouping.py
@@ -22,15 +22,16 @@
 SOFTWARE.
 """
 
+import random
+
+import numpy as np
+
 import torchquantum as tq
 from torchquantum.measurement import (
     expval_joint_analytical,
     expval_joint_sampling_grouping,
 )
 
-import numpy as np
-import random
-
 
 def test_expval_joint_sampling_grouping():
     n_obs = 20
@@ -54,7 +55,7 @@ def test_expval_joint_sampling_grouping():
     )
     for obs in obs_all:
         # assert
-        assert np.isclose(expval_ana[obs], expval_sam[obs][0].item(), atol=1e-2)
+        assert np.isclose(expval_ana[obs], expval_sam[obs][0].item(), atol=0.015)
         print(obs, expval_ana[obs], expval_sam[obs][0].item())
 
 
diff --git a/test/measurement/test_measure.py b/test/measurement/test_measure.py
index 38c45df6..5fafa180 100644
--- a/test/measurement/test_measure.py
+++ b/test/measurement/test_measure.py
@@ -22,11 +22,12 @@
 SOFTWARE.
 """
 
-import torchquantum as tq
+import numpy as np
+from qiskit import transpile
+from qiskit_aer import AerSimulator
 
+import torchquantum as tq
 from torchquantum.plugin import op_history2qiskit
-from qiskit import Aer, transpile
-import numpy as np
 
 
 def test_measure():
@@ -42,7 +43,7 @@ def test_measure():
 
     circ = op_history2qiskit(qdev.n_wires, qdev.op_history)
     circ.measure_all()
-    simulator = Aer.get_backend("aer_simulator")
+    simulator = AerSimulator()
     circ = transpile(circ, simulator)
     qiskit_res = simulator.run(circ, shots=n_shots).result()
     qiskit_counts = qiskit_res.get_counts()
diff --git a/test/operator/test_ControlledU.py b/test/operator/test_ControlledU.py
index 5bc01096..e80dee1d 100644
--- a/test/operator/test_ControlledU.py
+++ b/test/operator/test_ControlledU.py
@@ -25,14 +25,13 @@
 # test the controlled unitary function
 
 
-import torchquantum as tq
-import torchquantum.functional as tqf
 from test.utils import check_all_close
 
 # import pdb
 # pdb.set_trace()
 import numpy as np
 
+import torchquantum as tq
 
 flag = 4
 
diff --git a/test/plugin/test_qiskit2tq.py b/test/plugin/test_qiskit2tq.py
new file mode 100644
index 00000000..23427423
--- /dev/null
+++ b/test/plugin/test_qiskit2tq.py
@@ -0,0 +1,174 @@
+"""
+MIT License
+
+Copyright (c) 2020-present TorchQuantum Authors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+"""
+
+import random
+
+import numpy as np
+import pytest
+import torch
+import torch.optim as optim
+from qiskit import QuantumCircuit
+from qiskit.circuit import Parameter, ParameterVector
+from torch.optim.lr_scheduler import CosineAnnealingLR
+
+import torchquantum as tq
+from torchquantum.plugin import qiskit2tq
+
+seed = 42
+random.seed(seed)
+np.random.seed(seed)
+torch.manual_seed(seed)
+
+
+class TQModel(tq.QuantumModule):
+    def __init__(self, init_params=None):
+        super().__init__()
+        self.n_wires = 2
+        self.rx = tq.RX(has_params=True, trainable=True, init_params=[init_params[0]])
+        self.u3_0 = tq.U3(has_params=True, trainable=True, init_params=init_params[1:4])
+        self.u3_1 = tq.U3(
+            has_params=True,
+            trainable=True,
+            init_params=torch.tensor(
+                [
+                    init_params[4] + init_params[2],
+                    init_params[5] * init_params[3],
+                    init_params[6] * init_params[1],
+                ]
+            ),
+        )
+        self.cu3_0 = tq.CU3(
+            has_params=True,
+            trainable=True,
+            init_params=torch.tensor(
+                [
+                    torch.sin(init_params[7]),
+                    torch.abs(torch.sin(init_params[8])),
+                    torch.abs(torch.sin(init_params[9]))
+                    * torch.exp(init_params[2] + init_params[3]),
+                ]
+            ),
+        )
+
+    def forward(self, q_device: tq.QuantumDevice):
+        q_device.reset_states(1)
+        self.rx(q_device, wires=0)
+        self.u3_0(q_device, wires=0)
+        self.u3_1(q_device, wires=1)
+        self.cu3_0(q_device, wires=[0, 1])
+
+
+def get_qiskit_ansatz():
+    ansatz = QuantumCircuit(2)
+    ansatz_param = Parameter("Θ")  # parameter
+    ansatz.rx(ansatz_param, 0)
+    ansatz_param_vector = ParameterVector("φ", 9)  # parameter vector
+    ansatz.u(ansatz_param_vector[0], ansatz_param_vector[1], ansatz_param_vector[2], 0)
+    ansatz.u(
+        ansatz_param_vector[3] + ansatz_param_vector[1],  # parameter expression
+        ansatz_param_vector[4] * ansatz_param_vector[2],
+        ansatz_param_vector[5] / ansatz_param_vector[0],
+        1,
+    )
+    ansatz.cu(
+        np.sin(ansatz_param_vector[6]),  # numpy functions
+        np.abs(np.sin(ansatz_param_vector[7])),  # nested numpy functions
+        # complex expression
+        np.abs(np.sin(ansatz_param_vector[8]))
+        * np.exp(ansatz_param_vector[1] + ansatz_param_vector[2]),
+        0.0,
+        0,
+        1,
+    )
+    return ansatz
+
+
+def train_step(target_state, device, model, optimizer):
+    model(device)
+    result_state = device.get_states_1d()[0]
+
+    # compute the state infidelity
+    loss = 1 - torch.dot(result_state, target_state).abs() ** 2
+
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+
+    infidelity = loss.item()
+    target_state_vector = target_state.detach().cpu().numpy()
+    result_state_vector = result_state.detach().cpu().numpy()
+    print(
+        f"infidelity (loss): {infidelity}, \n target state : "
+        f"{target_state_vector}, \n "
+        f"result state : {result_state_vector}\n"
+    )
+    return infidelity, target_state_vector, result_state_vector
+
+
+def train(init_params, backend):
+    device = torch.device("cpu")
+
+    if backend == "qiskit":
+        ansatz = get_qiskit_ansatz()
+        model = qiskit2tq(ansatz, initial_parameters=init_params).to(device)
+    elif backend == "torchquantum":
+        model = TQModel(init_params).to(device)
+
+    print(f"{backend} model:", model)
+
+    n_epochs = 10
+    optimizer = optim.Adam(model.parameters(), lr=1e-2, weight_decay=0)
+    scheduler = CosineAnnealingLR(optimizer, T_max=n_epochs)
+
+    q_device = tq.QuantumDevice(n_wires=2)
+    target_state = torch.tensor([0, 1, 0, 0], dtype=torch.complex64)
+
+    result_list = []
+    for epoch in range(1, n_epochs + 1):
+        print(f"Epoch {epoch}, LR: {optimizer.param_groups[0]['lr']}")
+        result_list.append(train_step(target_state, q_device, model, optimizer))
+        scheduler.step()
+
+    return result_list
+
+
+@pytest.mark.parametrize(
+    "init_params",
+    [
+        torch.nn.init.uniform_(torch.ones(10), -np.pi, np.pi),
+        torch.nn.init.uniform_(torch.ones(10), -np.pi, np.pi),
+        torch.nn.init.uniform_(torch.ones(10), -np.pi, np.pi),
+    ],
+)
+def test_qiskit2tq(init_params):
+    qiskit_result = train(init_params, "qiskit")
+    tq_result = train(init_params, "torchquantum")
+    for qi_tensor, tq_tensor in zip(qiskit_result, tq_result):
+        torch.testing.assert_close(qi_tensor[0], tq_tensor[0])
+        torch.testing.assert_close(qi_tensor[1], tq_tensor[1])
+        torch.testing.assert_close(qi_tensor[2], tq_tensor[2])
+
+
+if __name__ == "__main__":
+    test_qiskit2tq(torch.nn.init.uniform_(torch.ones(10), -np.pi, np.pi))
diff --git a/test/plugin/test_qiskit2tq_op_history.py b/test/plugin/test_qiskit2tq_op_history.py
index 67a67e80..b94fb7fd 100644
--- a/test/plugin/test_qiskit2tq_op_history.py
+++ b/test/plugin/test_qiskit2tq_op_history.py
@@ -22,11 +22,11 @@
 SOFTWARE.
 """
 
-from torchquantum.plugin import qiskit2tq_op_history
-import torchquantum as tq
-from qiskit.circuit.random import random_circuit
 from qiskit import QuantumCircuit
 
+import torchquantum as tq
+from torchquantum.plugin import qiskit2tq_op_history
+
 
 def test_qiskit2tp_op_history():
     circ = QuantumCircuit(3, 3)
diff --git a/test/plugin/test_qiskit_plugins.py b/test/plugin/test_qiskit_plugins.py
index 684dbfc6..76d0a4db 100644
--- a/test/plugin/test_qiskit_plugins.py
+++ b/test/plugin/test_qiskit_plugins.py
@@ -22,26 +22,24 @@
 SOFTWARE.
 """
 
-from qiskit import QuantumCircuit
-import numpy as np
 import random
-from qiskit.opflow import StateFn, X, Y, Z, I
 
-import torchquantum as tq
+import numpy as np
+import pytest
+from qiskit.quantum_info import Pauli, Statevector
 
-from torchquantum.plugin import op_history2qiskit, QiskitProcessor
+import torchquantum as tq
+from torchquantum.plugin import QiskitProcessor, op_history2qiskit
 from torchquantum.util import switch_little_big_endian_state
 
-import torch
-import pytest
-
 pauli_str_op_dict = {
-    "X": X,
-    "Y": Y,
-    "Z": Z,
-    "I": I,
+    "X": Pauli("X"),
+    "Y": Pauli("Y"),
+    "Z": Pauli("Z"),
+    "I": Pauli("I"),
 }
 
+
 @pytest.mark.skip
 def test_expval_observable():
     # seed = 0
@@ -67,19 +65,18 @@ def test_expval_observable():
         for ob in obs[1:]:
             # note here the order is reversed because qiskit is in little endian
             operator = pauli_str_op_dict[ob] ^ operator
-        psi = StateFn(qiskit_circ)
-        psi_evaled = psi.eval()._primitive._data
+        psi = Statevector(qiskit_circ)
         state_tq = switch_little_big_endian_state(
             qdev.get_states_1d().detach().numpy()
         )[0]
-        assert np.allclose(psi_evaled, state_tq, atol=1e-5)
+        assert np.allclose(psi.data, state_tq, atol=1e-5)
 
-        expval_qiskit = (~psi @ operator @ psi).eval().real
+        expval_qiskit = psi.expectation_value(operator).real
         # print(expval_qiskit_processor, expval_qiskit)
         if (
             n_wires <= 3
         ):  # if too many wires, the stochastic method is not accurate due to limited shots
-            assert np.isclose(expval_qiskit_processor, expval_qiskit, atol=1e-2)
+            assert np.isclose(expval_qiskit_processor, expval_qiskit, atol=0.015)
 
     print("expval observable test passed")
 
diff --git a/test/qiskit_plugin_test.py b/test/qiskit_plugin_test.py
index d8b7e94b..a5aed71a 100644
--- a/test/qiskit_plugin_test.py
+++ b/test/qiskit_plugin_test.py
@@ -24,21 +24,22 @@
 
 import argparse
 import pdb
-import torch
-import torchquantum as tq
-import numpy as np
+from test.static_mode_test import QLayer as AllRandomLayer
 
-from qiskit import Aer, execute
+import numpy as np
+import torch
+from qiskit_aer import AerSimulator
 from torchpack.utils.logging import logger
+
+import torchquantum as tq
+from torchquantum.macro import F_DTYPE
+from torchquantum.plugin import tq2qiskit
 from torchquantum.util import (
+    find_global_phase,
+    get_expectations_from_counts,
     switch_little_big_endian_matrix,
     switch_little_big_endian_state,
-    get_expectations_from_counts,
-    find_global_phase,
 )
-from test.static_mode_test import QLayer as AllRandomLayer
-from torchquantum.plugin import tq2qiskit
-from torchquantum.macro import F_DTYPE
 
 
 def unitary_tq_vs_qiskit_test():
@@ -59,8 +60,9 @@ def unitary_tq_vs_qiskit_test():
 
         # qiskit
         circ = tq2qiskit(q_layer, x)
-        simulator = Aer.get_backend("unitary_simulator")
-        result = execute(circ, simulator).result()
+        simulator = AerSimulator(method="unitary")
+        circ.save_unitary()
+        result = simulator.run(circ).result()
         unitary_qiskit = result.get_unitary(circ)
 
         stable_threshold = 1e-5
@@ -115,10 +117,11 @@ def state_tq_vs_qiskit_test():
         # qiskit
         circ = tq2qiskit(q_layer, x)
         # Select the StatevectorSimulator from the Aer provider
-        simulator = Aer.get_backend("statevector_simulator")
+        simulator = AerSimulator(method="statevector")
+        circ.save_statevector()
 
         # Execute and get counts
-        result = execute(circ, simulator).result()
+        result = simulator.run(circ).result()
         state_qiskit = result.get_statevector(circ)
 
         stable_threshold = 1e-5
@@ -175,11 +178,10 @@ def measurement_tq_vs_qiskit_test():
         circ = tq2qiskit(q_layer, x)
         circ.measure(list(range(n_wires)), list(range(n_wires)))
 
-        # Select the QasmSimulator from the Aer provider
-        simulator = Aer.get_backend("qasm_simulator")
+        simulator = AerSimulator()
 
         # Execute and get counts
-        result = execute(circ, simulator, shots=1000000).result()
+        result = simulator.run(circ, shots=1000000).result()
         counts = result.get_counts(circ)
         measured_qiskit = get_expectations_from_counts(counts, n_wires=n_wires)
 
diff --git a/torchquantum/algorithm/__init__.py b/torchquantum/algorithm/__init__.py
index 7dfb672a..c7413a2e 100644
--- a/torchquantum/algorithm/__init__.py
+++ b/torchquantum/algorithm/__init__.py
@@ -22,7 +22,8 @@
 SOFTWARE.
 """
 
-from .vqe import *
-from .hamiltonian import *
-from .qft import *
-from .grover import *
+from .vqe import VQE
+from .hamiltonian import Hamiltonian
+from .qft import QFT
+from .grover import Grover
+from .qcbm import QCBM, MMDLoss
diff --git a/torchquantum/algorithm/qcbm.py b/torchquantum/algorithm/qcbm.py
new file mode 100644
index 00000000..35a6fb75
--- /dev/null
+++ b/torchquantum/algorithm/qcbm.py
@@ -0,0 +1,96 @@
+import torch
+import torch.nn as nn
+
+import torchquantum as tq
+
+__all__ = ["QCBM", "MMDLoss"]
+
+
+class MMDLoss(nn.Module):
+    """Squared maximum mean discrepancy with radial basis function kerne"""
+
+    def __init__(self, scales, space):
+        """
+        Initialize MMDLoss object. Calculates and stores the kernel matrix.
+
+        Args:
+            scales: Bandwidth parameters.
+            space: Basis input space.
+        """
+        super().__init__()
+
+        gammas = 1 / (2 * (scales**2))
+
+        # squared Euclidean distance
+        sq_dists = torch.abs(space[:, None] - space[None, :]) ** 2
+
+        # Kernel matrix
+        self.K = sum(torch.exp(-gamma * sq_dists) for gamma in gammas) / len(scales)
+        self.scales = scales
+
+    def k_expval(self, px, py):
+        """
+        Kernel expectation value
+
+        Args:
+            px: First probability distribution
+            py: Second probability distribution
+
+        Returns:
+            Expectation value of the RBF Kernel.
+        """
+
+        return px @ self.K @ py
+
+    def forward(self, px, py):
+        """
+        Squared MMD loss.
+
+        Args:
+            px: First probability distribution
+            py: Second probability distribution
+
+        Returns:
+            Squared MMD loss.
+        """
+        pxy = px - py
+        return self.k_expval(pxy, pxy)
+
+
+class QCBM(nn.Module):
+    """
+    Quantum Circuit Born Machine (QCBM)
+
+    Attributes:
+        ansatz: An Ansatz object
+        n_wires: Number of wires in the ansatz used.
+
+    Methods:
+        __init__: Initialize the QCBM object.
+        forward: Returns the probability distribution (output from measurement).
+    """
+
+    def __init__(self, n_wires, ansatz):
+        """
+        Initialize QCBM object
+
+        Args:
+            ansatz (Ansatz): An Ansatz object
+            n_wires (int): Number of wires in the ansatz used.
+        """
+        super().__init__()
+
+        self.ansatz = ansatz
+        self.n_wires = n_wires
+
+    def forward(self):
+        """
+        Execute and obtain the probability distribution
+
+        Returns:
+            Probabilities (torch.Tensor)
+        """
+        qdev = tq.QuantumDevice(n_wires=self.n_wires, bsz=1, device="cpu")
+        self.ansatz(qdev)
+        probs = torch.abs(qdev.states.flatten()) ** 2
+        return probs
diff --git a/torchquantum/density/density_func.py b/torchquantum/density/density_func.py
index fb15bf3d..5bde99a8 100644
--- a/torchquantum/density/density_func.py
+++ b/torchquantum/density/density_func.py
@@ -217,7 +217,7 @@ def apply_unitary_density_bmm(density, mat, wires):
     permute_to_dag = permute_to_dag + devices_dims_dag
     permute_back_dag = list(np.argsort(permute_to_dag))
     original_shape = new_density.shape
-    permuted_dag = new_density.permute(permute_to_dag).reshape([original_shape[0], -1, matdag.shape[0]])
+    permuted_dag = new_density.permute(permute_to_dag).reshape([original_shape[0], -1, matdag.shape[-1]])
 
     if len(matdag.shape) > 2:
         # both matrix and state are in batch mode
diff --git a/torchquantum/density/density_mat.py b/torchquantum/density/density_mat.py
index 8260a01b..1bf406ea 100644
--- a/torchquantum/density/density_mat.py
+++ b/torchquantum/density/density_mat.py
@@ -126,6 +126,12 @@ def print_2d(self, index):
         _matrix = torch.reshape(self._matrix[index], [2 ** self.n_wires] * 2)
         print(_matrix)
 
+        
+    def get_2d_matrix(self, index):
+        _matrix = torch.reshape(self._matrix[index], [2 ** self.n_wires] * 2)
+        return _matrix
+
+
     def trace(self, index):
         """Calculate and return the trace of the density matrix at the given index.
 
diff --git a/torchquantum/device/noisedevices.py b/torchquantum/device/noisedevices.py
index 3da88eff..dded7a4d 100644
--- a/torchquantum/device/noisedevices.py
+++ b/torchquantum/device/noisedevices.py
@@ -30,17 +30,37 @@
 from torchquantum.functional import func_name_dict, func_name_dict_collect
 from typing import Union
 
-__all__ = ["NoiseDevice"]
+__all__ = ["NoiseDevice", "NoiseModel"]
+
+
+class NoiseModel:
+    ''
+
+    def __init__(self,
+                 kraus_dict
+                 ):
+        """A quantum noise model
+        Args:
+            kraus_dict: the karus_dict for this noise_model.
+            For example:
+                 kraus_dict={"Bitflip":0.5, "Phaseflip":0.5}
+        """
+        self._kraus_dict = kraus_dict
+        # TODO: Check that the trace is preserved
+
+    def kraus_dict(self):
+        return self._kraus_dict
 
 
 class NoiseDevice(nn.Module):
     def __init__(
-        self,
-        n_wires: int,
-        device_name: str = "noisedevice",
-        bsz: int = 1,
-        device: Union[torch.device, str] = "cpu",
-        record_op: bool = False,
+            self,
+            n_wires: int,
+            device_name: str = "noisedevice",
+            bsz: int = 1,
+            device: Union[torch.device, str] = "cpu",
+            record_op: bool = False,
+            noise_model: NoiseModel = NoiseModel(kraus_dict={"Bitflip": 0, "Phaseflip": 0})
     ):
         """A quantum device that support the density matrix simulation
         Args:
@@ -73,6 +93,50 @@ def __init__(
         self.record_op = record_op
         self.op_history = []
 
+        self._noise_model = noise_model
+
+    def reset_op_history(self):
+        """Resets the all Operation of the quantum device"""
+        self.op_history = []
+
+    def print_2d(self, index):
+        """Print the matrix value at the given index.
+
+        This method prints the matrix value of `matrix[index]`. It reshapes the value into a 2D matrix
+        using the `torch.reshape` function and then prints it.
+
+        Args:
+            index (int): The index of the matrix value to print.
+
+        Examples:
+            >>> device = QuantumDevice(n_wires=2)
+            >>> device.matrix = torch.tensor([[1, 0, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 0, 0, 1]])
+            >>> device.print_2d(1)
+            tensor([[0, 0],
+                    [0, 1]])
+
+        """
+
+        _matrix = torch.reshape(self.densities[index], [2 ** self.n_wires] * 2)
+        print(_matrix)
+
+    def get_2d_matrix(self, index):
+        _matrix = torch.reshape(self.densities[index], [2 ** self.n_wires] * 2)
+        return _matrix
+
+    def get_densities_2d(self):
+        """Return the states in a 1d tensor."""
+        bsz = self.densities.shape[0]
+        return torch.reshape(self.densities, [bsz, 2 ** self.n_wires, 2 ** self.n_wires])
+
+    def get_density_2d(self):
+        """Return the state in a 1d tensor."""
+        return torch.reshape(self.density, [2 ** self.n_wires, 2 ** self.n_wires])
+
+    def calc_trace(self, index):
+        _matrix = torch.reshape(self.densities[index], [2 ** self.n_wires] * 2)
+        return torch.trace(_matrix)
+
     @property
     def name(self):
         """Return the name of the device."""
@@ -81,21 +145,41 @@ def name(self):
     def __repr__(self):
         return f" class: {self.name} \n device name: {self.device_name} \n number of qubits: {self.n_wires} \n batch size: {self.bsz} \n current computing device: {self.density.device} \n recording op history: {self.record_op} \n current states: {repr(self.get_probs_1d().cpu().detach().numpy())}"
 
-
     '''
     Get the probability of measuring each state to a one dimension
     tensor
     '''
+
     def get_probs_1d(self):
         """Return the states in a 1d tensor."""
         bsz = self.densities.shape[0]
-        densities2d=torch.reshape(self.densities, [bsz, 2**self.n_wires,2**self.n_wires])
-        return torch.diagonal(densities2d, offset=0, dim1=1, dim2=2)
+        densities2d = torch.reshape(self.densities, [bsz, 2 ** self.n_wires, 2 ** self.n_wires])
+        return torch.abs(torch.diagonal(densities2d, offset=0, dim1=1, dim2=2))
 
     def get_prob_1d(self):
         """Return the state in a 1d tensor."""
-        density2d=torch.reshape(self.density, [2**self.n_wires,2**self.n_wires])
-        return torch.diagonal(density2d, offset=0, dim1=0, dim2=1)
+        density2d = torch.reshape(self.density, [2 ** self.n_wires, 2 ** self.n_wires])
+        return torch.abs(torch.diagonal(density2d, offset=0, dim1=0, dim2=1))
+
+    def clone_densities(self, existing_densities: torch.Tensor):
+        """Clone the densities of the other quantum device."""
+        self.densities = existing_densities.clone()
+
+    def clone_from_states(self, existing_states: torch.Tensor):
+        """Clone the densities of the other quantum device using the conjugate transpose."""
+        # Ensure the dimensions match the expected shape for the outer product operation
+        assert 2 * (existing_states.dim() - 1) == (self.densities.dim() - 1)
+        #assert existing_states.shape[0] == self.densities.shape[0]
+        bsz = existing_states.shape[0]
+        state_dim = 2 ** self.n_wires
+        states_reshaped = existing_states.view(-1, state_dim, 1)  # [batch_size, state_dim, 1]
+        states_conj_transpose = torch.conj(states_reshaped).transpose(1, 2)  # [batch_size, 1, state_dim]
+        # Use torch.bmm for batched outer product
+        self.densities = torch.bmm(states_reshaped, states_conj_transpose)
+        self.densities = torch.reshape(self.densities, [bsz] + [2] * (2 * self.n_wires))
+
+    def noise_model(self):
+        return self._noise_model
 
 
 for func_name, func in func_name_dict.items():
diff --git a/torchquantum/encoding/encodings.py b/torchquantum/encoding/encodings.py
index f8d2056d..9685aec4 100644
--- a/torchquantum/encoding/encodings.py
+++ b/torchquantum/encoding/encodings.py
@@ -39,6 +39,7 @@ class Encoder(tq.QuantumModule):
         - forward(qdev: tq.QuantumDevice, x): Performs the encoding using a quantum device.
 
     """
+
     def __init__(self):
         super().__init__()
         pass
@@ -80,6 +81,18 @@ class GeneralEncoder(Encoder, metaclass=ABCMeta):
     {'input_idx': [12, 13, 14], 'func': 'u3', 'wires': [3]},
     {'input_idx': [15], 'func': 'u1', 'wires': [3]},
     ]
+
+    Example 3:
+    [
+    {'params': [0.25], 'func': 'rx', 'wires': [0]},
+    {'params': [0.25], 'func': 'rx', 'wires': [1]},
+    {'params': [0.25], 'func': 'rx', 'wires': [2]},
+    {'params': [0.25], 'func': 'rx', 'wires': [3]},
+    {'input_idx': [0], 'func': 'ry', 'wires': [0]},
+    {'input_idx': [1], 'func': 'ry', 'wires': [1]},
+    {'input_idx': [2], 'func': 'ry', 'wires': [2]},
+    {'input_idx': [3], 'func': 'ry', 'wires': [3]}
+    ]
     """
 
     def __init__(self, func_list):
@@ -90,7 +103,11 @@ def __init__(self, func_list):
     def forward(self, qdev: tq.QuantumDevice, x):
         for info in self.func_list:
             if tq.op_name_dict[info["func"]].num_params > 0:
-                params = x[:, info["input_idx"]]
+                # If params are provided in encoder, use those,
+                #  else use params from x
+                params = (torch.Tensor(info["params"]).repeat(x.shape[0], 1)
+                          if info.get("params")
+                          else x[:, info["input_idx"]])
             else:
                 params = None
             func_name_dict[info["func"]](
@@ -133,6 +150,7 @@ def to_qiskit(self, n_wires, x):
 class PhaseEncoder(Encoder, metaclass=ABCMeta):
     """PhaseEncoder is a subclass of Encoder and represents a phase encoder.
     It applies a specified quantum function to encode input data using a quantum device."""
+
     def __init__(self, func):
         super().__init__()
         self.func = func
@@ -163,6 +181,7 @@ def forward(self, qdev: tq.QuantumDevice, x):
 class MultiPhaseEncoder(Encoder, metaclass=ABCMeta):
     """PhaseEncoder is a subclass of Encoder and represents a phase encoder.
     It applies a specified quantum function to encode input data using a quantum device."""
+
     def __init__(self, funcs, wires=None):
         super().__init__()
         self.funcs = funcs if isinstance(funcs, Iterable) else [funcs]
@@ -198,7 +217,7 @@ def forward(self, qdev: tq.QuantumDevice, x):
             func_name_dict[func](
                 qdev,
                 wires=self.wires[k],
-                params=x[:, x_id : (x_id + stride)],
+                params=x[:, x_id: (x_id + stride)],
                 static=self.static_mode,
                 parent_graph=self.graph,
             )
@@ -208,6 +227,7 @@ def forward(self, qdev: tq.QuantumDevice, x):
 class StateEncoder(Encoder, metaclass=ABCMeta):
     """StateEncoder is a subclass of Encoder and represents a state encoder.
     It encodes the input data into the state vector of a quantum device."""
+
     def __init__(self):
         super().__init__()
 
@@ -230,19 +250,24 @@ def forward(self, qdev: tq.QuantumDevice, x):
             (
                 x,
                 torch.zeros(
-                    x.shape[0], 2**qdev.n_wires - x.shape[1], device=x.device
+                    x.shape[0], 2 ** qdev.n_wires - x.shape[1], device=x.device
                 ),
             ),
             dim=-1,
         )
         state = state.view([x.shape[0]] + [2] * qdev.n_wires)
 
-        qdev.states = state.type(C_DTYPE)
+        #TODO: Change to united format
+        if qdev.device_name == "noisedevice":
+            qdev.clone_from_states(state.type(C_DTYPE))
+        else:
+            qdev.states = state.type(C_DTYPE)
 
 
 class MagnitudeEncoder(Encoder, metaclass=ABCMeta):
     """MagnitudeEncoder is a subclass of Encoder and represents a magnitude encoder.
     It encodes the input data by considering the magnitudes of the elements."""
+
     def __init__(self):
         super().__init__()
 
diff --git a/torchquantum/functional/func_controlled_unitary.py b/torchquantum/functional/func_controlled_unitary.py
index dc909815..f5d745c0 100644
--- a/torchquantum/functional/func_controlled_unitary.py
+++ b/torchquantum/functional/func_controlled_unitary.py
@@ -24,8 +24,9 @@
 
 import numpy as np
 import torch
+
 from torchquantum.functional.gate_wrapper import gate_wrapper
-from torchquantum.macro import *
+from torchquantum.macro import C_DTYPE
 
 
 def controlled_unitary(
@@ -97,7 +98,7 @@ def controlled_unitary(
     n_wires = n_c_wires + n_t_wires
 
     # compute the new unitary, then permute
-    unitary = torch.tensor(torch.zeros(2**n_wires, 2**n_wires, dtype=C_DTYPE))
+    unitary = torch.zeros(2**n_wires, 2**n_wires, dtype=C_DTYPE)
     for k in range(2**n_wires - 2**n_t_wires):
         unitary[k, k] = 1.0 + 0.0j
 
diff --git a/torchquantum/functional/gate_wrapper.py b/torchquantum/functional/gate_wrapper.py
index f1383f2f..cab7379f 100644
--- a/torchquantum/functional/gate_wrapper.py
+++ b/torchquantum/functional/gate_wrapper.py
@@ -1,16 +1,13 @@
 import functools
-import torch
-import numpy as np
+from typing import TYPE_CHECKING, Callable
 
-from typing import Callable, Union, Optional, List, Dict, TYPE_CHECKING
-from ..macro import C_DTYPE, F_DTYPE, ABC, ABC_ARRAY, INV_SQRT2
-from ..util.utils import pauli_eigs, diag
-from torchpack.utils.logging import logger
-from torchquantum.util import normalize_statevector
+import numpy as np
+import torch
 
+from ..macro import ABC, ABC_ARRAY, C_DTYPE, F_DTYPE
 
 if TYPE_CHECKING:
-    from torchquantum.device import QuantumDevice, NoiseDevice
+    from torchquantum.device import QuantumDevice
 else:
     QuantumDevice = None
 
@@ -59,7 +56,7 @@ def apply_unitary_einsum(state, mat, wires):
 
     # All affected indices will be summed over, so we need the same number
     # of new indices
-    new_indices = ABC[total_wires: total_wires + len(device_wires)]
+    new_indices = ABC[total_wires : total_wires + len(device_wires)]
 
     # The new indices of the state are given by the old ones with the
     # affected indices replaced by the new_indices
@@ -181,16 +178,13 @@ def apply_unitary_density_einsum(density, mat, wires):
 
     # Tensor indices of the quantum state
     density_indices = ABC[:total_wires]
-    print("density_indices", density_indices)
 
     # Indices of the quantum state affected by this operation
     affected_indices = "".join(ABC_ARRAY[list(device_wires)].tolist())
-    print("affected_indices", affected_indices)
 
     # All affected indices will be summed over, so we need the same number
     # of new indices
-    new_indices = ABC[total_wires: total_wires + len(device_wires)]
-    print("new_indices", new_indices)
+    new_indices = ABC[total_wires : total_wires + len(device_wires)]
 
     # The new indices of the state are given by the old ones with the
     # affected indices replaced by the new_indices
@@ -199,7 +193,6 @@ def apply_unitary_density_einsum(density, mat, wires):
         zip(affected_indices, new_indices),
         density_indices,
     )
-    print("new_density_indices", new_density_indices)
 
     # Use the last literal as the indice of batch
     density_indices = ABC[-1] + density_indices
@@ -212,29 +205,24 @@ def apply_unitary_density_einsum(density, mat, wires):
     einsum_indices = (
         f"{new_indices}{affected_indices}," f"{density_indices}->{new_density_indices}"
     )
-    print("einsum_indices", einsum_indices)
 
     new_density = torch.einsum(einsum_indices, mat, density)
 
-    """
+    r"""
     Compute U \rho U^\dagger
     """
-    print("dagger")
 
     # Tensor indices of the quantum state
     density_indices = ABC[:total_wires]
-    print("density_indices", density_indices)
 
     # Indices of the quantum state affected by this operation
     affected_indices = "".join(
         ABC_ARRAY[[x + n_qubit for x in list(device_wires)]].tolist()
     )
-    print("affected_indices", affected_indices)
 
     # All affected indices will be summed over, so we need the same number
     # of new indices
-    new_indices = ABC[total_wires: total_wires + len(device_wires)]
-    print("new_indices", new_indices)
+    new_indices = ABC[total_wires : total_wires + len(device_wires)]
 
     # The new indices of the state are given by the old ones with the
     # affected indices replaced by the new_indices
@@ -243,7 +231,6 @@ def apply_unitary_density_einsum(density, mat, wires):
         zip(affected_indices, new_indices),
         density_indices,
     )
-    print("new_density_indices", new_density_indices)
 
     density_indices = ABC[-1] + density_indices
     new_density_indices = ABC[-1] + new_density_indices
@@ -255,7 +242,6 @@ def apply_unitary_density_einsum(density, mat, wires):
     einsum_indices = (
         f"{density_indices}," f"{affected_indices}{new_indices}->{new_density_indices}"
     )
-    print("einsum_indices", einsum_indices)
 
     new_density = torch.einsum(einsum_indices, density, matdag)
 
@@ -274,6 +260,7 @@ def apply_unitary_density_bmm(density, mat, wires):
     device_wires = wires
     n_qubit = density.dim() // 2
     mat = mat.type(C_DTYPE).to(density.device)
+
     """
     Compute U \rho
     """
@@ -284,7 +271,9 @@ def apply_unitary_density_bmm(density, mat, wires):
     permute_to = permute_to[:1] + devices_dims + permute_to[1:]
     permute_back = list(np.argsort(permute_to))
     original_shape = density.shape
-    permuted = density.permute(permute_to).reshape([original_shape[0], mat.shape[-1], -1])
+    permuted = density.permute(permute_to).reshape(
+        [original_shape[0], mat.shape[-1], -1]
+    )
 
     if len(mat.shape) > 2:
         # both matrix and state are in batch mode
@@ -295,10 +284,16 @@ def apply_unitary_density_bmm(density, mat, wires):
         expand_shape = [bsz] + list(mat.shape)
         new_density = mat.expand(expand_shape).bmm(permuted)
     new_density = new_density.view(original_shape).permute(permute_back)
+    r"""
+    Compute \rho U^\dagger
     """
-    Compute \rho U^\dagger 
-    """
-    matdag = torch.conj(mat)
+
+    matdag = mat.conj()
+    if matdag.dim() == 3:
+        matdag = matdag.permute(0, 2, 1)
+    else:
+        matdag = matdag.permute(1, 0)
+
     matdag = matdag.type(C_DTYPE).to(density.device)
 
     devices_dims_dag = [n_qubit + w + 1 for w in device_wires]
@@ -307,7 +302,9 @@ def apply_unitary_density_bmm(density, mat, wires):
         del permute_to_dag[d]
     permute_to_dag = permute_to_dag + devices_dims_dag
     permute_back_dag = list(np.argsort(permute_to_dag))
-    permuted_dag = new_density.permute(permute_to_dag).reshape([original_shape[0], -1, matdag.shape[0]])
+    permuted_dag = new_density.permute(permute_to_dag).reshape(
+        [original_shape[0], -1, matdag.shape[0]]
+    )
 
     if len(matdag.shape) > 2:
         # both matrix and state are in batch mode
@@ -321,17 +318,24 @@ def apply_unitary_density_bmm(density, mat, wires):
     return new_density
 
 
+_noise_mat_dict = {
+    "Bitflip": torch.tensor([[0, 1], [1, 0]], dtype=C_DTYPE),
+    "Phaseflip": torch.tensor([[1, 0], [0, -1]], dtype=C_DTYPE)
+}
+
+
 def gate_wrapper(
-        name,
-        mat,
-        method,
-        q_device: QuantumDevice,
-        wires,
-        params=None,
-        n_wires=None,
-        static=False,
-        parent_graph=None,
-        inverse=False,
+    name,
+    mat,
+    method,
+    q_device: QuantumDevice,
+    wires,
+    paramnum=0,
+    params=None,
+    n_wires=None,
+    static=False,
+    parent_graph=None,
+    inverse=False,
 ):
     """Perform the phaseshift gate.
 
@@ -366,7 +370,12 @@ def gate_wrapper(
             else:
                 # this is for directly inputting parameters as a number
                 params = torch.tensor(params, dtype=F_DTYPE)
-
+        '''
+        Check whether user don't set parameters of multi parameters gate 
+        in batch mode.
+        '''
+        if params.dim() == 1 and params.shape[0] == paramnum:
+            params = params.unsqueeze(0)
         if name in ["qubitunitary", "qubitunitaryfast", "qubitunitarystrict"]:
             params = params.unsqueeze(0) if params.dim() == 2 else params
         else:
@@ -382,9 +391,11 @@ def gate_wrapper(
             {
                 "name": name,  # type: ignore
                 "wires": np.array(wires).squeeze().tolist(),
-                "params": params.squeeze().detach().cpu().numpy().tolist()
-                if params is not None
-                else None,
+                "params": (
+                    params.squeeze().detach().cpu().numpy().tolist()
+                    if params is not None
+                    else None
+                ),
                 "inverse": inverse,
                 "trainable": params.requires_grad if params is not None else False,
             }
@@ -431,12 +442,26 @@ def gate_wrapper(
             else:
                 matrix = matrix.permute(1, 0)
         assert np.log2(matrix.shape[-1]) == len(wires)
-        if q_device.device_name=="noisedevice":
+        
+        # TODO: There might be a better way to discriminate noisedevice and normal statevector device
+        if q_device.device_name == "noisedevice":
             density = q_device.densities
-            print(density.shape)
             if method == "einsum":
                 return
             elif method == "bmm":
+                '''
+                Apply kraus operator if there is noise
+                '''
+                kraus_dict = q_device.noise_model().kraus_dict()
+                if (kraus_dict["Bitflip"] != 0 or kraus_dict["Phaseflip"] != 0):
+                    p_identity = 1 - kraus_dict["Bitflip"] ** 2 - kraus_dict["Phaseflip"] ** 2
+                    if kraus_dict["Bitflip"] != 0:
+                        noise_mat = kraus_dict["Bitflip"] * _noise_mat_dict["Bitflip"]
+                        density_noise = apply_unitary_density_bmm(density, noise_mat, wires)
+                    if kraus_dict["Phaseflip"] != 0:
+                        noise_mat = kraus_dict["Phaseflip"] * _noise_mat_dict["Bitflip"]
+                        density_noise = density_noise + apply_unitary_density_bmm(density, noise_mat, wires)
+                    density = p_identity * density + density_noise
                 q_device.densities = apply_unitary_density_bmm(density, matrix, wires)
         else:
             state = q_device.states
@@ -444,4 +469,3 @@ def gate_wrapper(
                 q_device.states = apply_unitary_einsum(state, matrix, wires)
             elif method == "bmm":
                 q_device.states = apply_unitary_bmm(state, matrix, wires)
-
diff --git a/torchquantum/functional/hadamard.py b/torchquantum/functional/hadamard.py
index a2a45c40..a2deb86b 100644
--- a/torchquantum/functional/hadamard.py
+++ b/torchquantum/functional/hadamard.py
@@ -160,7 +160,7 @@ def chadamard(
 
     name = "chadamard"
 
-    mat = mat_dict[name]
+    mat = _hadamard_mat_dict[name]
     gate_wrapper(
         name=name,
         mat=mat,
diff --git a/torchquantum/functional/paulix.py b/torchquantum/functional/paulix.py
index d07f066f..e2904d13 100644
--- a/torchquantum/functional/paulix.py
+++ b/torchquantum/functional/paulix.py
@@ -508,7 +508,7 @@ def toffoli(
 
     """
     name = "toffoli"
-    mat = mat_dict[name]
+    mat =  _x_mat_dict[name]
     gate_wrapper(
         name=name,
         mat=mat,
@@ -552,7 +552,7 @@ def rc3x(
         None.
     """
     name = "rc3x"
-    mat = mat_dict[name]
+    mat =  _x_mat_dict[name]
     gate_wrapper(
         name=name,
         mat=mat,
@@ -596,7 +596,7 @@ def rccx(
         None.
     """
     name = "rccx"
-    mat = mat_dict[name]
+    mat =  _x_mat_dict[name]
     gate_wrapper(
         name=name,
         mat=mat,
diff --git a/torchquantum/functional/phase_shift.py b/torchquantum/functional/phase_shift.py
index e873b834..e06bd901 100644
--- a/torchquantum/functional/phase_shift.py
+++ b/torchquantum/functional/phase_shift.py
@@ -88,6 +88,7 @@ def phaseshift(
         method=comp_method,
         q_device=q_device,
         wires=wires,
+        paramnum=1,
         params=params,
         n_wires=n_wires,
         static=static,
diff --git a/torchquantum/functional/qubit_unitary.py b/torchquantum/functional/qubit_unitary.py
index 151680a0..aea3510c 100644
--- a/torchquantum/functional/qubit_unitary.py
+++ b/torchquantum/functional/qubit_unitary.py
@@ -132,6 +132,7 @@ def qubitunitary(
         method=comp_method,
         q_device=q_device,
         wires=wires,
+        paramnum=4,
         params=params,
         n_wires=n_wires,
         static=static,
@@ -227,6 +228,7 @@ def qubitunitarystrict(
         q_device=q_device,
         wires=wires,
         params=params,
+        paramnum=4,
         n_wires=n_wires,
         static=static,
         parent_graph=parent_graph,
diff --git a/torchquantum/functional/r.py b/torchquantum/functional/r.py
index d788e418..bd2aa0f6 100644
--- a/torchquantum/functional/r.py
+++ b/torchquantum/functional/r.py
@@ -97,6 +97,7 @@ def r(
         method=comp_method,
         q_device=q_device,
         wires=wires,
+        paramnum=2,
         params=params,
         n_wires=n_wires,
         static=static,
diff --git a/torchquantum/functional/rot.py b/torchquantum/functional/rot.py
index 1de26ef3..af45cc7c 100644
--- a/torchquantum/functional/rot.py
+++ b/torchquantum/functional/rot.py
@@ -134,6 +134,7 @@ def rot(
         method=comp_method,
         q_device=q_device,
         wires=wires,
+        paramnum=3,
         params=params,
         n_wires=n_wires,
         static=static,
@@ -181,6 +182,7 @@ def crot(
         method=comp_method,
         q_device=q_device,
         wires=wires,
+        paramnum=3,
         params=params,
         n_wires=n_wires,
         static=static,
diff --git a/torchquantum/functional/rx.py b/torchquantum/functional/rx.py
index a1c5d732..47c3cfce 100644
--- a/torchquantum/functional/rx.py
+++ b/torchquantum/functional/rx.py
@@ -161,6 +161,7 @@ def rx(
         method=comp_method,
         q_device=q_device,
         wires=wires,
+        paramnum=1,
         params=params,
         n_wires=n_wires,
         static=static,
@@ -208,6 +209,7 @@ def rxx(
         method=comp_method,
         q_device=q_device,
         wires=wires,
+        paramnum=1,
         params=params,
         n_wires=n_wires,
         static=static,
diff --git a/torchquantum/functional/ry.py b/torchquantum/functional/ry.py
index d098c7df..29ec3330 100644
--- a/torchquantum/functional/ry.py
+++ b/torchquantum/functional/ry.py
@@ -143,6 +143,7 @@ def ryy(
         method=comp_method,
         q_device=q_device,
         wires=wires,
+        paramnum=1,
         params=params,
         n_wires=n_wires,
         static=static,
@@ -197,6 +198,7 @@ def cry(
         method=comp_method,
         q_device=q_device,
         wires=wires,
+        paramnum=1,
         params=params,
         n_wires=n_wires,
         static=static,
@@ -244,6 +246,7 @@ def ry(
         method=comp_method,
         q_device=q_device,
         wires=wires,
+        paramnum=1,
         params=params,
         n_wires=n_wires,
         static=static,
diff --git a/torchquantum/functional/rz.py b/torchquantum/functional/rz.py
index 0cc0c651..f2685d31 100644
--- a/torchquantum/functional/rz.py
+++ b/torchquantum/functional/rz.py
@@ -219,6 +219,7 @@ def multirz(
         method=comp_method,
         q_device=q_device,
         wires=wires,
+        paramnum=1,
         params=params,
         n_wires=n_wires,
         static=static,
@@ -266,6 +267,7 @@ def crz(
         method=comp_method,
         q_device=q_device,
         wires=wires,
+        paramnum=1,
         params=params,
         n_wires=n_wires,
         static=static,
@@ -313,6 +315,7 @@ def rz(
         method=comp_method,
         q_device=q_device,
         wires=wires,
+        paramnum=1,
         params=params,
         n_wires=n_wires,
         static=static,
@@ -360,6 +363,7 @@ def rzz(
         method=comp_method,
         q_device=q_device,
         wires=wires,
+        paramnum=1,
         params=params,
         n_wires=n_wires,
         static=static,
@@ -407,6 +411,7 @@ def rzx(
         method=comp_method,
         q_device=q_device,
         wires=wires,
+        paramnum=1,
         params=params,
         n_wires=n_wires,
         static=static,
diff --git a/torchquantum/functional/test.py b/torchquantum/functional/test.py
new file mode 100644
index 00000000..12cd9c59
--- /dev/null
+++ b/torchquantum/functional/test.py
@@ -0,0 +1,32 @@
+import torch
+
+from torchquantum.macro import C_DTYPE
+from torchquantum.density import density_func
+from torchquantum.density import density_mat
+
+if __name__ == "__main__":
+    mat = density_func.mat_dict["hadamard"]
+
+    Xgatemat = density_func.mat_dict["paulix"]
+    print(mat)
+    D = density_mat.DensityMatrix(2, 1)
+
+    rho = torch.zeros(2 ** 4, dtype=C_DTYPE)
+    rho = torch.reshape(rho, [4, 4])
+    rho[0][0] = 1 / 2
+    rho[0][3] = 1 / 2
+    rho[3][0] = 1 / 2
+    rho[3][3] = 1 / 2
+    rho = torch.reshape(rho, [2, 2, 2, 2])
+    D.update_matrix(rho)
+    D.print_2d(0)
+    newD = density_func.apply_unitary_density_bmm(D._matrix, Xgatemat, [1])
+
+    print("D matrix shape")
+    print(D._matrix.shape)
+
+    print("newD shape")
+    print(newD.shape)
+    D.update_matrix(newD)
+
+    D.print_2d(0)
diff --git a/torchquantum/functional/u1.py b/torchquantum/functional/u1.py
index 05a94910..be0efff1 100644
--- a/torchquantum/functional/u1.py
+++ b/torchquantum/functional/u1.py
@@ -110,13 +110,14 @@ def u1(
 
     """
     name = "u1"
-    mat = mat_dict[name]
+    mat = _u1_mat_dict[name]
     gate_wrapper(
         name=name,
         mat=mat,
         method=comp_method,
         q_device=q_device,
         wires=wires,
+        paramnum=1,
         params=params,
         n_wires=n_wires,
         static=static,
@@ -157,13 +158,14 @@ def cu1(
 
     """
     name = "cu1"
-    mat = mat_dict[name]
+    mat = _u1_mat_dict[name]
     gate_wrapper(
         name=name,
         mat=mat,
         method=comp_method,
         q_device=q_device,
         wires=wires,
+        paramnum=1,
         params=params,
         n_wires=n_wires,
         static=static,
diff --git a/torchquantum/functional/u2.py b/torchquantum/functional/u2.py
index 5a1d9b21..98d201bb 100644
--- a/torchquantum/functional/u2.py
+++ b/torchquantum/functional/u2.py
@@ -109,13 +109,14 @@ def u2(
 
     """
     name = "u2"
-    mat = mat_dict[name]
+    mat = _u2_mat_dict[name]
     gate_wrapper(
         name=name,
         mat=mat,
         method=comp_method,
         q_device=q_device,
         wires=wires,
+        paramnum=2,
         params=params,
         n_wires=n_wires,
         static=static,
@@ -156,13 +157,14 @@ def cu2(
 
     """
     name = "cu2"
-    mat = mat_dict[name]
+    mat = _u2_mat_dict[name]
     gate_wrapper(
         name=name,
         mat=mat,
         method=comp_method,
         q_device=q_device,
         wires=wires,
+        paramnum=2,
         params=params,
         n_wires=n_wires,
         static=static,
diff --git a/torchquantum/functional/u3.py b/torchquantum/functional/u3.py
index 9dd0927f..076718e5 100644
--- a/torchquantum/functional/u3.py
+++ b/torchquantum/functional/u3.py
@@ -158,6 +158,7 @@ def u3(
         method=comp_method,
         q_device=q_device,
         wires=wires,
+        paramnum=3,
         params=params,
         n_wires=n_wires,
         static=static,
@@ -249,6 +250,7 @@ def cu3(
         method=comp_method,
         q_device=q_device,
         wires=wires,
+        paramnum=3,
         params=params,
         n_wires=n_wires,
         static=static,
diff --git a/torchquantum/layer/layers/module_from_ops.py b/torchquantum/layer/layers/module_from_ops.py
index f5aea5e0..b0a14541 100644
--- a/torchquantum/layer/layers/module_from_ops.py
+++ b/torchquantum/layer/layers/module_from_ops.py
@@ -22,16 +22,16 @@
 SOFTWARE.
 """
 
+from typing import Iterable
+
+import numpy as np
 import torch
 import torch.nn as nn
+from torchpack.utils.logging import logger
+
 import torchquantum as tq
 import torchquantum.functional as tqf
-import numpy as np
-
-
-from typing import Iterable
 from torchquantum.plugin.qiskit import QISKIT_INCOMPATIBLE_FUNC_NAMES
-from torchpack.utils.logging import logger
 
 __all__ = [
     "QuantumModuleFromOps",
@@ -61,6 +61,6 @@ def forward(self, q_device: tq.QuantumDevice):
             None
 
         """
-        self.q_device = q_device
+        q_device.reset_states(1)
         for op in self.ops:
-            op(q_device)
+            op(q_device, wires=op.wires)
diff --git a/torchquantum/measurement/__init__.py b/torchquantum/measurement/__init__.py
index bec5efe0..8d2ba360 100644
--- a/torchquantum/measurement/__init__.py
+++ b/torchquantum/measurement/__init__.py
@@ -23,3 +23,4 @@
 """
 
 from .measurements import *
+from .density_measurements import *
diff --git a/torchquantum/measurement/density_measurements.py b/torchquantum/measurement/density_measurements.py
new file mode 100644
index 00000000..e1663eb2
--- /dev/null
+++ b/torchquantum/measurement/density_measurements.py
@@ -0,0 +1,330 @@
+import random
+
+import torch
+import torchquantum as tq
+import torchquantum.functional as tqf
+import numpy as np
+from torchquantum.macro import F_DTYPE
+
+from typing import Union, List
+from collections import Counter, OrderedDict
+
+from torchquantum.functional import mat_dict
+# from .operator import op_name_dict, Observable
+import torchquantum.operator as op
+from copy import deepcopy
+import matplotlib.pyplot as plt
+from torchquantum.measurement import gen_bitstrings
+from torchquantum.measurement import find_observable_groups
+
+__all__ = [
+    "expval_joint_sampling_grouping_density",
+    "expval_joint_sampling_density",
+    "expval_joint_analytical_density",
+    "expval_density",
+    "measure_density",
+    "MeasureAll_density"
+]
+
+
+def measure_density(noisedev: tq.NoiseDevice, n_shots=1024, draw_id=None):
+    """Measure the target density matrix and obtain classical bitstream distribution
+    Args:
+        noisedev: input tq.NoiseDevice
+        n_shots: number of simulated shots
+    Returns:
+        distribution of bitstrings
+    """
+    bitstring_candidates = gen_bitstrings(noisedev.n_wires)
+
+    state_mag = noisedev.get_probs_1d().abs().detach().cpu().numpy()
+    distri_all = []
+
+    for state_mag_one in state_mag:
+        state_prob_one = state_mag_one
+        measured = random.choices(
+            population=bitstring_candidates,
+            weights=state_prob_one,
+            k=n_shots,
+        )
+        counter = Counter(measured)
+        counter.update({key: 0 for key in bitstring_candidates})
+        distri = dict(counter)
+        distri = OrderedDict(sorted(distri.items()))
+        distri_all.append(distri)
+
+    if draw_id is not None:
+        plt.bar(distri_all[draw_id].keys(), distri_all[draw_id].values())
+        plt.xticks(rotation="vertical")
+        plt.xlabel("bitstring [qubit0, qubit1, ..., qubitN]")
+        plt.title("distribution of measured bitstrings")
+        plt.show()
+    return distri_all
+
+
+def expval_joint_sampling_grouping_density(
+        noisedev: tq.NoiseDevice,
+        observables: List[str],
+        n_shots_per_group=1024,
+):
+    assert len(observables) == len(set(observables)), "each observable should be unique"
+    # key is the group, values is the list of sub-observables
+    obs = []
+    for observable in observables:
+        obs.append(observable.upper())
+    # firstly find the groups
+    groups = find_observable_groups(obs)
+
+    # rotation to the desired basis
+    n_wires = noisedev.n_wires
+    paulix = op.op_name_dict["paulix"]
+    pauliy = op.op_name_dict["pauliy"]
+    pauliz = op.op_name_dict["pauliz"]
+    iden = op.op_name_dict["i"]
+    pauli_dict = {"X": paulix, "Y": pauliy, "Z": pauliz, "I": iden}
+
+    expval_all_obs = {}
+    for obs_group, obs_elements in groups.items():
+        # for each group need to clone a new qdev and its densities
+        noisedev_clone = tq.NoiseDevice(n_wires=noisedev.n_wires, bsz=noisedev.bsz, device=noisedev.device)
+        noisedev_clone.clone_densities(noisedev.densities)
+
+        for wire in range(n_wires):
+            for rotation in pauli_dict[obs_group[wire]]().diagonalizing_gates():
+                rotation(noisedev_clone, wires=wire)
+
+        # measure
+        distributions = measure_density(noisedev_clone, n_shots=n_shots_per_group)
+        # interpret the distribution for different observable elements
+        for obs_element in obs_elements:
+            expval_all = []
+            mask = np.ones(len(obs_element), dtype=bool)
+            mask[np.array([*obs_element]) == "I"] = False
+
+            for distri in distributions:
+                n_eigen_one = 0
+                n_eigen_minus_one = 0
+                for bitstring, n_count in distri.items():
+                    if np.dot(list(map(lambda x: eval(x), [*bitstring])), mask).sum() % 2 == 0:
+                        n_eigen_one += n_count
+                    else:
+                        n_eigen_minus_one += n_count
+
+                expval = n_eigen_one / n_shots_per_group + (-1) * n_eigen_minus_one / n_shots_per_group
+
+                expval_all.append(expval)
+            expval_all_obs[obs_element] = torch.tensor(expval_all, dtype=F_DTYPE)
+
+    return expval_all_obs
+
+
+def expval_joint_sampling_density(
+        qdev: tq.NoiseDevice,
+        observable: str,
+        n_shots=1024,
+):
+    """
+    Compute the expectation value of a joint observable from sampling
+    the measurement bistring
+    Args:
+        qdev: the noise device
+        observable: the joint observable, on the qubit 0, 1, 2, 3, etc in this order
+    Returns:
+        the expectation value
+    Examples:
+    >>> import torchquantum as tq
+    >>> import torchquantum.functional as tqf
+    >>> x = tq.QuantumDevice(n_wires=2)
+    >>> tqf.hadamard(x, wires=0)
+    >>> tqf.x(x, wires=1)
+    >>> tqf.cnot(x, wires=[0, 1])
+    >>> print(expval_joint_sampling(x, 'II', n_shots=8192))
+    tensor([[0.9997]])
+    >>> print(expval_joint_sampling(x, 'XX', n_shots=8192))
+    tensor([[0.9991]])
+    >>> print(expval_joint_sampling(x, 'ZZ', n_shots=8192))
+    tensor([[-0.9980]])
+    """
+    # rotation to the desired basis
+    n_wires = qdev.n_wires
+    paulix = op.op_name_dict["paulix"]
+    pauliy = op.op_name_dict["pauliy"]
+    pauliz = op.op_name_dict["pauliz"]
+    iden = op.op_name_dict["i"]
+    pauli_dict = {"X": paulix, "Y": pauliy, "Z": pauliz, "I": iden}
+
+    qdev_clone = tq.NoiseDevice(n_wires=qdev.n_wires, bsz=qdev.bsz, device=qdev.device)
+    qdev_clone.clone_densities(qdev.densities)
+
+    observable = observable.upper()
+    for wire in range(n_wires):
+        for rotation in pauli_dict[observable[wire]]().diagonalizing_gates():
+            rotation(qdev_clone, wires=wire)
+
+    mask = np.ones(len(observable), dtype=bool)
+    mask[np.array([*observable]) == "I"] = False
+
+    expval_all = []
+    # measure
+    distributions = measure_density(qdev_clone, n_shots=n_shots)
+    for distri in distributions:
+        n_eigen_one = 0
+        n_eigen_minus_one = 0
+        for bitstring, n_count in distri.items():
+            if np.dot(list(map(lambda x: eval(x), [*bitstring])), mask).sum() % 2 == 0:
+                n_eigen_one += n_count
+            else:
+                n_eigen_minus_one += n_count
+
+        expval = n_eigen_one / n_shots + (-1) * n_eigen_minus_one / n_shots
+        expval_all.append(expval)
+
+    return torch.tensor(expval_all, dtype=F_DTYPE)
+
+
+def expval_joint_analytical_density(
+        noisedev: tq.NoiseDevice,
+        observable: str,
+        n_shots=1024
+):
+    """
+    Compute the expectation value of a joint observable in analytical way, assuming the
+    density matrix is available.
+    Args:
+        qdev: the quantum device
+        observable: the joint observable, on the qubit 0, 1, 2, 3, etc in this order
+    Returns:
+        the expectation value
+    Examples:
+    >>> import torchquantum as tq
+    >>> import torchquantum.functional as tqf
+    >>> x = tq.QuantumDevice(n_wires=2)
+    >>> tqf.hadamard(x, wires=0)
+    >>> tqf.x(x, wires=1)
+    >>> tqf.cnot(x, wires=[0, 1])
+    >>> print(expval_joint_analytical(x, 'II'))
+    tensor([[1.0000]])
+    >>> print(expval_joint_analytical(x, 'XX'))
+    tensor([[1.0000]])
+    >>> print(expval_joint_analytical(x, 'ZZ'))
+    tensor([[-1.0000]])
+    """
+    # compute the hamiltonian matrix
+    paulix = mat_dict["paulix"]
+    pauliy = mat_dict["pauliy"]
+    pauliz = mat_dict["pauliz"]
+    iden = mat_dict["i"]
+    pauli_dict = {"X": paulix, "Y": pauliy, "Z": pauliz, "I": iden}
+
+    observable = observable.upper()
+    assert len(observable) == noisedev.n_wires
+    densities = noisedev.get_densities_2d()
+
+    hamiltonian = pauli_dict[observable[0]].to(densities.device)
+    for op in observable[1:]:
+        hamiltonian = torch.kron(hamiltonian, pauli_dict[op].to(densities.device))
+
+    batch_size = densities.shape[0]
+    expanded_hamiltonian = hamiltonian.unsqueeze(0).expand(batch_size, *hamiltonian.shape)
+
+    product = torch.bmm(expanded_hamiltonian, densities)
+
+    # Extract the diagonal elements from each matrix in the batch
+    diagonals = torch.diagonal(product, dim1=-2, dim2=-1)
+
+    # Sum the diagonal elements to get the trace for each batch
+    trace = torch.sum(diagonals, dim=-1).real
+
+    # Should use expectation= Tr(observable \times density matrix)
+    return trace
+
+
+def expval_density(
+        noisedev: tq.NoiseDevice,
+        wires: Union[int, List[int]],
+        observables: Union[op.Observable, List[op.Observable]],
+):
+    all_dims = np.arange(noisedev.n_wires + 1)
+    if isinstance(wires, int):
+        wires = [wires]
+        observables = [observables]
+
+    # rotation to the desired basis
+    for wire, observable in zip(wires, observables):
+        for rotation in observable.diagonalizing_gates():
+            rotation(noisedev, wires=wire)
+
+    # compute magnitude
+    state_mag = noisedev.get_probs_1d()
+    bsz = state_mag.shape[0]
+    state_mag = torch.reshape(state_mag, [bsz] + [2] * noisedev.n_wires)
+    expectations = []
+    for wire, observable in zip(wires, observables):
+        # compute marginal magnitude
+        reduction_dims = np.delete(all_dims, [0, wire + 1])
+        if reduction_dims.size == 0:
+            probs = state_mag
+        else:
+            probs = state_mag.sum(list(reduction_dims))
+        res = probs.mv(observable.eigvals.real.to(probs.device))
+        expectations.append(res)
+
+    return torch.stack(expectations, dim=-1)
+
+
+class MeasureAll_density(tq.QuantumModule):
+    """Obtain the expectation value of all the qubits."""
+
+    def __init__(self, obs, v_c_reg_mapping=None):
+        super().__init__()
+        self.obs = obs
+        self.v_c_reg_mapping = v_c_reg_mapping
+
+    def forward(self, qdev: tq.NoiseDevice):
+        x = expval_density(qdev, list(range(qdev.n_wires)), [self.obs()] * qdev.n_wires)
+
+        if self.v_c_reg_mapping is not None:
+            c2v_mapping = self.v_c_reg_mapping["c2v"]
+            """
+            the measurement is not normal order, need permutation
+            """
+            perm = []
+            for k in range(x.shape[-1]):
+                if k in c2v_mapping.keys():
+                    perm.append(c2v_mapping[k])
+            x = x[:, perm]
+
+        if self.noise_model_tq is not None and self.noise_model_tq.is_add_noise:
+            return self.noise_model_tq.apply_readout_error(x)
+        else:
+            return x
+
+    def set_v_c_reg_mapping(self, mapping):
+        self.v_c_reg_mapping = mapping
+
+
+if __name__ == '__main__':
+    qdev = tq.NoiseDevice(n_wires=2, bsz=5, device="cpu", record_op=True)  # use device='cuda' for GPU
+    qdev.h(wires=0)
+    qdev.cnot(wires=[0, 1])
+    # tqf.h(qdev, wires=1)
+    # tqf.x(qdev, wires=1)
+    # tqf.y(qdev, wires=1)
+    # tqf.cnot(qdev,wires=[0, 1])
+    # op = tq.RX(has_params=True, trainable=True, init_params=0.5)
+    # op(qdev, wires=0)
+    result = tq.expval_density(qdev, [0, 1], [tq.PauliZ(), tq.PauliZ()])
+    print(result.shape)
+
+    # measure the state on z basis
+    # print(tq.measure_density(qdev, n_shots=1024))
+
+    # obtain the expval on a observable
+    # expval = expval_joint_sampling_density(qdev, 'XZ', 100000)
+
+    # print("expval")
+    # print(expval)
+
+    # expval_ana = expval_joint_analytical_density(qdev, 'XZ')
+    # print("expval_ana")
+    # print(expval_ana)
diff --git a/torchquantum/measurement/measurements.py b/torchquantum/measurement/measurements.py
index c3c2daad..14b11b5d 100644
--- a/torchquantum/measurement/measurements.py
+++ b/torchquantum/measurement/measurements.py
@@ -43,13 +43,9 @@ def measure(qdev, n_shots=1024, draw_id=None):
         distribution of bitstrings
     """
     bitstring_candidates = gen_bitstrings(qdev.n_wires)
-    if isinstance(qdev, tq.QuantumDevice):
-        state_mag = qdev.get_states_1d().abs().detach().cpu().numpy()
-    elif isinstance(qdev, tq.NoiseDevice):
-        '''
-        Measure the density matrix in the computational basis
-        '''
-        state_mag = qdev.get_probs_1d().abs().detach().cpu().numpy()
+
+    #state_prob =
+    state_mag = qdev.get_states_1d().abs().detach().cpu().numpy()
     distri_all = []
 
     for state_mag_one in state_mag:
@@ -285,6 +281,7 @@ def expval(
         observables: Union[op.Observable, List[op.Observable]],
 ):
     all_dims = np.arange(qdev.states.dim())
+
     if isinstance(wires, int):
         wires = [wires]
         observables = [observables]
@@ -295,9 +292,9 @@ def expval(
             rotation(qdev, wires=wire)
 
     states = qdev.states
+
     # compute magnitude
     state_mag = torch.abs(states) ** 2
-
     expectations = []
     for wire, observable in zip(wires, observables):
         # compute marginal magnitude
@@ -424,7 +421,7 @@ def __init__(self, obs_list, v_c_reg_mapping=None):
         )
 
     def forward(self, qdev: tq.QuantumDevice):
-        res_all = self.measure_multiple_times(qdev)
+        res_all = self.measure_multiple_times(qdev).prod(-1)
 
         return res_all.sum(-1)
 
@@ -452,8 +449,9 @@ def __init__(self, obs_list, v_c_reg_mapping=None):
         )
 
     def forward(self, qdev: tq.QuantumDevice):
-        res_all = self.measure_multiple_times(qdev)
-        return (res_all * self.obs_list[0]["coefficient"]).sum(-1)
+        res_all = self.measure_multiple_times(qdev).prod(-1)
+        
+        return (res_all * torch.tensor(self.obs_list[0]["coefficient"])).sum(-1)
 
 
 if __name__ == '__main__':
diff --git a/torchquantum/noise_model/noise_models.py b/torchquantum/noise_model/noise_models.py
index 571314e9..2309c7e8 100644
--- a/torchquantum/noise_model/noise_models.py
+++ b/torchquantum/noise_model/noise_models.py
@@ -24,12 +24,11 @@
 
 import numpy as np
 import torch
-import torchquantum as tq
-
+from qiskit_aer.noise import NoiseModel
 from torchpack.utils.logging import logger
-from qiskit.providers.aer.noise import NoiseModel
-from torchquantum.util import get_provider
 
+import torchquantum as tq
+from torchquantum.util import get_provider
 
 __all__ = [
     "NoiseModelTQ",
@@ -50,31 +49,31 @@ def cos_adjust_noise(
     orig_noise_total_prob,
 ):
     """
-        Adjust the noise probability based on the current epoch and a cosine schedule.
+    Adjust the noise probability based on the current epoch and a cosine schedule.
+
+    Args:
+        current_epoch (int): The current epoch.
+        n_epochs (int): The total number of epochs.
+        prob_schedule (str): The probability schedule type. Possible values are:
+            - None: No schedule, use the original noise probability.
+            - "increase": Increase the noise probability using a cosine schedule.
+            - "decrease": Decrease the noise probability using a cosine schedule.
+            - "increase_decrease": Increase the noise probability until a separator epoch,
+              then decrease it using cosine schedules.
+        prob_schedule_separator (int): The epoch at which the schedule changes for
+            "increase_decrease" mode.
+        orig_noise_total_prob (float): The original noise probability.
+
+    Returns:
+        float: The adjusted noise probability based on the schedule.
+
+    Note:
+        The adjusted noise probability is returned as a float between 0 and 1.
+
+    Raises:
+        None.
 
-        Args:
-            current_epoch (int): The current epoch.
-            n_epochs (int): The total number of epochs.
-            prob_schedule (str): The probability schedule type. Possible values are:
-                - None: No schedule, use the original noise probability.
-                - "increase": Increase the noise probability using a cosine schedule.
-                - "decrease": Decrease the noise probability using a cosine schedule.
-                - "increase_decrease": Increase the noise probability until a separator epoch,
-                  then decrease it using cosine schedules.
-            prob_schedule_separator (int): The epoch at which the schedule changes for
-                "increase_decrease" mode.
-            orig_noise_total_prob (float): The original noise probability.
-
-        Returns:
-            float: The adjusted noise probability based on the schedule.
-
-        Note:
-            The adjusted noise probability is returned as a float between 0 and 1.
-
-        Raises:
-            None.
-
-        """
+    """
 
     if prob_schedule is None:
         noise_total_prob = orig_noise_total_prob
@@ -134,31 +133,31 @@ def cos_adjust_noise(
 
 def apply_readout_error_func(x, c2p_mapping, measure_info):
     """
-        Apply readout error to the measurement outcomes.
-
-        Args:
-            x (torch.Tensor): The measurement outcomes, represented as a tensor of shape (batch_size, num_qubits).
-            c2p_mapping (dict): Mapping from qubit indices to physical wire indices.
-            measure_info (dict): Measurement information dictionary containing the probabilities for different outcomes.
-
-        Returns:
-            torch.Tensor: The measurement outcomes after applying the readout error, represented as a tensor of the same shape as x.
-
-        Note:
-            The readout error is applied based on the given mapping and measurement information.
-            The measurement information dictionary should have the following structure:
-            {
-                (wire_1,): {"probabilities": [[p_0, p_1], [p_0, p_1]]},
-                (wire_2,): {"probabilities": [[p_0, p_1], [p_0, p_1]]},
-                ...
-            }
-            where wire_1, wire_2, ... are the physical wire indices, and p_0 and p_1 are the probabilities of measuring 0 and 1, respectively,
-            for each wire.
-
-        Raises:
-            None.
+    Apply readout error to the measurement outcomes.
+
+    Args:
+        x (torch.Tensor): The measurement outcomes, represented as a tensor of shape (batch_size, num_qubits).
+        c2p_mapping (dict): Mapping from qubit indices to physical wire indices.
+        measure_info (dict): Measurement information dictionary containing the probabilities for different outcomes.
+
+    Returns:
+        torch.Tensor: The measurement outcomes after applying the readout error, represented as a tensor of the same shape as x.
+
+    Note:
+        The readout error is applied based on the given mapping and measurement information.
+        The measurement information dictionary should have the following structure:
+        {
+            (wire_1,): {"probabilities": [[p_0, p_1], [p_0, p_1]]},
+            (wire_2,): {"probabilities": [[p_0, p_1], [p_0, p_1]]},
+            ...
+        }
+        where wire_1, wire_2, ... are the physical wire indices, and p_0 and p_1 are the probabilities of measuring 0 and 1, respectively,
+        for each wire.
+
+    Raises:
+        None.
 
-        """
+    """
     # add readout error
     noise_free_0_probs = (x + 1) / 2
     noise_free_1_probs = 1 - (x + 1) / 2
@@ -196,21 +195,22 @@ def apply_readout_error_func(x, c2p_mapping, measure_info):
 
 class NoiseCounter:
     """
-        A class for counting the occurrences of Pauli error gates.
+    A class for counting the occurrences of Pauli error gates.
 
-        Attributes:
-            counter_x (int): Counter for Pauli X errors.
-            counter_y (int): Counter for Pauli Y errors.
-            counter_z (int): Counter for Pauli Z errors.
-            counter_X (int): Counter for Pauli X errors (for two-qubit gates).
-            counter_Y (int): Counter for Pauli Y errors (for two-qubit gates).
-            counter_Z (int): Counter for Pauli Z errors (for two-qubit gates).
+    Attributes:
+        counter_x (int): Counter for Pauli X errors.
+        counter_y (int): Counter for Pauli Y errors.
+        counter_z (int): Counter for Pauli Z errors.
+        counter_X (int): Counter for Pauli X errors (for two-qubit gates).
+        counter_Y (int): Counter for Pauli Y errors (for two-qubit gates).
+        counter_Z (int): Counter for Pauli Z errors (for two-qubit gates).
 
-        Methods:
-            add(error): Adds a Pauli error to the counters based on the error type.
-            __str__(): Returns a string representation of the counters.
+    Methods:
+        add(error): Adds a Pauli error to the counters based on the error type.
+        __str__(): Returns a string representation of the counters.
+
+    """
 
-        """
     def __init__(self):
         self.counter_x = 0
         self.counter_y = 0
@@ -220,51 +220,51 @@ def __init__(self):
         self.counter_Z = 0
 
     def add(self, error):
-        if error == 'x':
+        if error == "x":
             self.counter_x += 1
-        elif error == 'y':
+        elif error == "y":
             self.counter_y += 1
-        elif error == 'z':
+        elif error == "z":
             self.counter_z += 1
-        if error == 'X':
+        if error == "X":
             self.counter_X += 1
-        elif error == 'Y':
+        elif error == "Y":
             self.counter_Y += 1
-        elif error == 'Z':
+        elif error == "Z":
             self.counter_Z += 1
         else:
             pass
-        
-    def __str__(self) -> str:
-        return f'single qubit error: pauli x = {self.counter_x}, pauli y = {self.counter_y}, pauli z = {self.counter_z}\n' + \
-               f'double qubit error: pauli x = {self.counter_X}, pauli y = {self.counter_Y}, pauli z = {self.counter_Z}'
 
+    def __str__(self) -> str:
+        return (
+            f"single qubit error: pauli x = {self.counter_x}, pauli y = {self.counter_y}, pauli z = {self.counter_z}\n"
+            + f"double qubit error: pauli x = {self.counter_X}, pauli y = {self.counter_Y}, pauli z = {self.counter_Z}"
+        )
 
 
 class NoiseModelTQ(object):
     """
-        A class for applying gate insertion and readout errors.
-
-        Attributes:
-            noise_model_name (str): Name of the noise model.
-            n_epochs (int): Number of epochs.
-            noise_total_prob (float): Total probability of noise.
-            ignored_ops (tuple): Operations to be ignored.
-            prob_schedule (list): Probability schedule.
-            prob_schedule_separator (str): Separator for probability schedule.
-            factor (float): Factor for adjusting probabilities.
-            add_thermal (bool): Flag indicating whether to add thermal relaxation.
-
-        Methods:
-            adjust_noise(current_epoch): Adjusts the noise based on the current epoch.
-            clean_parsed_noise_model_dict(nm_dict, ignored_ops): Cleans the parsed noise model dictionary.
-            parse_noise_model_dict(nm_dict): Parses the noise model dictionary.
-            magnify_probs(probs): Magnifies the probabilities based on a factor.
-            sample_noise_op(op_in): Samples a noise operation based on the given operation.
-            apply_readout_error(x): Applies readout error to the input.
-
-        """
+    A class for applying gate insertion and readout errors.
+
+    Attributes:
+        noise_model_name (str): Name of the noise model.
+        n_epochs (int): Number of epochs.
+        noise_total_prob (float): Total probability of noise.
+        ignored_ops (tuple): Operations to be ignored.
+        prob_schedule (list): Probability schedule.
+        prob_schedule_separator (str): Separator for probability schedule.
+        factor (float): Factor for adjusting probabilities.
+        add_thermal (bool): Flag indicating whether to add thermal relaxation.
+
+    Methods:
+        adjust_noise(current_epoch): Adjusts the noise based on the current epoch.
+        clean_parsed_noise_model_dict(nm_dict, ignored_ops): Cleans the parsed noise model dictionary.
+        parse_noise_model_dict(nm_dict): Parses the noise model dictionary.
+        magnify_probs(probs): Magnifies the probabilities based on a factor.
+        sample_noise_op(op_in): Samples a noise operation based on the given operation.
+        apply_readout_error(x): Applies readout error to the input.
 
+    """
 
     def __init__(
         self,
@@ -295,7 +295,9 @@ def __init__(
         self.ignored_ops = ignored_ops
 
         self.parsed_dict = self.parse_noise_model_dict(self.noise_model_dict)
-        self.parsed_dict = self.clean_parsed_noise_model_dict(self.parsed_dict, ignored_ops)
+        self.parsed_dict = self.clean_parsed_noise_model_dict(
+            self.parsed_dict, ignored_ops
+        )
         self.n_epochs = n_epochs
         self.prob_schedule = prob_schedule
         self.prob_schedule_separator = prob_schedule_separator
@@ -313,39 +315,66 @@ def adjust_noise(self, current_epoch):
 
     @staticmethod
     def clean_parsed_noise_model_dict(nm_dict, ignored_ops):
-        # remove the ignored operation in the instructions and probs  
+        # remove the ignored operation in the instructions and probs
         # --> only get the pauli-x,y,z errors. ignore the thermal relaxation errors (kraus operator)
 
         def filter_inst(inst_list: list) -> list:
             new_inst_list = []
             for inst in inst_list:
-                if inst['name'] in ignored_ops:
+                if inst["name"] in ignored_ops:
                     continue
                 new_inst_list.append(inst)
             return new_inst_list
 
-        ignored_ops           = set(ignored_ops)
-        single_depolarization = set(['x', 'y', 'z'])
-        double_depolarization = set(['IX', 'IY', 'IZ', 'XI', 'XX', 'XY', 'XZ', 'YI', 'YX', 'YY', 'YZ', 'ZI', 'ZX', 'ZY', 'ZZ']) # 16 - 1 = 15 combinations
+        ignored_ops = set(ignored_ops)
+        single_depolarization = set(["x", "y", "z"])
+        double_depolarization = set(
+            [
+                "IX",
+                "IY",
+                "IZ",
+                "XI",
+                "XX",
+                "XY",
+                "XZ",
+                "YI",
+                "YX",
+                "YY",
+                "YZ",
+                "ZI",
+                "ZX",
+                "ZY",
+                "ZZ",
+            ]
+        )  # 16 - 1 = 15 combinations
         for operation, operation_info in nm_dict.items():
             for qubit, qubit_info in operation_info.items():
                 inst_all = []
                 prob_all = []
                 if qubit_info["type"] == "qerror":
-                    for inst, prob in zip(qubit_info["instructions"], qubit_info["probabilities"]):
-                        if operation in ['x', 'sx', 'id', 'reset']:              # single qubit gate
-                            if any([inst_one["name"] in single_depolarization for inst_one in inst]):
+                    for inst, prob in zip(
+                        qubit_info["instructions"], qubit_info["probabilities"]
+                    ):
+                        if operation in ["x", "sx", "id", "reset"]:  # single qubit gate
+                            if any(
+                                [
+                                    inst_one["name"] in single_depolarization
+                                    for inst_one in inst
+                                ]
+                            ):
                                 inst_all.append(filter_inst(inst))
                                 prob_all.append(prob)
-                        elif operation in ['cx']:                                # double qubit gate
+                        elif operation in ["cx"]:  # double qubit gate
                             try:
-                                if inst[0]['params'][0] in double_depolarization and (inst[1]['name'] == 'id' or inst[2]['name'] == 'id'):
+                                if inst[0]["params"][0] in double_depolarization and (
+                                    inst[1]["name"] == "id" or inst[2]["name"] == "id"
+                                ):
                                     inst_all.append(filter_inst(inst))
                                     prob_all.append(prob)
                             except:
                                 pass  # don't know how to deal with this case
                         else:
-                            raise Exception(f'{operation} not considered...')
+                            raise Exception(f"{operation} not considered...")
                     nm_dict[operation][qubit]["instructions"] = inst_all
                     nm_dict[operation][qubit]["probabilities"] = prob_all
         return nm_dict
@@ -364,8 +393,13 @@ def parse_noise_model_dict(nm_dict):
             }
 
             if info["operations"][0] not in parsed.keys():
-                parsed[info["operations"][0]] = {tuple(info["gate_qubits"][0]): val_dict}
-            elif tuple(info["gate_qubits"][0]) not in parsed[info["operations"][0]].keys():
+                parsed[info["operations"][0]] = {
+                    tuple(info["gate_qubits"][0]): val_dict
+                }
+            elif (
+                tuple(info["gate_qubits"][0])
+                not in parsed[info["operations"][0]].keys()
+            ):
                 parsed[info["operations"][0]][tuple(info["gate_qubits"][0])] = val_dict
             else:
                 raise ValueError
@@ -432,30 +466,36 @@ def sample_noise_op(self, op_in):
 
         ops = []
         for instruction in instructions:
-            v_wires = [self.p_v_reg_mapping["p2v"][qubit] for qubit in instruction["qubits"]]
+            v_wires = [
+                self.p_v_reg_mapping["p2v"][qubit] for qubit in instruction["qubits"]
+            ]
             if instruction["name"] == "x":
                 ops.append(tq.PauliX(wires=v_wires))
-                self.noise_counter.add('x')
+                self.noise_counter.add("x")
             elif instruction["name"] == "y":
                 ops.append(tq.PauliY(wires=v_wires))
-                self.noise_counter.add('y')
+                self.noise_counter.add("y")
             elif instruction["name"] == "z":
                 ops.append(tq.PauliZ(wires=v_wires))
-                self.noise_counter.add('z')
+                self.noise_counter.add("z")
             elif instruction["name"] == "reset":
                 ops.append(tq.Reset(wires=v_wires))
             elif instruction["name"] == "pauli":
-                twoqubit_depolarization = list(instruction['params'][0])  # ['XY'] --> ['X', 'Y']
-                for singlequbit_deloparization, v_wire in zip(twoqubit_depolarization, v_wires):
-                    if singlequbit_deloparization == 'X':
+                twoqubit_depolarization = list(
+                    instruction["params"][0]
+                )  # ['XY'] --> ['X', 'Y']
+                for singlequbit_deloparization, v_wire in zip(
+                    twoqubit_depolarization, v_wires
+                ):
+                    if singlequbit_deloparization == "X":
                         ops.append(tq.PauliX(wires=[v_wire]))
-                        self.noise_counter.add('X')
-                    elif singlequbit_deloparization == 'Y':
+                        self.noise_counter.add("X")
+                    elif singlequbit_deloparization == "Y":
                         ops.append(tq.PauliY(wires=[v_wire]))
-                        self.noise_counter.add('Y')
-                    elif singlequbit_deloparization == 'Z':
+                        self.noise_counter.add("Y")
+                    elif singlequbit_deloparization == "Z":
                         ops.append(tq.PauliZ(wires=[v_wire]))
-                        self.noise_counter.add('Z')
+                        self.noise_counter.add("Z")
                     else:
                         pass  # 'I' case
             else:
@@ -474,25 +514,24 @@ def apply_readout_error(self, x):
 
 class NoiseModelTQActivation(object):
     """
-        A class for adding noise to the activations.
-
-        Attributes:
-            mean (tuple): Mean values of the noise.
-            std (tuple): Standard deviation values of the noise.
-            n_epochs (int): Number of epochs.
-            prob_schedule (list): Probability schedule.
-            prob_schedule_separator (str): Separator for probability schedule.
-            after_norm (bool): Flag indicating whether noise should be added after normalization.
-            factor (float): Factor for adjusting the noise.
-
-        Methods:
-            adjust_noise(current_epoch): Adjusts the noise based on the current epoch.
-            sample_noise_op(op_in): Samples a noise operation.
-            apply_readout_error(x): Applies readout error to the input.
-            add_noise(x, node_id, is_after_norm): Adds noise to the activations.
-
-        """
+    A class for adding noise to the activations.
+
+    Attributes:
+        mean (tuple): Mean values of the noise.
+        std (tuple): Standard deviation values of the noise.
+        n_epochs (int): Number of epochs.
+        prob_schedule (list): Probability schedule.
+        prob_schedule_separator (str): Separator for probability schedule.
+        after_norm (bool): Flag indicating whether noise should be added after normalization.
+        factor (float): Factor for adjusting the noise.
+
+    Methods:
+        adjust_noise(current_epoch): Adjusts the noise based on the current epoch.
+        sample_noise_op(op_in): Samples a noise operation.
+        apply_readout_error(x): Applies readout error to the input.
+        add_noise(x, node_id, is_after_norm): Adds noise to the activations.
 
+    """
 
     def __init__(
         self,
@@ -560,23 +599,23 @@ def add_noise(self, x, node_id, is_after_norm=False):
 
 class NoiseModelTQPhase(object):
     """
-        A class for adding noise to rotation parameters.
-
-        Attributes:
-            mean (float): Mean value of the noise.
-            std (float): Standard deviation value of the noise.
-            n_epochs (int): Number of epochs.
-            prob_schedule (list): Probability schedule.
-            prob_schedule_separator (str): Separator for probability schedule.
-            factor (float): Factor for adjusting the noise.
-
-        Methods:
-            adjust_noise(current_epoch): Adjusts the noise based on the current epoch.
-            sample_noise_op(op_in): Samples a noise operation.
-            apply_readout_error(x): Applies readout error to the input.
-            add_noise(phase): Adds noise to the rotation parameters.
+    A class for adding noise to rotation parameters.
+
+    Attributes:
+        mean (float): Mean value of the noise.
+        std (float): Standard deviation value of the noise.
+        n_epochs (int): Number of epochs.
+        prob_schedule (list): Probability schedule.
+        prob_schedule_separator (str): Separator for probability schedule.
+        factor (float): Factor for adjusting the noise.
+
+    Methods:
+        adjust_noise(current_epoch): Adjusts the noise based on the current epoch.
+        sample_noise_op(op_in): Samples a noise operation.
+        apply_readout_error(x): Applies readout error to the input.
+        add_noise(phase): Adds noise to the rotation parameters.
 
-        """
+    """
 
     def __init__(
         self,
@@ -638,40 +677,43 @@ def add_noise(self, phase):
 
 class NoiseModelTQReadoutOnly(NoiseModelTQ):
     """
-       A subclass of NoiseModelTQ that applies readout errors only.
+    A subclass of NoiseModelTQ that applies readout errors only.
+
+    This class inherits from NoiseModelTQ and overrides the sample_noise_op method to exclude the insertion of any noise operations other than readout errors. It is designed for scenarios where only readout errors are considered, and all other noise sources are ignored.
 
-       This class inherits from NoiseModelTQ and overrides the sample_noise_op method to exclude the insertion of any noise operations other than readout errors. It is designed for scenarios where only readout errors are considered, and all other noise sources are ignored.
+    Methods:
+        sample_noise_op(op_in): Returns an empty list, indicating no noise operations are applied.
+    """
 
-       Methods:
-           sample_noise_op(op_in): Returns an empty list, indicating no noise operations are applied.
-       """
     def sample_noise_op(self, op_in):
         return []
 
 
 class NoiseModelTQQErrorOnly(NoiseModelTQ):
     """
-        A subclass of NoiseModelTQ that applies only readout errors.
+    A subclass of NoiseModelTQ that applies only readout errors.
 
-        This class inherits from NoiseModelTQ and overrides the apply_readout_error method to apply readout errors. It removes activation noise and only focuses on readout errors in the noise model.
+    This class inherits from NoiseModelTQ and overrides the apply_readout_error method to apply readout errors. It removes activation noise and only focuses on readout errors in the noise model.
 
-        Methods:
-            apply_readout_error(x): Applies readout error to the given activation values.
+    Methods:
+        apply_readout_error(x): Applies readout error to the given activation values.
+
+    """
 
-        """
     def apply_readout_error(self, x):
         return x
 
 
 class NoiseModelTQActivationReadout(NoiseModelTQActivation):
     """
-       A subclass of NoiseModelTQActivation that applies readout errors.
+    A subclass of NoiseModelTQActivation that applies readout errors.
 
-       This class inherits from NoiseModelTQActivation and overrides the apply_readout_error method to incorporate readout errors. It combines activation noise and readout errors into the noise model.
+    This class inherits from NoiseModelTQActivation and overrides the apply_readout_error method to incorporate readout errors. It combines activation noise and readout errors into the noise model.
+
+    Methods:
+        apply_readout_error(x): Applies readout error to the given activation values
+    """
 
-       Methods:
-           apply_readout_error(x): Applies readout error to the given activation values
-       """
     def __init__(
         self,
         noise_model_name,
@@ -713,13 +755,14 @@ def apply_readout_error(self, x):
 
 class NoiseModelTQPhaseReadout(NoiseModelTQPhase):
     """
-        A subclass of NoiseModelTQPhase that applies readout errors to phase values.
+    A subclass of NoiseModelTQPhase that applies readout errors to phase values.
 
-        This class inherits from NoiseModelTQPhase and overrides the apply_readout_error method to apply readout errors specifically to phase values. It uses the noise model provided to introduce readout errors.
+    This class inherits from NoiseModelTQPhase and overrides the apply_readout_error method to apply readout errors specifically to phase values. It uses the noise model provided to introduce readout errors.
+
+    Methods:
+        apply_readout_error(x): Applies readout error to the given phase values.
+    """
 
-        Methods:
-            apply_readout_error(x): Applies readout error to the given phase values.
-        """
     def __init__(
         self,
         noise_model_name,
diff --git a/torchquantum/operator/op_types.py b/torchquantum/operator/op_types.py
index bdf35337..e005af6d 100644
--- a/torchquantum/operator/op_types.py
+++ b/torchquantum/operator/op_types.py
@@ -245,6 +245,7 @@ def forward(
                 params = self.params
 
             if self.clifford_quantization:
+                from torchquantum.util.quantization.clifford_quantization import CliffordQuantizer
                 params = CliffordQuantizer.quantize_sse(params)
             if self.n_wires is None:
                 self.func(q_device, self.wires, params=params, inverse=self.inverse)
diff --git a/torchquantum/operator/standard_gates/__init__.py b/torchquantum/operator/standard_gates/__init__.py
index 98f55997..87cdca2e 100644
--- a/torchquantum/operator/standard_gates/__init__.py
+++ b/torchquantum/operator/standard_gates/__init__.py
@@ -54,7 +54,7 @@
 from .xx_min_yy import XXMINYY
 from .xx_plus_yy import XXPLUSYY
 
-all_variables = [
+_all_variables = [
     EchoedCrossResonance,
     ECR,
     GlobalPhase,
@@ -127,16 +127,8 @@
     XXPLUSYY,
 ]
 
-__all__ = [a().__class__.__name__ for a in all_variables]
-
-# add the aliased and incomptaible classes
-__all__.extend(["U", "CH", "QubitUnitary", "QubitUnitaryFast"])
-
-# add the dictionary
-__all__.extend(["op_name_dict", "fixed_ops", "parameterized_ops"])
-
 # create the operations dictionary
-op_name_dict = {x.op_name: x for x in all_variables}
+op_name_dict = {_x.op_name: _x for _x in _all_variables}
 
 # add aliases as well
 op_name_dict.update(
@@ -161,5 +153,6 @@
     }
 )
 
-fixed_ops = [a().__class__.__name__ for a in all_variables if a.num_params == 0]
-parameterized_ops = [a().__class__.__name__ for a in all_variables if a.num_params > 0]
+# TODO: make this compatible with aliases
+fixed_ops = [_a().__class__.__name__ for _a in _all_variables if _a.num_params == 0]
+parameterized_ops = [_a().__class__.__name__ for _a in _all_variables if _a.num_params > 0]
diff --git a/torchquantum/operator/standard_gates/qubit_unitary.py b/torchquantum/operator/standard_gates/qubit_unitary.py
index 5f7fd9b1..4b087cd1 100644
--- a/torchquantum/operator/standard_gates/qubit_unitary.py
+++ b/torchquantum/operator/standard_gates/qubit_unitary.py
@@ -1,11 +1,12 @@
-from ..op_types import *
 from abc import ABCMeta
-from torchquantum.macro import C_DTYPE
-import torchquantum as tq
+
+import numpy as np
 import torch
-from torchquantum.functional import mat_dict
+
 import torchquantum.functional as tqf
-import numpy as np
+from torchquantum.macro import C_DTYPE
+
+from ..op_types import *
 
 
 class QubitUnitary(Operation, metaclass=ABCMeta):
@@ -118,7 +119,7 @@ def from_controlled_operation(
         n_wires = n_c_wires + n_t_wires
 
         # compute the new unitary, then permute
-        unitary = torch.tensor(torch.zeros(2**n_wires, 2**n_wires, dtype=C_DTYPE))
+        unitary = torch.zeros(2**n_wires, 2**n_wires, dtype=C_DTYPE)
         for k in range(2**n_wires - 2**n_t_wires):
             unitary[k, k] = 1.0 + 0.0j
 
diff --git a/torchquantum/plugin/qiskit/qiskit_plugin.py b/torchquantum/plugin/qiskit/qiskit_plugin.py
index bca3a7d2..97b7943b 100644
--- a/torchquantum/plugin/qiskit/qiskit_plugin.py
+++ b/torchquantum/plugin/qiskit/qiskit_plugin.py
@@ -21,25 +21,31 @@
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.
 """
+from __future__ import annotations
 
+from typing import Iterable, Optional
+
+import numpy as np
+import qiskit
+import qiskit.circuit.library.standard_gates as qiskit_gate
+import symengine
+import sympy
 import torch
+from qiskit import ClassicalRegister, QuantumCircuit, transpile
+from qiskit.circuit import CircuitInstruction, Parameter
+from qiskit.circuit.parameter import ParameterExpression
+from qiskit.circuit.parametervector import ParameterVectorElement
+from qiskit_aer import AerSimulator
+from torchpack.utils.logging import logger
+
 import torchquantum as tq
 import torchquantum.functional as tqf
-import qiskit.circuit.library.standard_gates as qiskit_gate
-import numpy as np
-
-from qiskit import QuantumCircuit, ClassicalRegister
-from qiskit import Aer, execute
-from qiskit.circuit import Parameter
-from torchpack.utils.logging import logger
+from torchquantum.functional import mat_dict
 from torchquantum.util import (
-    switch_little_big_endian_matrix,
     find_global_phase,
+    switch_little_big_endian_matrix,
     switch_little_big_endian_state,
 )
-from typing import Iterable, List
-from torchquantum.functional import mat_dict
-
 
 __all__ = [
     "tq2qiskit",
@@ -63,7 +69,7 @@ def qiskit2tq_op_history(circ):
     if getattr(circ, "_layout", None) is not None:
         try:
             p2v_orig = circ._layout.final_layout.get_physical_bits().copy()
-        except:
+        except AttributeError:
             p2v_orig = circ._layout.get_physical_bits().copy()
         p2v = {}
         for p, v in p2v_orig.items():
@@ -79,13 +85,15 @@ def qiskit2tq_op_history(circ):
     ops = []
     for gate in circ.data:
         op_name = gate[0].name
-        wires = list(map(lambda x: x.index, gate[1]))
+        wires = [circ.find_bit(qb).index for qb in gate.qubits]
         wires = [p2v[wire] for wire in wires]
         # sometimes the gate.params is ParameterExpression class
         init_params = (
             list(map(float, gate[0].params)) if len(gate[0].params) > 0 else None
         )
-        print(op_name,)
+        print(
+            op_name,
+        )
 
         if op_name in [
             "h",
@@ -104,12 +112,12 @@ def qiskit2tq_op_history(circ):
         ]:
             ops.append(
                 {
-                "name": op_name,  # type: ignore
-                "wires": np.array(wires),
-                "params": None,
-                "inverse": False,
-                "trainable": False,
-            }
+                    "name": op_name,  # type: ignore
+                    "wires": np.array(wires),
+                    "params": None,
+                    "inverse": False,
+                    "trainable": False,
+                }
             )
         elif op_name in [
             "rx",
@@ -138,12 +146,13 @@ def qiskit2tq_op_history(circ):
         ]:
             ops.append(
                 {
-                "name": op_name,  # type: ignore
-                "wires": np.array(wires),
-                "params": init_params,
-                "inverse": False,
-                "trainable": True
-            })
+                    "name": op_name,  # type: ignore
+                    "wires": np.array(wires),
+                    "params": init_params,
+                    "inverse": False,
+                    "trainable": True,
+                }
+            )
         elif op_name in ["barrier", "measure"]:
             continue
         else:
@@ -206,7 +215,10 @@ def append_parameterized_gate(func, circ, input_idx, params, wires):
         )
     elif func == "u2":
         from qiskit.circuit.library import U2Gate
-        circ.append(U2Gate(phi=params[input_idx[0]], lam=params[input_idx[1]]), wires, [])
+
+        circ.append(
+            U2Gate(phi=params[input_idx[0]], lam=params[input_idx[1]]), wires, []
+        )
         # circ.u2(phi=params[input_idx[0]], lam=params[input_idx[1]], qubit=wires[0])
     elif func == "u3":
         circ.u(
@@ -224,7 +236,7 @@ def append_parameterized_gate(func, circ, input_idx, params, wires):
         )
     else:
         raise NotImplementedError(
-            f"{func} cannot be converted to " f"parameterized Qiskit QuantumCircuit"
+            f"{func} cannot be converted to parameterized Qiskit QuantumCircuit"
         )
 
 
@@ -251,7 +263,7 @@ def append_fixed_gate(circ, func, params, wires, inverse):
     elif func == "sx":
         circ.sx(*wires)
     elif func in ["cnot", "cx"]:
-        circ.cnot(*wires)
+        circ.cx(*wires)
     elif func == "cz":
         circ.cz(*wires)
     elif func == "cy":
@@ -297,6 +309,7 @@ def append_fixed_gate(circ, func, params, wires, inverse):
         circ.cu1(params, *wires)
     elif func == "u2":
         from qiskit.circuit.library import U2Gate
+
         circ.append(U2Gate(phi=params[0], lam=params[1]), wires, [])
         # circ.u2(*list(params), *wires)
     elif func == "u3":
@@ -336,7 +349,7 @@ def append_fixed_gate(circ, func, params, wires, inverse):
 
 
 def tq2qiskit_initialize(q_device: tq.QuantumDevice, all_states):
-    """Call the qiskit initialize funtion and encoder the current quantum state
+    """Call the qiskit initialize function and encoder the current quantum state
      using initialize and return circuits
 
     Args:
@@ -436,7 +449,7 @@ def tq2qiskit(
             # generate only one qiskit QuantumCircuit
             assert module.params is None or module.params.shape[0] == 1
         except AssertionError:
-            logger.exception(f"Cannot convert batch model tq module")
+            logger.exception("Cannot convert batch model tq module")
 
     n_removed_ops = 0
 
@@ -489,7 +502,7 @@ def tq2qiskit(
         elif module.name == "SX":
             circ.sx(*module.wires)
         elif module.name == "CNOT":
-            circ.cnot(*module.wires)
+            circ.cx(*module.wires)
         elif module.name == "CZ":
             circ.cz(*module.wires)
         elif module.name == "CY":
@@ -535,7 +548,15 @@ def tq2qiskit(
             circ.cu1(module.params[0][0].item(), *module.wires)
         elif module.name == "U2":
             from qiskit.circuit.library import U2Gate
-            circ.append(U2Gate(phi=module.params[0].data.cpu().numpy()[0], lam=module.params[0].data.cpu().numpy()[0]), module.wires, [])
+
+            circ.append(
+                U2Gate(
+                    phi=module.params[0].data.cpu().numpy()[0],
+                    lam=module.params[0].data.cpu().numpy()[0],
+                ),
+                module.wires,
+                [],
+            )
             # circ.u2(*list(module.params[0].data.cpu().numpy()), *module.wires)
         elif module.name == "U3":
             circ.u3(*list(module.params[0].data.cpu().numpy()), *module.wires)
@@ -579,7 +600,7 @@ def tq2qiskit(
 
     if n_removed_ops > 0:
         logger.warning(
-            f"Remove {n_removed_ops} operations with small " f"parameter magnitude."
+            f"Remove {n_removed_ops} operations with small parameter magnitude."
         )
 
     return circ
@@ -665,11 +686,9 @@ def op_history2qiskit_expand_params(n_wires, op_history, bsz):
                 param = op["params"][i]
             else:
                 param = None
-            
-            append_fixed_gate(
-                circ, op["name"], param, op["wires"], op["inverse"]
-            )
-            
+
+            append_fixed_gate(circ, op["name"], param, op["wires"], op["inverse"])
+
         circs_all.append(circ)
 
     return circs_all
@@ -677,14 +696,14 @@ def op_history2qiskit_expand_params(n_wires, op_history, bsz):
 
 # construct a tq QuantumModule object according to the qiskit QuantumCircuit
 # object
-def qiskit2tq_Operator(circ: QuantumCircuit):
+def qiskit2tq_Operator(circ: QuantumCircuit, initial_parameters=None):
     if getattr(circ, "_layout", None) is not None:
         try:
             p2v_orig = circ._layout.final_layout.get_physical_bits().copy()
-        except:
+        except AttributeError:
             try:
                 p2v_orig = circ._layout.get_physical_bits().copy()
-            except:
+            except AttributeError:
                 p2v_orig = circ._layout.initial_layout.get_physical_bits().copy()
         p2v = {}
         for p, v in p2v_orig.items():
@@ -697,14 +716,23 @@ def qiskit2tq_Operator(circ: QuantumCircuit):
         for p in range(circ.num_qubits):
             p2v[p] = p
 
+    if initial_parameters is None:
+        initial_parameters = torch.nn.init.uniform_(
+            torch.ones(len(circ.parameters)), -np.pi, np.pi
+        )
+
+    param_to_index = {}
+    for i, param in enumerate(circ.parameters):
+        param_to_index[param] = i
+
     ops = []
     for gate in circ.data:
         op_name = gate[0].name
-        wires = list(map(lambda x: x.index, gate[1]))
+        wires = [circ.find_bit(qb).index for qb in gate.qubits]
         wires = [p2v[wire] for wire in wires]
-        # sometimes the gate.params is ParameterExpression class
-        init_params = (
-            list(map(float, gate[0].params)) if len(gate[0].params) > 0 else None
+
+        init_params = qiskit2tq_translate_qiskit_params(
+            gate, initial_parameters, param_to_index
         )
 
         if op_name in [
@@ -762,12 +790,57 @@ def qiskit2tq_Operator(circ: QuantumCircuit):
             raise NotImplementedError(
                 f"{op_name} conversion to tq is currently not supported."
             )
-    
+
     return ops
 
 
-def qiskit2tq(circ: QuantumCircuit):
-    ops = qiskit2tq_Operator(circ)
+def qiskit2tq_translate_qiskit_params(
+    circuit_instruction: CircuitInstruction, initial_parameters, param_to_index
+):
+    parameters = []
+    for p in circuit_instruction.operation.params:
+        if isinstance(p, Parameter) or isinstance(p, ParameterVectorElement):
+            parameters.append(initial_parameters[param_to_index[p]])
+        elif isinstance(p, ParameterExpression):
+            if len(p.parameters) == 0:
+                parameters.append(float(p))
+                continue
+
+            expr = p.sympify().simplify()
+            if isinstance(expr, symengine.Expr):  # qiskit uses symengine if available
+                expr = expr._sympy_()  # sympy.Expr
+
+            for free_symbol in expr.free_symbols:
+                # replace names: theta[0] -> theta_0
+                # ParameterVector creates symbols with brackets like theta[0]
+                # but sympy.lambdify does not allow brackets in symbol names
+                free_symbol.name = free_symbol.name.replace("[", "_").replace("]", "")
+
+            parameter_list = list(p.parameters)
+            sympy_symbols = [param._symbol_expr for param in parameter_list]
+            # replace names again: theta[0] -> theta_0
+            sympy_symbols = [
+                sympy.Symbol(str(symbol).replace("[", "_").replace("]", ""))
+                for symbol in sympy_symbols
+            ]
+            lam_f = sympy.lambdify(sympy_symbols, expr, modules="math")
+            parameters.append(
+                lam_f(
+                    *[
+                        initial_parameters[param_to_index[param]]
+                        for param in parameter_list
+                    ]
+                )
+            )
+        else:  # non-parameterized gate
+            parameters.append(p)
+    return parameters
+
+
+def qiskit2tq(
+    circ: QuantumCircuit, initial_parameters: Optional[list[torch.nn.Parameter]] = None
+):
+    ops = qiskit2tq_Operator(circ, initial_parameters)
     return tq.QuantumModuleFromOps(ops)
 
 
@@ -789,11 +862,11 @@ def test_qiskit2tq():
     circ.sx(3)
 
     circ.crx(theta=0.4, control_qubit=0, target_qubit=1)
-    circ.cnot(control_qubit=2, target_qubit=1)
+    circ.cx(control_qubit=2, target_qubit=1)
 
     circ.u3(theta=-0.1, phi=-0.2, lam=-0.4, qubit=3)
-    circ.cnot(control_qubit=3, target_qubit=0)
-    circ.cnot(control_qubit=0, target_qubit=2)
+    circ.cx(control_qubit=3, target_qubit=0)
+    circ.cx(control_qubit=0, target_qubit=2)
     circ.x(2)
     circ.x(3)
     circ.u2(phi=-0.2, lam=-0.9, qubit=3)
@@ -801,8 +874,10 @@ def test_qiskit2tq():
 
     m = qiskit2tq(circ)
 
-    simulator = Aer.get_backend("unitary_simulator")
-    result = execute(circ, simulator).result()
+    backend = AerSimulator(method="unitary")
+    circ = transpile(circ, backend)
+    circ.save_unitary()
+    result = backend.run(circ).result()
     unitary_qiskit = result.get_unitary(circ)
 
     unitary_tq = m.get_unitary(q_dev)
@@ -966,8 +1041,10 @@ def test_tq2qiskit():
 
     circuit = tq2qiskit(test_module, inputs)
 
-    simulator = Aer.get_backend("unitary_simulator")
-    result = execute(circuit, simulator).result()
+    backend = AerSimulator(method="unitary")
+    circuit = transpile(circuit, backend)
+    circuit.save_unitary()
+    result = backend.run(circuit).result()
     unitary_qiskit = result.get_unitary(circuit)
 
     unitary_tq = test_module.get_unitary(q_dev, inputs)
@@ -994,8 +1071,10 @@ def test_tq2qiskit_parameterized():
     for k, x in enumerate(inputs[0]):
         binds[params[k]] = x.item()
 
-    simulator = Aer.get_backend("unitary_simulator")
-    result = execute(circuit, simulator, parameter_binds=[binds]).result()
+    backend = AerSimulator(method="unitary")
+    circuit = transpile(circuit, backend)
+    circuit.save_unitary()
+    result = backend.run(circuit, parameter_binds=[binds]).result()
     unitary_qiskit = result.get_unitary(circuit)
 
     # print(unitary_qiskit)
diff --git a/torchquantum/plugin/qiskit/qiskit_processor.py b/torchquantum/plugin/qiskit/qiskit_processor.py
index 2d91e7c3..1a484d7b 100644
--- a/torchquantum/plugin/qiskit/qiskit_processor.py
+++ b/torchquantum/plugin/qiskit/qiskit_processor.py
@@ -22,34 +22,29 @@
 SOFTWARE.
 """
 
-import torch
-import torchquantum as tq
-import pathos.multiprocessing as multiprocessing
+import datetime
 import itertools
 
-from qiskit import Aer, execute, IBMQ, transpile, QuantumCircuit
-from qiskit.providers.aer.noise import NoiseModel
-from qiskit.tools.monitor import job_monitor
+import numpy as np
+import pathos.multiprocessing as multiprocessing
+import torch
+from qiskit import QuantumCircuit, transpile
 from qiskit.exceptions import QiskitError
-from .qiskit_plugin import (
-    tq2qiskit,
-    tq2qiskit_parameterized,
-    tq2qiskit_measurement,
-)
+from qiskit.transpiler import PassManager
+from qiskit_aer import AerSimulator
+from qiskit_aer.noise import NoiseModel
+from torchpack.utils.logging import logger
+from tqdm import tqdm
+
+import torchquantum as tq
 from torchquantum.util import (
+    get_circ_stats,
     get_expectations_from_counts,
-    get_provider,
     get_provider_hub_group_project,
-    get_circ_stats,
 )
-from .qiskit_macros import IBMQ_NAMES
-from tqdm import tqdm
-from torchpack.utils.logging import logger
-from qiskit.transpiler import PassManager
-import numpy as np
-import datetime
 
-from .my_job_monitor import my_job_monitor
+from .qiskit_macros import IBMQ_NAMES
+from .qiskit_plugin import tq2qiskit, tq2qiskit_measurement, tq2qiskit_parameterized
 
 
 class EmptyPassManager(PassManager):
@@ -60,13 +55,10 @@ def run(self, circuits, output_name: str = None, callback=None):
 def run_job_worker(data):
     while True:
         try:
-            job = execute(**(data[0]))
-            qiskit_verbose = data[1]
-            if qiskit_verbose:
-                job_monitor(job, interval=1)
+            job = AerSimulator(**(data))
             result = job.result()
             counts = result.get_counts()
-            # circ_num = len(data[0]['parameter_binds'])
+            # circ_num = len(data['parameter_binds'])
             # logger.info(
             #     f'run job worker successful, circuit number = {circ_num}')
             break
@@ -191,7 +183,6 @@ def qiskit_init(self):
 
         if self.backend is None:
             # initialize now
-            IBMQ.load_account()
             self.provider = get_provider_hub_group_project(
                 hub=self.hub,
                 group=self.group,
@@ -203,9 +194,7 @@ def qiskit_init(self):
                 self.coupling_map = self.get_coupling_map(self.backend_name)
             else:
                 # use simulator
-                self.backend = Aer.get_backend(
-                    "qasm_simulator", max_parallel_experiments=0
-                )
+                self.backend = AerSimulator(max_parallel_experiments=0)
                 self.noise_model = self.get_noise_model(self.noise_model_name)
                 self.coupling_map = self.get_coupling_map(self.coupling_map_name)
                 self.basis_gates = self.get_basis_gates(self.basis_gates_name)
@@ -320,7 +309,6 @@ def process_parameterized(
                     for i in range(0, len(binds_all), chunk_size)
                 ]
 
-            qiskit_verbose = self.max_jobs <= 6
             feed_dicts = []
             for split_bind in split_binds:
                 feed_dict = {
@@ -332,7 +320,7 @@ def process_parameterized(
                     "noise_model": self.noise_model,
                     "parameter_binds": split_bind,
                 }
-                feed_dicts.append([feed_dict, qiskit_verbose])
+                feed_dicts.append(feed_dict)
 
             p = multiprocessing.Pool(self.max_jobs)
             results = p.map(run_job_worker, feed_dicts)
@@ -345,16 +333,14 @@ def process_parameterized(
                     results[-1] = [results[-1]]
                 counts = list(itertools.chain(*results))
         else:
-            job = execute(
-                experiments=transpiled_circ,
-                backend=self.backend,
+            job = self.backend.run(
+                transpiled_circ,
                 pass_manager=self.empty_pass_manager,
                 shots=self.n_shots,
                 seed_simulator=self.seed_simulator,
                 noise_model=self.noise_model,
                 parameter_binds=binds_all,
             )
-            job_monitor(job, interval=1)
 
             result = job.result()
             counts = result.get_counts()
@@ -497,7 +483,6 @@ def process_parameterized_and_shift(
                 for i in range(0, len(binds_all), chunk_size)
             ]
 
-            qiskit_verbose = self.max_jobs <= 6
             feed_dicts = []
             for split_bind in split_binds:
                 feed_dict = {
@@ -509,7 +494,7 @@ def process_parameterized_and_shift(
                     "noise_model": self.noise_model,
                     "parameter_binds": split_bind,
                 }
-                feed_dicts.append([feed_dict, qiskit_verbose])
+                feed_dicts.append(feed_dict)
 
             p = multiprocessing.Pool(self.max_jobs)
             results = p.map(run_job_worker, feed_dicts)
@@ -533,16 +518,15 @@ def process_parameterized_and_shift(
             for circ in split_circs:
                 while True:
                     try:
-                        job = execute(
-                            experiments=circ,
-                            backend=self.backend,
+                        job = self.backend.run(
+                            circ,
                             pass_manager=self.empty_pass_manager,
                             shots=self.n_shots,
                             seed_simulator=self.seed_simulator,
                             noise_model=self.noise_model,
                             parameter_binds=binds_all,
                         )
-                        job_monitor(job, interval=1)
+
                         result = (
                             job.result()
                         )  # qiskit.providers.ibmq.job.exceptions.IBMQJobFailureError:Job has failed. Use the error_message() method to get more details
@@ -555,7 +539,7 @@ def process_parameterized_and_shift(
                         # total_cont += 1
                         # print(total_time_spent / total_cont)
                         break
-                    except (QiskitError) as e:
+                    except QiskitError as e:
                         logger.warning("Job failed, rerun now.")
                         print(e.message)
 
@@ -613,7 +597,6 @@ def process_multi_measure(
             circ_all[i : i + chunk_size] for i in range(0, len(circ_all), chunk_size)
         ]
 
-        qiskit_verbose = self.max_jobs <= 2
         feed_dicts = []
         for split_circ in split_circs:
             feed_dict = {
@@ -624,7 +607,7 @@ def process_multi_measure(
                 "seed_simulator": self.seed_simulator,
                 "noise_model": self.noise_model,
             }
-            feed_dicts.append([feed_dict, qiskit_verbose])
+            feed_dicts.append(feed_dict)
 
         p = multiprocessing.Pool(self.max_jobs)
         results = p.map(run_job_worker, feed_dicts)
@@ -661,9 +644,8 @@ def process(
         transpiled_circs = self.transpile(circs)
         self.transpiled_circs = transpiled_circs
 
-        job = execute(
-            experiments=transpiled_circs,
-            backend=self.backend,
+        job = self.backend.run(
+            transpiled_circs,
             shots=self.n_shots,
             # initial_layout=self.initial_layout,
             seed_transpiler=self.seed_transpiler,
@@ -673,7 +655,6 @@ def process(
             noise_model=self.noise_model,
             optimization_level=self.optimization_level,
         )
-        job_monitor(job, interval=1)
 
         result = job.result()
         counts = result.get_counts()
@@ -704,7 +685,6 @@ def process_ready_circs_get_counts(self, circs_all, parallel=True):
                 for i in range(0, len(circs_all), chunk_size)
             ]
 
-            qiskit_verbose = self.max_jobs <= 6
             feed_dicts = []
             for split_circ in split_circs:
                 feed_dict = {
@@ -715,7 +695,7 @@ def process_ready_circs_get_counts(self, circs_all, parallel=True):
                     "seed_simulator": self.seed_simulator,
                     "noise_model": self.noise_model,
                 }
-                feed_dicts.append([feed_dict, qiskit_verbose])
+                feed_dicts.append(feed_dict)
 
             p = multiprocessing.Pool(self.max_jobs)
             results = p.map(run_job_worker, feed_dicts)
@@ -728,15 +708,13 @@ def process_ready_circs_get_counts(self, circs_all, parallel=True):
                     results[-1] = [results[-1]]
                 counts = list(itertools.chain(*results))
         else:
-            job = execute(
-                experiments=circs_all,
-                backend=self.backend,
+            job = self.backend.run(
+                circs_all,
                 pass_manager=self.empty_pass_manager,
                 shots=self.n_shots,
                 seed_simulator=self.seed_simulator,
                 noise_model=self.noise_model,
             )
-            job_monitor(job, interval=1)
 
             result = job.result()
             counts = [result.get_counts()]
@@ -758,9 +736,9 @@ def process_circs_get_joint_expval(self, circs_all, observable, parallel=True):
         for circ_ in circs_all:
             circ = circ_.copy()
             for k, obs in enumerate(observable):
-                if obs == 'X':
+                if obs == "X":
                     circ.h(k)
-                elif obs == 'Y':
+                elif obs == "Y":
                     circ.z(k)
                     circ.s(k)
                     circ.h(k)
@@ -771,8 +749,10 @@ def process_circs_get_joint_expval(self, circs_all, observable, parallel=True):
 
         mask = np.ones(len(observable), dtype=bool)
         mask[np.array([*observable]) == "I"] = False
-    
-        counts = self.process_ready_circs_get_counts(circs_all_diagonalized, parallel=parallel)
+
+        counts = self.process_ready_circs_get_counts(
+            circs_all_diagonalized, parallel=parallel
+        )
 
         # here we need to switch the little and big endian of distribution bitstrings
         distributions = []
@@ -786,19 +766,25 @@ def process_circs_get_joint_expval(self, circs_all, observable, parallel=True):
             n_eigen_one = 0
             n_eigen_minus_one = 0
             for bitstring, n_count in distri.items():
-                if np.dot(list(map(lambda x: eval(x), [*bitstring])), mask).sum() % 2 == 0:
+                if (
+                    np.dot(list(map(lambda x: eval(x), [*bitstring])), mask).sum() % 2
+                    == 0
+                ):
                     n_eigen_one += n_count
                 else:
                     n_eigen_minus_one += n_count
-            
-            expval = n_eigen_one / self.n_shots + (-1) * n_eigen_minus_one / self.n_shots
+
+            expval = (
+                n_eigen_one / self.n_shots + (-1) * n_eigen_minus_one / self.n_shots
+            )
             expval_all.append(expval)
 
         return expval_all
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     import pdb
+
     pdb.set_trace()
     circ = QuantumCircuit(3)
     circ.h(0)
@@ -806,11 +792,9 @@ def process_circs_get_joint_expval(self, circs_all, observable, parallel=True):
     circ.cx(1, 2)
     circ.rx(0.1, 0)
 
-    qiskit_processor = QiskitProcessor(
-        use_real_qc=False
-    )
+    qiskit_processor = QiskitProcessor(use_real_qc=False)
 
-    qiskit_processor.process_circs_get_joint_expval([circ], 'XII')
+    qiskit_processor.process_circs_get_joint_expval([circ], "XII")
 
     qdev = tq.QuantumDevice(n_wires=3, bsz=1)
     qdev.h(0)
@@ -819,5 +803,5 @@ def process_circs_get_joint_expval(self, circs_all, observable, parallel=True):
     qdev.rx(0, 0.1)
 
     from torchquantum.measurement import expval_joint_sampling
-    print(expval_joint_sampling(qdev, 'XII', n_shots=8192))
 
+    print(expval_joint_sampling(qdev, "XII", n_shots=8192))
diff --git a/torchquantum/plugin/qiskit/qiskit_pulse.py b/torchquantum/plugin/qiskit/qiskit_pulse.py
index b9c78760..ab28774f 100644
--- a/torchquantum/plugin/qiskit/qiskit_pulse.py
+++ b/torchquantum/plugin/qiskit/qiskit_pulse.py
@@ -22,12 +22,8 @@
 SOFTWARE.
 """
 
-import torch
-import torchquantum as tq
-from qiskit import pulse, QuantumCircuit
-from qiskit.pulse import library
-from qiskit.test.mock import FakeQuito, FakeArmonk, FakeBogota
-from qiskit.compiler import assemble, schedule
+from qiskit import pulse
+
 from .qiskit_macros import IBMQ_PNAMES
 
 
diff --git a/torchquantum/plugin/qiskit/qiskit_unitary_gate.py b/torchquantum/plugin/qiskit/qiskit_unitary_gate.py
index ce46ff04..b60427dd 100644
--- a/torchquantum/plugin/qiskit/qiskit_unitary_gate.py
+++ b/torchquantum/plugin/qiskit/qiskit_unitary_gate.py
@@ -15,19 +15,16 @@
 """
 
 from collections import OrderedDict
-import numpy
 
-from qiskit.circuit import Gate, ControlledGate
-from qiskit.circuit import QuantumCircuit
-from qiskit.circuit import QuantumRegister, Qubit
-from qiskit.circuit.exceptions import CircuitError
+import numpy
+import qiskit
+from qiskit.circuit import ControlledGate, Gate, QuantumCircuit, QuantumRegister, Qubit
 from qiskit.circuit._utils import _compute_control_matrix
+from qiskit.circuit.exceptions import CircuitError
 from qiskit.circuit.library.standard_gates import U3Gate
-from qiskit.quantum_info.operators.predicates import matrix_equal
-from qiskit.quantum_info.operators.predicates import is_unitary_matrix
-from qiskit.quantum_info import OneQubitEulerDecomposer
-from qiskit.quantum_info.synthesis.two_qubit_decompose import two_qubit_cnot_decompose
-from qiskit.extensions.exceptions import ExtensionError
+from qiskit.quantum_info.operators.predicates import is_unitary_matrix, matrix_equal
+from qiskit.synthesis import OneQubitEulerDecomposer
+from qiskit.synthesis.two_qubit.two_qubit_decompose import two_qubit_cnot_decompose
 
 _DECOMPOSER1Q = OneQubitEulerDecomposer("U3")
 
@@ -58,12 +55,12 @@ def __init__(self, data, label=None):
         data = numpy.array(data, dtype=complex)
         # Check input is unitary
         if not is_unitary_matrix(data, atol=1e-5):
-            raise ExtensionError("Input matrix is not unitary.")
+            raise ValueError("Input matrix is not unitary.")
         # Check input is N-qubit matrix
         input_dim, output_dim = data.shape
         num_qubits = int(numpy.log2(input_dim))
         if input_dim != output_dim or 2**num_qubits != input_dim:
-            raise ExtensionError("Input matrix is not an N-qubit operator.")
+            raise ValueError("Input matrix is not an N-qubit operator.")
 
         self._qasm_name = None
         self._qasm_definition = None
@@ -116,7 +113,9 @@ def _define(self):
         else:
             q = QuantumRegister(self.num_qubits, "q")
             qc = QuantumCircuit(q, name=self.name)
-            qc.append(qiskit.circuit.library.Isometry(self.to_matrix(), 0, 0), qargs=q[:])
+            qc.append(
+                qiskit.circuit.library.Isometry(self.to_matrix(), 0, 0), qargs=q[:]
+            )
             self.definition = qc
 
     def control(self, num_ctrl_qubits=1, label=None, ctrl_state=None):
@@ -155,7 +154,7 @@ def control(self, num_ctrl_qubits=1, label=None, ctrl_state=None):
         pmat = Operator(iso.inverse()).data @ cmat
         diag = numpy.diag(pmat)
         if not numpy.allclose(diag, diag[0]):
-            raise ExtensionError("controlled unitary generation failed")
+            raise ValueError("controlled unitary generation failed")
         phase = numpy.angle(diag[0])
         if phase:
             # need to apply to _definition since open controls creates temporary definition
diff --git a/torchquantum/plugin/qiskit_pulse.py b/torchquantum/plugin/qiskit_pulse.py
index 81775b0d..30a4b162 100644
--- a/torchquantum/plugin/qiskit_pulse.py
+++ b/torchquantum/plugin/qiskit_pulse.py
@@ -1,10 +1,6 @@
-import torch
-import torchquantum as tq
-from qiskit import pulse, QuantumCircuit
-from qiskit.pulse import library
-from qiskit.test.mock import FakeQuito, FakeArmonk, FakeBogota
-from qiskit.compiler import assemble, schedule
-from .qiskit_macros import IBMQ_PNAMES
+from qiskit import pulse
+
+from .qiskit.qiskit_macros import IBMQ_PNAMES
 
 
 def circ2pulse(circuits, name):
@@ -24,7 +20,7 @@ def circ2pulse(circuits, name):
         >>> qc.cx(0, 1)
         >>> circ2pulse(qc, 'ibmq_oslo')
     """
-    
+
     if name in IBMQ_PNAMES:
         backend = name()
         with pulse.build(backend) as pulse_tq:
diff --git a/torchquantum/pulse/pulse_utils.py b/torchquantum/pulse/pulse_utils.py
index 68c66568..51803ab0 100644
--- a/torchquantum/pulse/pulse_utils.py
+++ b/torchquantum/pulse/pulse_utils.py
@@ -23,55 +23,30 @@
 """
 
 import copy
-import sched
-import qiskit
-import itertools
-import numpy as np
+from typing import Union
 
-from itertools import repeat
-from qiskit.providers import aer
-from qiskit.providers.fake_provider import *
-from qiskit.circuit import Gate
+import numpy as np
+import qiskit
+from qiskit import QuantumCircuit, pulse
 from qiskit.compiler import assemble
-from qiskit import pulse, QuantumCircuit, IBMQ
+from qiskit.providers.fake_provider import *
+from qiskit.pulse import Schedule
 from qiskit.pulse.instructions import Instruction
 from qiskit.pulse.transforms import block_to_schedule
-from qiskit_nature.drivers import UnitsType, Molecule
-from scipy.optimize import minimize, LinearConstraint
-from qiskit_nature.converters.second_quantization import QubitConverter
-from qiskit_nature.properties.second_quantization.electronic import ParticleNumber
-from qiskit_nature.problems.second_quantization import ElectronicStructureProblem
-from typing import List, Tuple, Iterable, Union, Dict, Callable, Set, Optional, Any
-from qiskit.pulse import (
-    Schedule,
-    GaussianSquare,
-    Drag,
-    Delay,
-    Play,
-    ControlChannel,
-    DriveChannel,
-)
-from qiskit_nature.mappers.second_quantization import ParityMapper, JordanWignerMapper
-from qiskit_nature.transformers.second_quantization.electronic import (
-    ActiveSpaceTransformer,
-)
-from qiskit_nature.drivers.second_quantization import (
-    ElectronicStructureDriverType,
-    ElectronicStructureMoleculeDriver,
-)
+from scipy.optimize import LinearConstraint
 
 
 def is_parametric_pulse(t0, *inst: Union["Schedule", Instruction]):
     """
-        Check if the instruction is a parametric pulse.
+    Check if the instruction is a parametric pulse.
 
-        Args:
-            t0 (tuple): Tuple containing the time and instruction.
-            inst (tuple): Tuple containing the instruction.
+    Args:
+        t0 (tuple): Tuple containing the time and instruction.
+        inst (tuple): Tuple containing the instruction.
 
-        Returns:
-            bool: True if the instruction is a parametric pulse, False otherwise.
-        """
+    Returns:
+        bool: True if the instruction is a parametric pulse, False otherwise.
+    """
     inst = t0[1]
     t0 = t0[0]
     if isinstance(inst, pulse.Play):
@@ -82,14 +57,14 @@ def is_parametric_pulse(t0, *inst: Union["Schedule", Instruction]):
 
 def extract_ampreal(pulse_prog):
     """
-       Extract the real part of pulse amplitudes from the pulse program.
+    Extract the real part of pulse amplitudes from the pulse program.
 
-       Args:
-           pulse_prog (Schedule): The pulse program.
+    Args:
+        pulse_prog (Schedule): The pulse program.
 
-       Returns:
-           np.array: Array of real parts of pulse amplitudes.
-       """
+    Returns:
+        np.array: Array of real parts of pulse amplitudes.
+    """
     # extract the real part of pulse amplitude, igonred the imaginary part.
     amp_list = list(
         map(
@@ -104,14 +79,14 @@ def extract_ampreal(pulse_prog):
 
 def extract_amp(pulse_prog):
     """
-        Extract the pulse amplitudes from the pulse program.
+    Extract the pulse amplitudes from the pulse program.
 
-        Args:
-            pulse_prog (Schedule): The pulse program.
+    Args:
+        pulse_prog (Schedule): The pulse program.
 
-        Returns:
-            np.array: Array of pulse amplitudes.
-        """
+    Returns:
+        np.array: Array of pulse amplitudes.
+    """
     # extract the pulse amplitdue.
     amp_list = list(
         map(
@@ -132,15 +107,15 @@ def extract_amp(pulse_prog):
 
 def is_phase_pulse(t0, *inst: Union["Schedule", Instruction]):
     """
-        Check if the instruction is a phase pulse.
+    Check if the instruction is a phase pulse.
 
-        Args:
-            t0 (tuple): Tuple containing the time and instruction.
-            inst (tuple): Tuple containing the instruction.
+    Args:
+        t0 (tuple): Tuple containing the time and instruction.
+        inst (tuple): Tuple containing the instruction.
 
-        Returns:
-            bool: True if the instruction is a phase pulse, False otherwise.
-        """
+    Returns:
+        bool: True if the instruction is a phase pulse, False otherwise.
+    """
     inst = t0[1]
     t0 = t0[0]
     if isinstance(inst, pulse.ShiftPhase):
@@ -150,14 +125,14 @@ def is_phase_pulse(t0, *inst: Union["Schedule", Instruction]):
 
 def extract_phase(pulse_prog):
     """
-        Extract the phase values from the pulse program.
+    Extract the phase values from the pulse program.
 
-        Args:
-            pulse_prog (Schedule): The pulse program.
+    Args:
+        pulse_prog (Schedule): The pulse program.
 
-        Returns:
-            list: List of phase values.
-        """
+    Returns:
+        list: List of phase values.
+    """
 
     for _, ShiftPhase in pulse_prog.filter(is_phase_pulse).instructions:
         # print(play.pulse.amp)
@@ -175,15 +150,15 @@ def extract_phase(pulse_prog):
 
 def cir2pul(circuit, backend):
     """
-        Transform a quantum circuit to a pulse schedule.
+    Transform a quantum circuit to a pulse schedule.
 
-        Args:
-            circuit (QuantumCircuit): The quantum circuit.
-            backend: The backend for the pulse schedule.
+    Args:
+        circuit (QuantumCircuit): The quantum circuit.
+        backend: The backend for the pulse schedule.
 
-        Returns:
-            Schedule: The pulse schedule.
-        """
+    Returns:
+        Schedule: The pulse schedule.
+    """
     # transform quantum circuit to pulse schedule
     with pulse.build(backend) as pulse_prog:
         pulse.call(circuit)
@@ -192,15 +167,15 @@ def cir2pul(circuit, backend):
 
 def snp(qubit, backend):
     """
-        Create a Schedule for the simultaneous z measurement of a qubit and a control qubit.
+    Create a Schedule for the simultaneous z measurement of a qubit and a control qubit.
 
-        Args:
-            qubit (int): The target qubit.
-            backend: The backend for the pulse schedule.
+    Args:
+        qubit (int): The target qubit.
+        backend: The backend for the pulse schedule.
 
-        Returns:
-            Schedule: The pulse schedule for simultaneous z measurement.
-        """
+    Returns:
+        Schedule: The pulse schedule for simultaneous z measurement.
+    """
     circuit = QuantumCircuit(qubit + 1)
     circuit.h(qubit)
     sched = cir2pul(circuit, backend)
@@ -210,16 +185,16 @@ def snp(qubit, backend):
 
 def tnp(qubit, cqubit, backend):
     """
-        Create a Schedule for the simultaneous controlled-x measurement of a qubit and a control qubit.
+    Create a Schedule for the simultaneous controlled-x measurement of a qubit and a control qubit.
 
-        Args:
-            qubit (int): The target qubit.
-            cqubit (int): The control qubit.
-            backend: The backend for the pulse schedule.
+    Args:
+        qubit (int): The target qubit.
+        cqubit (int): The control qubit.
+        backend: The backend for the pulse schedule.
 
-        Returns:
-            Schedule: The pulse schedule for simultaneous controlled-x measurement.
-        """
+    Returns:
+        Schedule: The pulse schedule for simultaneous controlled-x measurement.
+    """
     circuit = QuantumCircuit(cqubit + 1)
     circuit.cx(qubit, cqubit)
     sched = cir2pul(circuit, backend)
@@ -229,30 +204,30 @@ def tnp(qubit, cqubit, backend):
 
 def pul_append(sched1, sched2):
     """
-        Append two pulse schedules.
+    Append two pulse schedules.
 
-        Args:
-            sched1 (Schedule): The first pulse schedule.
-            sched2 (Schedule): The second pulse schedule.
+    Args:
+        sched1 (Schedule): The first pulse schedule.
+        sched2 (Schedule): The second pulse schedule.
 
-        Returns:
-            Schedule: The combined pulse schedule.
-        """
+    Returns:
+        Schedule: The combined pulse schedule.
+    """
     sched = sched1.append(sched2)
     return sched
 
 
 def map_amp(pulse_ansatz, modified_list):
     """
-        Map modified pulse amplitudes to the pulse ansatz.
+    Map modified pulse amplitudes to the pulse ansatz.
 
-        Args:
-            pulse_ansatz (Schedule): The pulse ansatz.
-            modified_list (list): List of modified pulse amplitudes.
+    Args:
+        pulse_ansatz (Schedule): The pulse ansatz.
+        modified_list (list): List of modified pulse amplitudes.
 
-        Returns:
-            Schedule: The pulse schedule with modified amplitudes.
-        """
+    Returns:
+        Schedule: The pulse schedule with modified amplitudes.
+    """
     sched = Schedule()
     for inst, amp in zip(
         pulse_ansatz.filter(is_parametric_pulse).instructions, modified_list
@@ -274,18 +249,18 @@ def get_from(d: dict, key: str):
 
 def run_pulse_sim(measurement_pulse):
     """
-        Run pulse simulations for the given measurement pulses.
+    Run pulse simulations for the given measurement pulses.
 
-        Args:
-            measurement_pulse (list): List of measurement pulses.
+    Args:
+        measurement_pulse (list): List of measurement pulses.
 
-        Returns:
-            list: List of measurement results.
-        """
+    Returns:
+        list: List of measurement results.
+    """
     measure_result = []
     for measure_pulse in measurement_pulse:
         shots = 1024
-        pulse_sim = qiskit.providers.aer.PulseSimulator.from_backend(FakeJakarta())
+        pulse_sim = qiskit_aer.PulseSimulator.from_backend(FakeJakarta())
         pul_sim = assemble(
             measure_pulse,
             backend=pulse_sim,
@@ -306,14 +281,14 @@ def run_pulse_sim(measurement_pulse):
 
 def gen_LC(parameters_array):
     """
-        Generate linear constraints for the optimization.
+    Generate linear constraints for the optimization.
 
-        Args:
-            parameters_array (np.array): Array of parameters.
+    Args:
+        parameters_array (np.array): Array of parameters.
 
-        Returns:
-            LinearConstraint: Linear constraint for the optimization.
-        """
+    Returns:
+        LinearConstraint: Linear constraint for the optimization.
+    """
     dim_design = int(len(parameters_array))
     Mid = int(len(parameters_array) / 2)
     bound = np.ones((dim_design, 2)) * np.array([0, 0.9])
@@ -327,15 +302,15 @@ def gen_LC(parameters_array):
 
 def observe_genearte(pulse_ansatz, backend):
     """
-        Generate measurement pulses for observing the pulse ansatz.
+    Generate measurement pulses for observing the pulse ansatz.
 
-        Args:
-            pulse_ansatz (Schedule): The pulse ansatz.
-            backend: The backend for the pulse schedule.
+    Args:
+        pulse_ansatz (Schedule): The pulse ansatz.
+        backend: The backend for the pulse schedule.
 
-        Returns:
-            list: List of measurement pulses.
-        """
+    Returns:
+        list: List of measurement pulses.
+    """
     qubits = 0, 1
     with pulse.build(backend) as pulse_measurez0:
         # z measurement of qubit 0 and 1
diff --git a/torchquantum/pulse/quantum_pulse_simulation.py b/torchquantum/pulse/quantum_pulse_simulation.py
new file mode 100644
index 00000000..703f097d
--- /dev/null
+++ b/torchquantum/pulse/quantum_pulse_simulation.py
@@ -0,0 +1,188 @@
+import torch
+import torch.optim as optim
+import torchquantum as tq
+import torchquantum.functional as tqf
+import numpy as np
+
+class QuantumPulseDemo(tq.QuantumModule):
+    """
+    Quantum pulse demonstration module.
+
+    This module defines a parameterized quantum pulse and applies it to a quantum device.
+    """
+
+    def __init__(self):
+        """
+        Initializes the QuantumPulseDemo module.
+
+        Args:
+            n_wires (int): Number of quantum wires (qubits).
+            n_steps (int): Number of steps for the quantum pulse.
+            hamil (list): Hamiltonian for the quantum pulse.
+        """
+        super().__init__()
+        self.n_wires = 2
+
+        # Quantum encoder
+        self.encoder = tq.GeneralEncoder([
+            {'input_idx': [0], 'func': 'rx', 'wires': [0]},
+            {'input_idx': [1], 'func': 'rx', 'wires': [1]}
+        ])
+
+        # Define parameterized quantum pulse
+        self.pulse = tq.pulse.QuantumPulseDirect(n_steps=4, hamil=[[0, 1], [1, 0]])
+
+    def forward(self, x):
+        """
+        Forward pass through the QuantumPulseDemo module.
+
+        Args:
+            x (torch.Tensor): Input tensor.
+
+        Returns:
+            torch.Tensor: Measurement result from the quantum device.
+        """
+        qdev = tq.QuantumDevice(n_wires=self.n_wires, bsz=x.shape[0], device=x.device)
+        self.encoder(qdev, x)
+        self.apply_pulse(qdev)
+        return tq.measure(qdev)
+
+    def apply_pulse(self, qdev):
+        """
+        Applies the parameterized quantum pulse to the quantum device.
+
+        Args:
+            qdev (tq.QuantumDevice): Quantum device to apply the pulse to.
+        """
+        pulse_params = self.pulse.pulse_shape.detach().cpu().numpy()
+        # Apply pulse to the quantum device (adjust based on actual pulse application logic)
+        for params in pulse_params:
+            tqf.rx(qdev, wires=0, params=params)
+            tqf.rx(qdev, wires=1, params=params)
+
+class OM_EOM_Simulation:
+    """
+    Optical modulation with electro-optic modulator (EOM) simulation.
+
+    This class simulates a sequence of optical pulses with or without EOM modulation.
+    """
+
+    def __init__(self, pulse_duration, modulation_bandwidth=None, eom_mode=False):
+        """
+        Initializes the OM_EOM_Simulation.
+
+        Args:
+            pulse_duration (float): Duration of each pulse.
+            modulation_bandwidth (float, optional): Bandwidth of modulation. Defaults to None.
+            eom_mode (bool, optional): Whether to simulate EOM mode. Defaults to False.
+        """
+        self.pulse_duration = pulse_duration
+        self.modulation_bandwidth = modulation_bandwidth
+        self.eom_mode = eom_mode
+
+    def simulate_sequence(self):
+        """
+        Simulates a sequence of optical pulses with specified parameters.
+
+        Returns:
+            list: Sequence of pulses and delays.
+        """
+        # Initialize the sequence
+        sequence = []
+
+        # Add pulses and delays to the sequence
+        if self.modulation_bandwidth:
+            # Apply modulation bandwidth effect
+            sequence.append(('Delay', 0))
+            sequence.append(('Pulse', 'NoisyChannel'))
+        for _ in range(3):
+            # Apply pulses with specified duration
+            sequence.append(('Delay', self.pulse_duration))
+            if self.eom_mode:
+                # Apply EOM mode operation
+                sequence.append(('Pulse', 'EOM'))
+            else:
+                # Apply regular pulse
+                sequence.append(('Pulse', 'Regular'))
+            # Apply a delay between pulses
+            sequence.append(('Delay', 0))
+
+        return sequence
+
+class QuantumPulseDemoRunner:
+    """
+    Runner for training the QuantumPulseDemo model and simulating the OM_EOM_Simulation.
+    """
+
+    def __init__(self, pulse_duration, modulation_bandwidth=None, eom_mode=False):
+        """
+        Initializes the QuantumPulseDemoRunner.
+
+        Args:
+            pulse_duration (float): Duration of each pulse.
+            modulation_bandwidth (float, optional): Bandwidth of modulation. Defaults to None.
+            eom_mode (bool, optional): Whether to simulate EOM mode. Defaults to False.
+        """
+        self.model = QuantumPulseDemo()
+        self.optimizer = optim.Adam(params=self.model.pulse.parameters(), lr=5e-3)
+        self.target_unitary = self._initialize_target_unitary()
+        self.simulator = OM_EOM_Simulation(pulse_duration, modulation_bandwidth, eom_mode)
+
+    def _initialize_target_unitary(self):
+        """
+        Initializes the target unitary matrix.
+
+        Returns:
+            torch.Tensor: Target unitary matrix.
+        """
+        theta = 0.6
+        return torch.tensor(
+            [
+                [np.cos(theta / 2), -1j * np.sin(theta / 2)],
+                [-1j * np.sin(theta / 2), np.cos(theta / 2)],
+            ],
+            dtype=torch.complex64,
+        )
+
+    def train(self, epochs=1000):
+        """
+        Trains the QuantumPulseDemo model.
+
+        Args:
+            epochs (int, optional): Number of training epochs. Defaults to 1000.
+        """
+        for epoch in range(epochs):
+            x = torch.tensor([[np.pi, np.pi]], dtype=torch.float32)
+
+            qdev = self.model(x)
+
+            loss = (
+                1
+                - (
+                    torch.trace(self.model.pulse.get_unitary() @ self.target_unitary)
+                    / self.target_unitary.shape[0]
+                ).abs()
+                ** 2
+            )
+
+            self.optimizer.zero_grad()
+            loss.backward()
+            self.optimizer.step()
+
+            if epoch % 100 == 0:
+                print(f'Epoch {epoch}, Loss: {loss.item()}')
+                print('Current Pulse Shape:', self.model.pulse.pulse_shape)
+                print('Current Unitary:\n', self.model.pulse.get_unitary())
+
+    def run_simulation(self):
+        """
+        Runs the OM_EOM_Simulation.
+        """
+        sequence = self.simulator.simulate_sequence()
+        for step in sequence:
+            print(step)
+
+# Example usage
+runner = QuantumPulseDemoRunner(pulse_duration=100, modulation_bandwidth=5, eom_mode=True)
+runner.train()
+runner.run_simulation()
diff --git a/torchquantum/pulse/templates/pulse_utils.py b/torchquantum/pulse/templates/pulse_utils.py
index bad2a9b5..30d4c2f7 100644
--- a/torchquantum/pulse/templates/pulse_utils.py
+++ b/torchquantum/pulse/templates/pulse_utils.py
@@ -1,40 +1,15 @@
 import copy
-import sched
-import qiskit
-import itertools
-import numpy as np
+from typing import Union
 
-from itertools import repeat
-from qiskit.providers import aer
-from qiskit.providers.fake_provider import *
-from qiskit.circuit import Gate
+import numpy as np
+import qiskit
+from qiskit import QuantumCircuit, pulse
 from qiskit.compiler import assemble
-from qiskit import pulse, QuantumCircuit, IBMQ
+from qiskit.providers.fake_provider import *
+from qiskit.pulse import Schedule
 from qiskit.pulse.instructions import Instruction
 from qiskit.pulse.transforms import block_to_schedule
-from qiskit_nature.drivers import UnitsType, Molecule
-from scipy.optimize import minimize, LinearConstraint
-from qiskit_nature.converters.second_quantization import QubitConverter
-from qiskit_nature.properties.second_quantization.electronic import ParticleNumber
-from qiskit_nature.problems.second_quantization import ElectronicStructureProblem
-from typing import List, Tuple, Iterable, Union, Dict, Callable, Set, Optional, Any
-from qiskit.pulse import (
-    Schedule,
-    GaussianSquare,
-    Drag,
-    Delay,
-    Play,
-    ControlChannel,
-    DriveChannel,
-)
-from qiskit_nature.mappers.second_quantization import ParityMapper, JordanWignerMapper
-from qiskit_nature.transformers.second_quantization.electronic import (
-    ActiveSpaceTransformer,
-)
-from qiskit_nature.drivers.second_quantization import (
-    ElectronicStructureDriverType,
-    ElectronicStructureMoleculeDriver,
-)
+from scipy.optimize import LinearConstraint
 
 
 def is_parametric_pulse(t0, *inst: Union["Schedule", Instruction]):
@@ -154,7 +129,7 @@ def run_pulse_sim(measurement_pulse):
     measure_result = []
     for measure_pulse in measurement_pulse:
         shots = 1024
-        pulse_sim = qiskit.providers.aer.PulseSimulator.from_backend(FakeJakarta())
+        pulse_sim = qiskit_aer.PulseSimulator.from_backend(FakeJakarta())
         pul_sim = assemble(
             measure_pulse,
             backend=pulse_sim,
diff --git a/torchquantum/util/utils.py b/torchquantum/util/utils.py
index caeee471..23f67e5b 100644
--- a/torchquantum/util/utils.py
+++ b/torchquantum/util/utils.py
@@ -22,27 +22,26 @@
 SOFTWARE.
 """
 
+from __future__ import annotations
+
 import copy
-from typing import Dict, Iterable, List, TYPE_CHECKING
+from typing import TYPE_CHECKING, Iterable
 
 import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from opt_einsum import contract
-from qiskit_ibm_runtime import QiskitRuntimeService
 from qiskit.exceptions import QiskitError
-from qiskit.providers.aer.noise.device.parameters import gate_error_values
 from torchpack.utils.config import Config
 from torchpack.utils.logging import logger
 
 import torchquantum as tq
 from torchquantum.macro import C_DTYPE
 
-
 if TYPE_CHECKING:
-    from torchquantum.module import QuantumModule
     from torchquantum.device import QuantumDevice
+    from torchquantum.module import QuantumModule
 else:
     QuantumModule = None
     QuantumDevice = None
@@ -98,14 +97,14 @@ def pauli_eigs(n) -> np.ndarray:
 
 def diag(x):
     """
-        Compute the diagonal matrix from a given input tensor.
+    Compute the diagonal matrix from a given input tensor.
 
-        Args:
-            x (torch.Tensor): Input tensor.
+    Args:
+        x (torch.Tensor): Input tensor.
 
-        Returns:
-            torch.Tensor: Diagonal matrix with the diagonal elements from the input tensor.
-        """
+    Returns:
+        torch.Tensor: Diagonal matrix with the diagonal elements from the input tensor.
+    """
     # input tensor, output tensor with diagonal as the input
     # manual implementation because torch.diag does not support autograd of
     # complex number
@@ -120,20 +119,21 @@ def diag(x):
 
 class Timer(object):
     """
-       Timer class to measure the execution time of a code block.
+    Timer class to measure the execution time of a code block.
 
-       Args:
-           device (str): Device to use for timing. Can be "gpu" or "cpu".
-           name (str): Name of the task being measured.
-           times (int): Number of times the task will be executed.
+    Args:
+        device (str): Device to use for timing. Can be "gpu" or "cpu".
+        name (str): Name of the task being measured.
+        times (int): Number of times the task will be executed.
 
-       Example:
-           # Measure the execution time of a code block on the GPU
-           with Timer(device="gpu", name="MyTask", times=100):
-               # Code block to be measured
-               ...
+    Example:
+        # Measure the execution time of a code block on the GPU
+        with Timer(device="gpu", name="MyTask", times=100):
+            # Code block to be measured
+            ...
+
+    """
 
-       """
     def __init__(self, device="gpu", name="", times=100):
         self.device = device
         self.name = name
@@ -158,20 +158,20 @@ def __exit__(self, exc_type, exc_value, tb):
 
 def get_unitary_loss(model: nn.Module):
     """
-        Calculate the unitary loss of a model.
+    Calculate the unitary loss of a model.
 
-        The unitary loss measures the deviation of the trainable unitary matrices
-        in the model from the identity matrix.
+    The unitary loss measures the deviation of the trainable unitary matrices
+    in the model from the identity matrix.
 
-        Args:
-            model (nn.Module): The model containing trainable unitary matrices.
+    Args:
+        model (nn.Module): The model containing trainable unitary matrices.
 
-        Returns:
-            torch.Tensor: The unitary loss.
+    Returns:
+        torch.Tensor: The unitary loss.
 
-        Example:
-            loss = get_unitary_loss(model)
-        """
+    Example:
+        loss = get_unitary_loss(model)
+    """
     loss = 0
     for name, params in model.named_parameters():
         if "TrainableUnitary" in name:
@@ -187,21 +187,21 @@ def get_unitary_loss(model: nn.Module):
 
 def legalize_unitary(model: nn.Module):
     """
-        Legalize the unitary matrices in the model.
+    Legalize the unitary matrices in the model.
 
-        The function modifies the trainable unitary matrices in the model by applying
-        singular value decomposition (SVD) and reassembling the matrices using the
-        reconstructed singular values.
+    The function modifies the trainable unitary matrices in the model by applying
+    singular value decomposition (SVD) and reassembling the matrices using the
+    reconstructed singular values.
 
-        Args:
-            model (nn.Module): The model containing trainable unitary matrices.
+    Args:
+        model (nn.Module): The model containing trainable unitary matrices.
 
-        Returns:
-            None
+    Returns:
+        None
 
-        Example:
-            legalize_unitary(model)
-        """
+    Example:
+        legalize_unitary(model)
+    """
     with torch.no_grad():
         for name, params in model.named_parameters():
             if "TrainableUnitary" in name:
@@ -212,22 +212,22 @@ def legalize_unitary(model: nn.Module):
 
 def switch_little_big_endian_matrix(mat):
     """
-        Switches the little-endian and big-endian order of a multi-dimensional matrix.
+    Switches the little-endian and big-endian order of a multi-dimensional matrix.
 
-        The function reshapes the input matrix to a 2D or multi-dimensional matrix with dimensions
-        that are powers of 2. It then switches the order of the dimensions, effectively changing
-        the little-endian order to big-endian, or vice versa. The function can handle both
-        batched and non-batched matrices.
+    The function reshapes the input matrix to a 2D or multi-dimensional matrix with dimensions
+    that are powers of 2. It then switches the order of the dimensions, effectively changing
+    the little-endian order to big-endian, or vice versa. The function can handle both
+    batched and non-batched matrices.
 
-        Args:
-            mat (numpy.ndarray): The input matrix.
+    Args:
+        mat (numpy.ndarray): The input matrix.
 
-        Returns:
-            numpy.ndarray: The matrix with the switched endian order.
+    Returns:
+        numpy.ndarray: The matrix with the switched endian order.
 
-        Example:
-            switched_mat = switch_little_big_endian_matrix(mat)
-        """
+    Example:
+        switched_mat = switch_little_big_endian_matrix(mat)
+    """
     if len(mat.shape) % 2 == 1:
         is_batch_matrix = True
         bsz = mat.shape[0]
@@ -251,25 +251,25 @@ def switch_little_big_endian_matrix(mat):
 
 def switch_little_big_endian_state(state):
     """
-       Switches the little-endian and big-endian order of a quantum state vector.
+    Switches the little-endian and big-endian order of a quantum state vector.
 
-       The function reshapes the input state vector to a 1D or multi-dimensional state vector with
-       dimensions that are powers of 2. It then switches the order of the dimensions, effectively
-       changing the little-endian order to big-endian, or vice versa. The function can handle both
-       batched and non-batched state vectors.
+    The function reshapes the input state vector to a 1D or multi-dimensional state vector with
+    dimensions that are powers of 2. It then switches the order of the dimensions, effectively
+    changing the little-endian order to big-endian, or vice versa. The function can handle both
+    batched and non-batched state vectors.
 
-       Args:
-           state (numpy.ndarray): The input state vector.
+    Args:
+        state (numpy.ndarray): The input state vector.
 
-       Returns:
-           numpy.ndarray: The state vector with the switched endian order.
+    Returns:
+        numpy.ndarray: The state vector with the switched endian order.
 
-       Raises:
-           ValueError: If the dimension of the state vector is not 1 or 2.
+    Raises:
+        ValueError: If the dimension of the state vector is not 1 or 2.
 
-       Example:
-           switched_state = switch_little_big_endian_state(state)
-       """
+    Example:
+        switched_state = switch_little_big_endian_state(state)
+    """
 
     if len(state.shape) > 1:
         is_batch_state = True
@@ -279,7 +279,7 @@ def switch_little_big_endian_state(state):
         is_batch_state = False
         reshape = [2] * int(np.log2(state.size))
     else:
-        logger.exception(f"Dimension of statevector should be 1 or 2")
+        logger.exception("Dimension of statevector should be 1 or 2")
         raise ValueError
 
     original_shape = state.shape
@@ -310,25 +310,25 @@ def switch_little_big_endian_state_test():
 
 def get_expectations_from_counts(counts, n_wires):
     """
-       Calculate expectation values from counts.
+    Calculate expectation values from counts.
 
-       This function takes a counts dictionary or a list of counts dictionaries
-       and calculates the expectation values based on the probability of measuring
-       the state '1' on each wire. The expectation values are computed as the
-       flipped difference between the probability of measuring '1' and the probability
-       of measuring '0' on each wire.
+    This function takes a counts dictionary or a list of counts dictionaries
+    and calculates the expectation values based on the probability of measuring
+    the state '1' on each wire. The expectation values are computed as the
+    flipped difference between the probability of measuring '1' and the probability
+    of measuring '0' on each wire.
 
-       Args:
-           counts (dict or list[dict]): The counts dictionary or a list of counts dictionaries.
-           n_wires (int): The number of wires.
+    Args:
+        counts (dict or list[dict]): The counts dictionary or a list of counts dictionaries.
+        n_wires (int): The number of wires.
 
-       Returns:
-           numpy.ndarray: The expectation values.
+    Returns:
+        numpy.ndarray: The expectation values.
 
-       Example:
-           counts = {'000': 10, '100': 5, '010': 15}
-           expectations = get_expectations_from_counts(counts, 3)
-       """
+    Example:
+        counts = {'000': 10, '100': 5, '010': 15}
+        expectations = get_expectations_from_counts(counts, 3)
+    """
     exps = []
     if isinstance(counts, dict):
         counts = [counts]
@@ -349,29 +349,33 @@ def get_expectations_from_counts(counts, n_wires):
 
 def find_global_phase(mat1, mat2, threshold):
     """
-        Find a numerical stable global phase between two matrices.
-
-        This function compares the elements of two matrices `mat1` and `mat2`
-        and identifies a numerical stable global phase by finding the first
-        non-zero element pair with absolute values greater than the specified
-        threshold. The global phase is calculated as the ratio of the corresponding
-        elements in `mat2` and `mat1`.
-
-        Args:
-            mat1 (numpy.ndarray): The first matrix.
-            mat2 (numpy.ndarray): The second matrix.
-            threshold (float): The threshold for identifying non-zero elements.
-
-        Returns:
-            float or None: The global phase ratio if a numerical stable phase is found,
-                None otherwise.
-
-        Example:
-            mat1 = np.array([[1+2j, 0+1j], [0-1j, 2+3j]])
-            mat2 = np.array([[2+4j, 0+2j], [0-2j, 4+6j]])
-            threshold = 0.5
-            global_phase = find_global_phase(mat1, mat2, threshold)
-        """
+    Find a numerical stable global phase between two matrices.
+
+    This function compares the elements of two matrices `mat1` and `mat2`
+    and identifies a numerical stable global phase by finding the first
+    non-zero element pair with absolute values greater than the specified
+    threshold. The global phase is calculated as the ratio of the corresponding
+    elements in `mat2` and `mat1`.
+
+    Args:
+        mat1 (numpy.ndarray): The first matrix.
+        mat2 (numpy.ndarray): The second matrix.
+        threshold (float): The threshold for identifying non-zero elements.
+
+    Returns:
+        float or None: The global phase ratio if a numerical stable phase is found,
+            None otherwise.
+
+    Example:
+        mat1 = np.array([[1+2j, 0+1j], [0-1j, 2+3j]])
+        mat2 = np.array([[2+4j, 0+2j], [0-2j, 4+6j]])
+        threshold = 0.5
+        global_phase = find_global_phase(mat1, mat2, threshold)
+    """
+    if not isinstance(mat1, np.ndarray):
+        mat1 = np.asarray(mat1)
+    if not isinstance(mat2, np.ndarray):
+        mat2 = np.asarray(mat2)
     for i in range(mat1.shape[0]):
         for j in range(mat1.shape[1]):
             # find a numerical stable global phase
@@ -380,7 +384,7 @@ def find_global_phase(mat1, mat2, threshold):
     return None
 
 
-def build_module_op_list(m: QuantumModule, x=None) -> List:
+def build_module_op_list(m: QuantumModule, x=None) -> list:
     """
     serialize all operations in the module and generate a list with
     [{'name': RX, 'has_params': True, 'trainable': True, 'wires': [0],
@@ -435,39 +439,39 @@ def build_module_op_list(m: QuantumModule, x=None) -> List:
 
 
 def build_module_from_op_list(
-    op_list: List[Dict], remove_ops=False, thres=None
+    op_list: list[dict], remove_ops=False, thres=None
 ) -> QuantumModule:
     """
-       Build a quantum module from an operation list.
-
-       This function takes an operation list, which contains dictionaries representing
-       quantum operations, and constructs a quantum module from those operations.
-       The module can optionally remove operations based on certain criteria, such as
-       low parameter values. The removed operations can be counted and logged.
-
-       Args:
-           op_list (List[Dict]): The operation list, where each dictionary represents
-               an operation with keys: "name", "has_params", "trainable", "wires",
-               "n_wires", and "params".
-           remove_ops (bool): Whether to remove operations based on certain criteria.
-               Defaults to False.
-           thres (float): The threshold for removing operations. If a parameter value
-               is smaller in absolute value than this threshold, the corresponding
-               operation is removed. Defaults to None, in which case a threshold of
-               1e-5 is used.
-
-       Returns:
-           QuantumModule: The constructed quantum module.
-
-       Example:
-           op_list = [
-               {"name": "RX", "has_params": True, "trainable": True, "wires": [0], "n_wires": 2, "params": [0.5]},
-               {"name": "CNOT", "has_params": False, "trainable": False, "wires": [0, 1], "n_wires": 2, "params": None},
-               {"name": "RY", "has_params": True, "trainable": True, "wires": [1], "n_wires": 2, "params": [1.2]},
-           ]
-           module = build_module_from_op_list(op_list, remove_ops=True, thres=0.1)
-       """
-    logger.info(f"Building module from op_list...")
+    Build a quantum module from an operation list.
+
+    This function takes an operation list, which contains dictionaries representing
+    quantum operations, and constructs a quantum module from those operations.
+    The module can optionally remove operations based on certain criteria, such as
+    low parameter values. The removed operations can be counted and logged.
+
+    Args:
+        op_list (List[Dict]): The operation list, where each dictionary represents
+            an operation with keys: "name", "has_params", "trainable", "wires",
+            "n_wires", and "params".
+        remove_ops (bool): Whether to remove operations based on certain criteria.
+            Defaults to False.
+        thres (float): The threshold for removing operations. If a parameter value
+            is smaller in absolute value than this threshold, the corresponding
+            operation is removed. Defaults to None, in which case a threshold of
+            1e-5 is used.
+
+    Returns:
+        QuantumModule: The constructed quantum module.
+
+    Example:
+        op_list = [
+            {"name": "RX", "has_params": True, "trainable": True, "wires": [0], "n_wires": 2, "params": [0.5]},
+            {"name": "CNOT", "has_params": False, "trainable": False, "wires": [0, 1], "n_wires": 2, "params": None},
+            {"name": "RY", "has_params": True, "trainable": True, "wires": [1], "n_wires": 2, "params": [1.2]},
+        ]
+        module = build_module_from_op_list(op_list, remove_ops=True, thres=0.1)
+    """
+    logger.info("Building module from op_list...")
     thres = 1e-5 if thres is None else thres
     n_removed_ops = 0
     ops = []
@@ -499,38 +503,38 @@ def build_module_from_op_list(
     if n_removed_ops > 0:
         logger.warning(f"Remove in total {n_removed_ops} pruned operations.")
     else:
-        logger.info(f"Do not remove any operations.")
+        logger.info("Do not remove any operations.")
 
     return tq.QuantumModuleFromOps(ops)
 
 
 def build_module_description_test():
     """
-        Test function for building module descriptions.
-
-        This function demonstrates the usage of `build_module_op_list` and `build_module_from_op_list`
-        functions to build module descriptions and create quantum modules from those descriptions.
-
-        Example:
-            import pdb
-            from torchquantum.plugins import tq2qiskit
-            from examples.core.models.q_models import QFCModel12
-
-            pdb.set_trace()
-            q_model = QFCModel12({"n_blocks": 4})
-            desc = build_module_op_list(q_model.q_layer)
-            print(desc)
-            q_dev = tq.QuantumDevice(n_wires=4)
-            m = build_module_from_op_list(desc)
-            tq2qiskit(q_dev, m, draw=True)
-
-            desc = build_module_op_list(
-                tq.RandomLayerAllTypes(n_ops=200, wires=[0, 1, 2, 3], qiskit_compatible=True)
-            )
-            print(desc)
-            m1 = build_module_from_op_list(desc)
-            tq2qiskit(q_dev, m1, draw=True)
-        """
+    Test function for building module descriptions.
+
+    This function demonstrates the usage of `build_module_op_list` and `build_module_from_op_list`
+    functions to build module descriptions and create quantum modules from those descriptions.
+
+    Example:
+        import pdb
+        from torchquantum.plugins import tq2qiskit
+        from examples.core.models.q_models import QFCModel12
+
+        pdb.set_trace()
+        q_model = QFCModel12({"n_blocks": 4})
+        desc = build_module_op_list(q_model.q_layer)
+        print(desc)
+        q_dev = tq.QuantumDevice(n_wires=4)
+        m = build_module_from_op_list(desc)
+        tq2qiskit(q_dev, m, draw=True)
+
+        desc = build_module_op_list(
+            tq.RandomLayerAllTypes(n_ops=200, wires=[0, 1, 2, 3], qiskit_compatible=True)
+        )
+        print(desc)
+        m1 = build_module_from_op_list(desc)
+        tq2qiskit(q_dev, m1, draw=True)
+    """
     import pdb
 
     from torchquantum.plugin import tq2qiskit
@@ -560,7 +564,7 @@ def get_p_v_reg_mapping(circ):
     """
     try:
         p2v_orig = circ._layout.final_layout.get_physical_bits().copy()
-    except:
+    except AttributeError:
         p2v_orig = circ._layout.get_physical_bits().copy()
     mapping = {
         "p2v": {},
@@ -601,7 +605,7 @@ def get_v_c_reg_mapping(circ):
     """
     try:
         p2v_orig = circ._layout.final_layout.get_physical_bits().copy()
-    except:
+    except AttributeError:
         p2v_orig = circ._layout.get_physical_bits().copy()
     p2v = {}
     for p, v in p2v_orig.items():
@@ -630,15 +634,15 @@ def get_v_c_reg_mapping(circ):
 
 def get_cared_configs(conf, mode) -> Config:
     """
-        Get the relevant configurations based on the mode.
+    Get the relevant configurations based on the mode.
 
-        Args:
-            conf (Config): The configuration object.
-            mode (str): The mode indicating the desired configuration.
+    Args:
+        conf (Config): The configuration object.
+        mode (str): The mode indicating the desired configuration.
 
-        Returns:
-            Config: The modified configuration object with only the relevant configurations preserved.
-        """
+    Returns:
+        Config: The modified configuration object with only the relevant configurations preserved.
+    """
 
     conf = copy.deepcopy(conf)
     ignores = [
@@ -706,55 +710,39 @@ def get_cared_configs(conf, mode) -> Config:
 
 def get_success_rate(properties, transpiled_circ):
     """
-        Estimate the success rate of a transpiled quantum circuit.
-
-        Args:
-            properties (list): List of gate error properties.
-            transpiled_circ (QuantumCircuit): The transpiled quantum circuit.
-
-        Returns:
-            float: The estimated success rate.
-        """
-    # estimate the success rate according to the error rates of single and
-    # two-qubit gates in transpiled circuits
-
-    gate_errors = gate_error_values(properties)
-    # construct the error dict
-    gate_error_dict = {}
-    for gate_error in gate_errors:
-        if gate_error[0] not in gate_error_dict.keys():
-            gate_error_dict[gate_error[0]] = {tuple(gate_error[1]): gate_error[2]}
-        else:
-            gate_error_dict[gate_error[0]][tuple(gate_error[1])] = gate_error[2]
+    Estimate the success rate of a transpiled quantum circuit.
 
-    success_rate = 1
-    for gate in transpiled_circ.data:
-        gate_success_rate = (
-            1 - gate_error_dict[gate[0].name][tuple(map(lambda x: x.index, gate[1]))]
-        )
-        if gate_success_rate == 0:
-            gate_success_rate = 1e-5
-        success_rate *= gate_success_rate
+    Args:
+        properties (list): List of gate error properties.
+        transpiled_circ (QuantumCircuit): The transpiled quantum circuit.
+
+    Returns:
+        float: The estimated success rate.
+    """
+    raise NotImplementedError
 
-    return success_rate
 
 def get_provider(backend_name, hub=None):
     """
-        Get the provider object for a specific backend from IBM Quantum.
+    Get the provider object for a specific backend from IBM Quantum.
 
-        Args:
-            backend_name (str): Name of the backend.
-            hub (str): Optional hub name.
+    Args:
+        backend_name (str): Name of the backend.
+        hub (str): Optional hub name.
 
-        Returns:
-            IBMQProvider: The provider object.
-        """
+    Returns:
+        IBMQProvider: The provider object.
+    """
     # mass-inst-tech-1 or MIT-1
     if backend_name in ["ibmq_casablanca", "ibmq_rome", "ibmq_bogota", "ibmq_jakarta"]:
         if hub == "mass" or hub is None:
-            provider = QiskitRuntimeService(channel = "ibm_quantum", instance = "ibm-q-research/mass-inst-tech-1/main")
+            provider = QiskitRuntimeService(
+                channel="ibm_quantum", instance="ibm-q-research/mass-inst-tech-1/main"
+            )
         elif hub == "mit":
-            provider = QiskitRuntimeService(channel = "ibm_quantum", instance = "ibm-q-research/MIT-1/main")
+            provider = QiskitRuntimeService(
+                channel="ibm_quantum", instance="ibm-q-research/MIT-1/main"
+            )
         else:
             raise ValueError(f"not supported backend {backend_name} in hub " f"{hub}")
     elif backend_name in [
@@ -764,38 +752,51 @@ def get_provider(backend_name, hub=None):
         "ibmq_guadalupe",
         "ibmq_montreal",
     ]:
-        provider = QiskitRuntimeService(channel = "ibm_quantum", instance = "ibm-q-ornl/anl/csc428")
+        provider = QiskitRuntimeService(
+            channel="ibm_quantum", instance="ibm-q-ornl/anl/csc428"
+        )
     else:
         if hub == "mass" or hub is None:
             try:
-                provider = QiskitRuntimeService(channel = "ibm_quantum", instance = "ibm-q-research/mass-inst-tech-1/main")
+                provider = QiskitRuntimeService(
+                    channel="ibm_quantum",
+                    instance="ibm-q-research/mass-inst-tech-1/main",
+                )
             except QiskitError:
                 # logger.warning(f"Cannot use MIT backend, roll back to open")
-                logger.warning(f"Use the open backend")
-                provider = QiskitRuntimeService(channel = "ibm_quantum", instance = "ibm-q/open/main")
+                logger.warning("Use the open backend")
+                provider = QiskitRuntimeService(
+                    channel="ibm_quantum", instance="ibm-q/open/main"
+                )
         elif hub == "mit":
-            provider = QiskitRuntimeService(channel = "ibm_quantum", instance = "ibm-q-research/MIT-1/main")
+            provider = QiskitRuntimeService(
+                channel="ibm_quantum", instance="ibm-q-research/MIT-1/main"
+            )
         else:
-            provider = QiskitRuntimeService(channel = "ibm_quantum", instance = "ibm-q/open/main")
+            provider = QiskitRuntimeService(
+                channel="ibm_quantum", instance="ibm-q/open/main"
+            )
 
     return provider
 
 
 def get_provider_hub_group_project(hub="ibm-q", group="open", project="main"):
-    provider = QiskitRuntimeService(channel = "ibm_quantum", instance = f"{hub}/{group}/{project}")
+    provider = QiskitRuntimeService(
+        channel="ibm_quantum", instance=f"{hub}/{group}/{project}"
+    )
     return provider
 
 
 def normalize_statevector(states):
     """
-       Normalize a statevector to ensure the square magnitude of the statevector sums to 1.
+    Normalize a statevector to ensure the square magnitude of the statevector sums to 1.
 
-       Args:
-           states (torch.Tensor): The statevector tensor.
+    Args:
+        states (torch.Tensor): The statevector tensor.
 
-       Returns:
-           torch.Tensor: The normalized statevector tensor.
-       """
+    Returns:
+        torch.Tensor: The normalized statevector tensor.
+    """
     # make sure the square magnitude of statevector sum to 1
     # states = states.contiguous()
     original_shape = states.shape
@@ -826,7 +827,7 @@ def get_circ_stats(circ):
 
     for gate in circ.data:
         op_name = gate[0].name
-        wires = list(map(lambda x: x.index, gate[1]))
+        wires = [circ.find_bit(qb).index for qb in gate.qubits]
         if op_name in n_gates_dict.keys():
             n_gates_dict[op_name] += 1
         else:
@@ -854,7 +855,7 @@ def get_circ_stats(circ):
 
 def partial_trace(
     q_device: QuantumDevice,
-    keep_indices: List[int],
+    keep_indices: list[int],
 ) -> torch.Tensor:
     """Returns a density matrix with only some qubits kept.
     Args:
@@ -957,22 +958,22 @@ def dm_to_mixture_of_state(dm: torch.Tensor, atol=1e-10):
 
 def partial_trace_test():
     """
-        Test function for performing partial trace on a quantum device.
+    Test function for performing partial trace on a quantum device.
 
-        This function demonstrates how to use the `partial_trace` function from `torchquantum.functional`
-        to perform partial trace on a quantum device.
+    This function demonstrates how to use the `partial_trace` function from `torchquantum.functional`
+    to perform partial trace on a quantum device.
 
-        The function applies Hadamard gate on the first qubit and a CNOT gate between the first and second qubits.
-        Then, it performs partial trace on the first qubit and converts the resulting density matrices into
-        mixtures of states.
+    The function applies Hadamard gate on the first qubit and a CNOT gate between the first and second qubits.
+    Then, it performs partial trace on the first qubit and converts the resulting density matrices into
+    mixtures of states.
 
-        Prints the resulting mixture of states.
+    Prints the resulting mixture of states.
 
-        Note: This function assumes that you have already imported the necessary modules and functions.
+    Note: This function assumes that you have already imported the necessary modules and functions.
 
-        Returns:
-            None
-        """
+    Returns:
+        None
+    """
     import torchquantum.functional as tqf
 
     n_wires = 4
@@ -987,7 +988,8 @@ def partial_trace_test():
 
     print(mixture)
 
-def pauli_string_to_matrix(pauli: str, device=torch.device('cpu')) -> torch.Tensor:
+
+def pauli_string_to_matrix(pauli: str, device=torch.device("cpu")) -> torch.Tensor:
     mat_dict = {
         "paulix": torch.tensor([[0, 1], [1, 0]], dtype=C_DTYPE),
         "pauliy": torch.tensor([[0, -1j], [1j, 0]], dtype=C_DTYPE),
@@ -1008,68 +1010,82 @@ def pauli_string_to_matrix(pauli: str, device=torch.device('cpu')) -> torch.Tens
         matrix = torch.kron(matrix, pauli_dict[op].to(device))
     return matrix
 
+
 if __name__ == "__main__":
     build_module_description_test()
     switch_little_big_endian_matrix_test()
     switch_little_big_endian_state_test()
 
 
-def parameter_shift_gradient(model, input_data, expectation_operator, shift_rate=np.pi*0.5, shots=1024):
-    ''' 
-    This function calculates the gradient of a parametrized circuit using the parameter shift rule to be fed into 
-    a classical optimizer, its formula is given by 
-    gradient for the ith parameter =( expectation_value(the_ith_parameter + shift_rate)-expectation_value(the_ith_parameter - shift_rate) ) *0.5 
-    Args: 
+def parameter_shift_gradient(
+    model, input_data, expectation_operator, shift_rate=np.pi * 0.5, shots=1024
+):
+    """
+    This function calculates the gradient of a parametrized circuit using the parameter shift rule to be fed into
+    a classical optimizer, its formula is given by
+    gradient for the ith parameter =( expectation_value(the_ith_parameter + shift_rate)-expectation_value(the_ith_parameter - shift_rate) ) *0.5
+    Args:
         model(tq.QuantumModule): the model that you want to use, which includes the quantum device and the parameters
         input(torch.tensor): the input data that you are using
-        expectation_operator(str): the observable that you want to calculate the expectation value of, usually the Z operator 
+        expectation_operator(str): the observable that you want to calculate the expectation value of, usually the Z operator
         (i.e 'ZZZ' for 3 qubits or 3 wires)
         shift_rate(float , optional): the rate that you would like to shift the parameter with at every iteration, by default pi*0.5
         shots(int , optional): the number of shots to use per parameter ,(for 10 parameters and 1024 shots = 10240 shots in total)
         by default = 1024.
     Returns:
-        torch.tensor : An array of the gradients of all the parameters in the circuit. 
-    '''
+        torch.tensor : An array of the gradients of all the parameters in the circuit.
+    """
     par_num = []
-    for p in model.parameters():#since the model.parameters() Returns an iterator over module parameters,to get the number of parameter i have to iterate over all of them
+    for (
+        p
+    ) in (
+        model.parameters()
+    ):  # since the model.parameters() Returns an iterator over module parameters,to get the number of parameter i have to iterate over all of them
         par_num.append(p)
     gradient_of_par = torch.zeros(len(par_num))
-                                        
-    def clone_model(model_to_clone):#i have to note:this clone_model function was made with GPT 
+
+    def clone_model(
+        model_to_clone,
+    ):  # i have to note:this clone_model function was made with GPT
         cloned_model = type(model_to_clone)()  # Create a new instance of the same class
-        cloned_model.load_state_dict(model_to_clone.state_dict())  # Copy the state dictionary
+        cloned_model.load_state_dict(
+            model_to_clone.state_dict()
+        )  # Copy the state dictionary
         return cloned_model
 
     # Clone the models
-    model_plus_shift  = clone_model(model)
+    model_plus_shift = clone_model(model)
     model_minus_shift = clone_model(model)
-    state_dict_plus_shift  = model_plus_shift.state_dict()
+    state_dict_plus_shift = model_plus_shift.state_dict()
     state_dict_minus_shift = model_minus_shift.state_dict()
     #####################
 
     for idx, key in enumerate(state_dict_plus_shift):
-        if idx < 2:  # Skip the first two keys because they are not paramters
+        if idx < 2:  # Skip the first two keys because they are not parameters
             continue
-        state_dict_plus_shift[key]  +=  shift_rate
-        state_dict_minus_shift[key] -=  shift_rate
-        
-        model_plus_shift.load_state_dict(state_dict_plus_shift  )
+        state_dict_plus_shift[key] += shift_rate
+        state_dict_minus_shift[key] -= shift_rate
+
+        model_plus_shift.load_state_dict(state_dict_plus_shift)
         model_minus_shift.load_state_dict(state_dict_minus_shift)
-        
+
         model_plus_shift.forward(input_data)
         model_minus_shift.forward(input_data)
-        
-        state_dict_plus_shift  = model_plus_shift.state_dict()
+
+        state_dict_plus_shift = model_plus_shift.state_dict()
         state_dict_minus_shift = model_minus_shift.state_dict()
-                
-        
-        
-        expectation_plus_shift = tq.expval_joint_sampling(model_plus_shift.q_device, observable=expectation_operator, n_shots=shots)
-        expectation_minus_shift = tq.expval_joint_sampling(model_minus_shift.q_device, observable=expectation_operator, n_shots=shots)
 
+        expectation_plus_shift = tq.expval_joint_sampling(
+            model_plus_shift.q_device, observable=expectation_operator, n_shots=shots
+        )
+        expectation_minus_shift = tq.expval_joint_sampling(
+            model_minus_shift.q_device, observable=expectation_operator, n_shots=shots
+        )
+
+        state_dict_plus_shift[key] -= shift_rate
+        state_dict_minus_shift[key] += shift_rate
 
-        state_dict_plus_shift[key]  -=  shift_rate
-        state_dict_minus_shift[key] +=  shift_rate
-        
-        gradient_of_par[idx-2] = (expectation_plus_shift - expectation_minus_shift) * 0.5
+        gradient_of_par[idx - 2] = (
+            expectation_plus_shift - expectation_minus_shift
+        ) * 0.5
     return gradient_of_par