Merge branch 'unitary-hack' into qiskit2tq-parameterexpression

.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

.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

.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/[email protected]

README.md

@@ -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
 - v0.1.8 Available!

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.

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()

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)