From 4f799b7fe14cdd950229801051da636db7ce87ea Mon Sep 17 00:00:00 2001 From: mlowe308 <66105625+mlowe308@users.noreply.github.com> Date: Tue, 6 Aug 2024 15:49:26 -0400 Subject: [PATCH] Add files via upload --- .../lab4/lab4-analysis_Lowe.ipynb | 971 ++++++++++++++++++ 1 file changed, 971 insertions(+) create mode 100644 solutions_from_participants/lab4/lab4-analysis_Lowe.ipynb diff --git a/solutions_from_participants/lab4/lab4-analysis_Lowe.ipynb b/solutions_from_participants/lab4/lab4-analysis_Lowe.ipynb new file mode 100644 index 0000000..8ff016c --- /dev/null +++ b/solutions_from_participants/lab4/lab4-analysis_Lowe.ipynb @@ -0,0 +1,971 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "00dee99f-b89f-460d-bcb9-a76f50db8c93", + "metadata": {}, + "source": [ + "#### Here are my results for Lab 4 but I do not know if I did the work correctly. If you have any feedback, please let me know. Also if you have physical intuition about this problem, I would appreciate hearing about it.\n", + "#### mlowe@loyola.edu" + ] + }, + { + "cell_type": "markdown", + "id": "ef5370aa-ee29-439c-958c-8a35feeebd9d", + "metadata": {}, + "source": [ + "# Lab 4 Simulating Nature at Utility Scale\n", + "\n", + "In this lab we will explore utility scale work by simulating the dynamics of a large Heisenberg spin chain. The goal is to measure the dynamics of $Z_i$ for a given site as a function of time and external field $h$ for two different phases of the spin chain.\n", + "\n", + "This lab will be broken into sections matching the Qiskit Patterns framework which include the following steps:\n", + "\n", + "1. Map the system to quantum circuits and operators\n", + "2. Optimize the circuits to be executed\n", + "3. Execute the time evolution circuits\n", + "4. Analyze or post-process the results\n", + "\n", + "### Introduction\n", + "The Heisenberg model, introduced in the late 1920s, is a popular model used to study magnetic phenomena and phase transitions in many-body systems. It is related to its more simplified cousin, the Ising model, and examines the dynamics that emerge from what is known as the *exchange interaction*. This interaction arises from a combination of the Pauli exclusion principle and the Coulomb interaction [[1]](https://doi.org/10.1119/1.4798343) and has a Hamiltonian of the form:\n", + "\n", + "$$ H = \\sum_{i=1}^N\\left(J_x X_iX_{i+1} + J_y Y_iY_{i+1} + J_z Z_iZ_{i+1}\\right). $$\n", + "\n", + "Here $N$ is the number of sites in our chain and $X_i$, $Y_i$, and $Z_i$ are the Pauli operators which act on the $i^{th}$ site. The parameters $J_x$, $J_y$, and $J_z$, represent the coupling strength for the Coulomb ($J_x$ and $J_y$) and Ising ($J_z$) interaction. For the rest of the lab we'll consider $J_x=J_y=1$ for simplicity. \n", + "\n", + "In general this model has a few phases based on the ratio $\\Delta = J_z/J$ (also known as the anisotropy). In this lab we will explore two of them:\n", + "- The **istropic** phase where $\\Delta = 1$. This is also known as the **XXX** phase.\n", + "- The **anisotropic** phase when $\\Delta \\neq 1$.\n", + "\n", + "To measure some interesting dynamics of this system, we will also introduce a transverse magnetic field with a strength $h$ which will interact with each site through the Pauli $X$ operator. The Hamiltonian of our spin chain will now take the form:\n", + "\n", + "\\begin{align} \n", + "H =& \\sum_{i=1}^N\\left(J X_iX_{i+1} + J Y_iY_{i+1} + J_z Z_iZ_{i+1} + hX_i\\right) \\\\\n", + " =& \\sum_{i=1}^N\\left(X_iX_{i+1} + Y_iY_{i+1} + \\Delta Z_iZ_{i+1} + hX_i\\right)\n", + "\\end{align}\n", + "\n", + "where we have substituted $\\Delta = J_z/J$." + ] + }, + { + "cell_type": "markdown", + "id": "2c9bb9d8-313a-4ec8-9558-6ba723466f90", + "metadata": {}, + "source": [ + "## Step 0: Setup\n", + "\n", + "The code cells below will install all the required packages needed for this lab, you will also set your API token as an environment variable which the grader will need to save your progress." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "25596175-0fe5-4d69-9173-6db84f3dc4dc", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: qiskit==1.1.0 in 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pip install --upgrade pip\u001b[0m\n", + "Note: you may need to restart the kernel to use updated packages.\n", + "Requirement already satisfied: matplotlib in /opt/.qbraid/environments/qbraid_000000/pyenv/lib/python3.11/site-packages (3.9.1)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /opt/.qbraid/environments/qbraid_000000/pyenv/lib/python3.11/site-packages (from matplotlib) (1.2.1)\n", + "Requirement already satisfied: cycler>=0.10 in /opt/.qbraid/environments/qbraid_000000/pyenv/lib/python3.11/site-packages (from matplotlib) (0.12.1)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /opt/.qbraid/environments/qbraid_000000/pyenv/lib/python3.11/site-packages (from matplotlib) (4.53.1)\n", + "Requirement already satisfied: kiwisolver>=1.3.1 in /opt/.qbraid/environments/qbraid_000000/pyenv/lib/python3.11/site-packages (from matplotlib) (1.4.5)\n", + "Requirement already satisfied: numpy>=1.23 in 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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49m/opt/.qbraid/environments/qbraid_000000/pyenv/bin/python -m pip install --upgrade pip\u001b[0m\n", + "Note: you may need to restart the kernel to use updated packages.\n", + "Requirement already satisfied: pylatexenc in /opt/.qbraid/environments/qbraid_000000/pyenv/lib/python3.11/site-packages (2.10)\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49m/opt/.qbraid/environments/qbraid_000000/pyenv/bin/python -m pip install --upgrade pip\u001b[0m\n", + "Note: you may need to restart the kernel to use updated packages.\n", + "Requirement already satisfied: networkx in /opt/.qbraid/environments/qbraid_000000/pyenv/lib/python3.11/site-packages (3.2.1)\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49m/opt/.qbraid/environments/qbraid_000000/pyenv/bin/python -m pip install --upgrade pip\u001b[0m\n", + "Note: you may need to restart the kernel to use updated packages.\n", + "Collecting git+https://github.com/ryanhill1/Quantum-Challenge-Grader.git\n", + " Cloning https://github.com/ryanhill1/Quantum-Challenge-Grader.git to /tmp/pip-req-build-mvosx0nu\n", + " Running command git clone --filter=blob:none --quiet https://github.com/ryanhill1/Quantum-Challenge-Grader.git /tmp/pip-req-build-mvosx0nu\n", + " Resolved https://github.com/ryanhill1/Quantum-Challenge-Grader.git to commit a9baa682d3a07450befd4db4453a7590c7d446c0\n", + " Preparing metadata (setup.py) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: typeguard in /opt/.qbraid/environments/qbraid_000000/pyenv/lib/python3.11/site-packages (from qc-grader==0.19.7) (4.3.0)\n", + "Requirement already satisfied: jsonpickle in /opt/.qbraid/environments/qbraid_000000/pyenv/lib/python3.11/site-packages (from qc-grader==0.19.7) (3.0.3)\n", + "Requirement already satisfied: ipycytoscape in /opt/.qbraid/environments/qbraid_000000/pyenv/lib/python3.11/site-packages (from qc-grader==0.19.7) (1.3.3)\n", + "Requirement already satisfied: plotly in /opt/.qbraid/environments/qbraid_000000/pyenv/lib/python3.11/site-packages (from qc-grader==0.19.7) (5.22.0)\n", + "Requirement already satisfied: networkx in /opt/.qbraid/environments/qbraid_000000/pyenv/lib/python3.11/site-packages (from qc-grader==0.19.7) (3.2.1)\n", + "Requirement already satisfied: graphviz in /opt/.qbraid/environments/qbraid_000000/pyenv/lib/python3.11/site-packages (from qc-grader==0.19.7) (0.20.3)\n", + "Requirement already satisfied: ipywidgets>=7.6.0 in /opt/.qbraid/environments/qbraid_000000/pyenv/lib/python3.11/site-packages (from ipycytoscape->qc-grader==0.19.7) (8.1.3)\n", + "Requirement already satisfied: spectate>=1.0.0 in /opt/.qbraid/environments/qbraid_000000/pyenv/lib/python3.11/site-packages (from ipycytoscape->qc-grader==0.19.7) (1.0.1)\n", + "Requirement already satisfied: tenacity>=6.2.0 in /opt/.qbraid/environments/qbraid_000000/pyenv/lib/python3.11/site-packages (from plotly->qc-grader==0.19.7) (8.5.0)\n", + "Requirement already satisfied: packaging in /opt/.qbraid/environments/qbraid_000000/pyenv/lib/python3.11/site-packages (from plotly->qc-grader==0.19.7) (24.1)\n", + "Requirement already satisfied: typing-extensions>=4.10.0 in /opt/.qbraid/environments/qbraid_000000/pyenv/lib/python3.11/site-packages (from typeguard->qc-grader==0.19.7) (4.12.2)\n", + "Requirement already satisfied: comm>=0.1.3 in /opt/.qbraid/environments/qbraid_000000/pyenv/lib/python3.11/site-packages (from ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (0.2.2)\n", + "Requirement already satisfied: ipython>=6.1.0 in /opt/.qbraid/environments/qbraid_000000/pyenv/lib/python3.11/site-packages (from ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (8.26.0)\n", + "Requirement already satisfied: traitlets>=4.3.1 in /opt/.qbraid/environments/qbraid_000000/pyenv/lib/python3.11/site-packages (from ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (5.14.3)\n", + "Requirement already satisfied: widgetsnbextension~=4.0.11 in 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jedi>=0.16->ipython>=6.1.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (0.8.4)\n", + "Requirement already satisfied: ptyprocess>=0.5 in /opt/.qbraid/environments/qbraid_000000/pyenv/lib/python3.11/site-packages (from pexpect>4.3->ipython>=6.1.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (0.7.0)\n", + "Requirement already satisfied: wcwidth in /opt/.qbraid/environments/qbraid_000000/pyenv/lib/python3.11/site-packages (from prompt-toolkit<3.1.0,>=3.0.41->ipython>=6.1.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (0.2.13)\n", + "Requirement already satisfied: executing>=1.2.0 in /opt/.qbraid/environments/qbraid_000000/pyenv/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (2.0.1)\n", + "Requirement already satisfied: asttokens>=2.1.0 in /opt/.qbraid/environments/qbraid_000000/pyenv/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (2.4.1)\n", + "Requirement already satisfied: pure-eval in /opt/.qbraid/environments/qbraid_000000/pyenv/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (0.2.2)\n", + "Requirement already satisfied: six>=1.12.0 in /opt/.qbraid/environments/qbraid_000000/pyenv/lib/python3.11/site-packages (from asttokens>=2.1.0->stack-data->ipython>=6.1.0->ipywidgets>=7.6.0->ipycytoscape->qc-grader==0.19.7) (1.16.0)\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49m/opt/.qbraid/environments/qbraid_000000/pyenv/bin/python -m pip install --upgrade pip\u001b[0m\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install qiskit[visualization]==1.1.0\n", + "# Use the following if you are on MacOS/zsh\n", + "#!pip install 'qiskit[visualization]'==1.1.0\n", + "%pip install qiskit_ibm_runtime\n", + "%pip install matplotlib\n", + "%pip install pylatexenc\n", + "%pip install networkx\n", + "%pip install git+https://github.com/ryanhill1/Quantum-Challenge-Grader.git" + ] + }, + { + "cell_type": "markdown", + "id": "851f1506-14b4-419f-90c0-06da5a3cf347", + "metadata": {}, + "source": [ + "## Step 1: Map the system to quantum circuits and operators\n", + "\n", + "\n", + "In this lab we will examine the dynamics of the expectation value $\\langle Z_i \\rangle$ averaged over each site as a function of the field strength $h$. In the experiments below, you will prepare a circuit which implements the time evolution operator acting on the state $|000...0\\rangle$ according to the system parametersFor simplicity we will should set $\\delta t = \\frac{\\pi}{4}$. This ensures that $\\theta_J = -2J_z\\delta t = -\\frac{\\pi}{2}$ which makes the time evolution of our system much simpler for the isotropic phase.\n", + "\n", + "In this first exercise, you will create a function to generate the Hamiltonian in the form of a `SparsePauliOp` object. We'll introduce the modules you'll need, define some system parameters, and demonstrate a quick example of how you might want to accomplish this task." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4ffa3e60-23fc-45cb-aa77-cbc8d3b793ed", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from qiskit.circuit import QuantumCircuit, Parameter\n", + "from qiskit.circuit.library import PauliEvolutionGate\n", + "from qiskit.quantum_info import SparsePauliOp\n", + "from qiskit_ibm_runtime import QiskitRuntimeService\n", + "from qiskit.transpiler import CouplingMap\n", + "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n", + "from qiskit.synthesis import LieTrotter\n", + "\n", + "from qiskit_ibm_runtime.options import EstimatorOptions, DynamicalDecouplingOptions\n", + "from qiskit_ibm_runtime import EstimatorV2, Batch\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import json\n" + ] + }, + { + "cell_type": "markdown", + "id": "12ec8a2a-d1a5-4a1a-ba30-75bbaab76dc3", + "metadata": {}, + "source": [ + "## Step 4: Post-Process\n", + "\n", + "In these last two exercises you will retrieve and post-process your results.\n", + "\n", + "### Exercise 7: Retrieve your job results\n", + "For this exercise you'll use the `service.job(job_id)` method in order to retrieve your jobs by job id. You can obtain these ids either from the dictionary you defined earlier or by loading the json file which you saved them to. The lab assumes that the data you retrieve will be in the form of a dictionary whose keys associate with each phase you simulated and corresponding values contain a list of the job data.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "1d08652c-9785-44e4-8298-719d19a309cf", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expectation values for pub 0: [0.81571047 0.85484857 0.76506313 0.78948047 1.15717987 1.10325299\n", + " 1.3200847 0.89757171 1.34010816 0.85366594 0.81968722 0.46760491\n", + " 0.94905709 0.24027198 0.10695171 0.10354202 0.13213325 0.7412065\n", + " 0.67058069 0.92001268 0.61869254 0.8708431 1.05222078 1.15962165\n", + " 0.44567831 0.42586209 0.385429 1.19961848 0.87589938 1.04294029\n", + " 0.83431114 0.98053263 1.01004418 1.0449065 0.92952241 1.14504941\n", + " 1.17215613 0.85564328 0.97201384 0.76945966 1.0308478 0.70741309\n", + " 1.5968245 1.1140778 0.80344233 0.96058286 0.9012848 0.8598877\n", + " 0.86579918 0.88056728]\n", + "Expectation values for pub 1: [0.79494446 0.57899231 0.77004937 0.85888539 0.86204386 0.71552062\n", + " 0.75980297 1.08569504 0.95561552 0.92087444 0.79763157 0.7119615\n", + " 0.85044154 0.68405791 0.74241817 0.36590914 0.98752809 0.37587268\n", + " 0.46246542 0.85791011 0.73162003 0.71681037 0.70761658 0.80480182\n", + " 0.85833579 0.66407585 0.95202168 0.79888226 0.61559196 0.67422766\n", + " 0.74657136 0.77531169 0.98162101 0.6874475 0.96323172 0.47092847\n", + " 0.94275017 0.56013589 0.60874653 0.40943392 0.47798227 0.43763829\n", + " 0.36463988 0.0013792 0.02458783 0.1560044 0.12896085 0.91450606\n", + " 0.2119718 0.60999508]\n", + "Expectation values for pub 2: [-4.11930805e-03 9.59668591e-03 1.67509070e-02 -9.14183603e-03\n", + " 2.83787555e-02 1.23929815e-01 2.31193121e-01 1.16492582e-01\n", + " 1.40694551e-01 6.72976016e-02 3.90318881e-02 2.48380768e-02\n", + " 7.67834913e-02 1.45257154e-01 1.80036629e-01 1.25496390e-01\n", + " 1.78678343e-01 2.22057953e-01 1.98635318e-01 9.13150755e-02\n", + " 1.72783870e-01 1.49873305e-01 1.39312591e-01 4.09471343e-02\n", + " 8.56350827e-02 5.48823613e-02 1.66411215e-01 4.54287528e-02\n", + " 8.39363788e-02 5.46961749e-02 -7.38822652e-03 2.34459620e-03\n", + " -3.39451157e-17 3.21535721e-03 1.29029901e-02 1.22140935e-02\n", + " 1.05517996e-01 6.78908533e-03 7.71578412e-02 6.26221588e-02\n", + " 6.04773241e-02 1.10834603e-01 5.23386166e-02 1.52527200e-01\n", + " 6.42716391e-02 5.86272645e-02 2.45946510e-02 1.51138321e-02\n", + " 4.77368944e-02 4.78699258e-01]\n", + "Expectation values for pub 3: [ 1.23251801e-01 -7.65584053e-03 -7.65165235e-04 -4.38057037e-03\n", + " -3.22825365e-03 1.03147973e-02 3.79053546e-02 -8.91181026e-04\n", + " -3.73112055e-02 -1.60171335e-02 -2.13021831e-03 3.52092642e-03\n", + " 4.21395750e-03 4.27493409e-02 4.03989171e-02 3.45305771e-02\n", + " 1.84680908e-02 4.99130880e-02 6.53027613e-02 1.40457229e-01\n", + " 5.57614688e-02 3.14626878e-02 1.11507151e-01 1.29527417e-01\n", + " 2.53729103e-01 4.09035101e-02 1.53179923e-01 7.89312217e-02\n", + " 2.53962674e-01 2.14916774e-01 7.20372593e-02 1.26509300e-01\n", + " -1.77870332e-02 2.71822204e-01 2.19756618e-01 6.51684995e-02\n", + " 9.65109013e-02 5.14310236e-02 2.01121874e-01 5.50068524e-02\n", + " 3.79655806e-02 -5.61772905e-06 -5.42885495e-04 8.49365462e-02\n", + " 1.06907945e-01 -1.35938588e-03 -1.03018857e-02 2.36467414e-02\n", + " -5.31246849e-02 3.27875732e-01]\n", + "Expectation values for pub 4: [ 0.60659137 0.4632073 0.1106126 0.23897664 0.56002652 0.28945499\n", + " 0.14405968 0.51738747 0.15698277 0.27497012 0.16756931 0.33774128\n", + " 0.32971129 0.77744756 0.36133356 0.52345378 0.47198047 0.78171759\n", + " 0.56350201 0.48774477 0.69887872 0.79899957 0.55585662 0.14729179\n", + " 0.14985427 0.15005949 0.02856491 0.4347198 1.06634204 0.38041784\n", + " 0.35202242 0.08702471 0.15144625 0.20855911 0.1660497 0.14064552\n", + " 0.77030438 0.1613707 0.11586167 0.21935322 0.07829764 0.55119201\n", + " 0.34117357 0.14794411 0.23590652 -0.00731353 0.11713432 0.16987758\n", + " 0.33779284 0.44533308]\n", + "Expectation values for pub 5: [0.89483908 0.74526518 0.82697106 0.79853845 0.77310923 0.40616177\n", + " 0.86004397 0.67929623 0.50504673 0.01837587 0.08234216 0.27490953\n", + " 0.64480547 1.26925347 1.09225262 1.08602561 0.98562412 0.8229235\n", + " 0.86567078 1.33330116 0.86042158 1.04653368 1.00334535 1.13126148\n", + " 1.19165072 1.1320501 1.214107 1.00853389 1.07696397 1.05894742\n", + " 0.90226355 0.26010424 0.25886639 0.47719453 0.75499501 0.92832792\n", + " 0.98666264 0.82723885 0.97790823 0.74071645 0.69306934 1.04498147\n", + " 0.90560353 1.31243099 1.16907098 1.09965834 1.17285688 0.84594781\n", + " 0.97492941 0.68189777]\n", + "Expectation values for pub 6: [0.79359516 0.72286416 0.82009852 0.12925247 0.06707656 0.15873759\n", + " 0.08827907 0.21795735 0.25105047 0.05069018 0.11807328 0.6083853\n", + " 0.34184714 0.88808296 0.71809186 0.86738943 0.81339714 1.01360567\n", + " 1.16517374 1.04254223 1.02710206 0.72907145 0.8661259 0.61090729\n", + " 0.81147257 0.25569985 0.33081546 0.9690709 0.77099167 0.92598733\n", + " 0.94892089 1.13709517 1.07235403 0.99786801 0.90630512 0.92814498\n", + " 0.85232046 1.08008429 0.61978729 0.83868613 0.84006866 0.83201881\n", + " 0.89770599 0.62267349 1.31204545 0.82258821 0.8549411 0.59127553\n", + " 0.78059386 0.98648346]\n", + "Expectation values for pub 7: [ 0.5575012 0.56956563 0.30541587 0.08809729 0.14566669 0.00337194\n", + " 0.08978402 0.04250945 0.034185 -0.01474359 0.06376369 0.03126428\n", + " -0.01823185 0.11738767 0.07852449 0.33852979 0.13787004 0.57650988\n", + " 0.22739517 0.38275817 0.8561124 0.66602753 1.02506078 0.6343535\n", + " 0.58142077 0.47095609 0.43335678 0.47018511 0.49224412 0.32694853\n", + " 0.61834308 0.47289034 0.43185462 0.11408563 0.24103585 0.44410766\n", + " 0.56304265 0.4520187 0.52979554 0.60918389 0.76323223 0.65911172\n", + " 0.40272423 0.43318556 0.59088901 0.09875479 0.55070915 0.10169982\n", + " 0.47699597 0.61641985]\n", + "Expectation values for pub 8: [ 3.44482921e-01 -1.87332472e-01 -5.51228289e-02 2.92075834e-02\n", + " -1.38200968e-04 7.45524716e-03 -9.24086511e-17 -3.55229405e-02\n", + " 1.94685775e-02 6.59051081e-02 1.77449308e-01 8.78024307e-02\n", + " 6.71233692e-02 3.82101158e-17 5.02608061e-02 6.15429565e-02\n", + " 5.79131632e-02 2.05162169e-02 1.15769640e-01 1.37467340e-01\n", + " 3.09353544e-02 2.57218793e-02 1.29249743e-01 3.46327228e-04\n", + " 4.62910606e-16 2.62080858e-02 2.26890241e-02 6.35908078e-02\n", + " 2.29076182e-02 3.79236744e-02 1.01796397e-02 5.50356181e-05\n", + " 1.67799148e-02 1.80403855e-02 -5.81807075e-04 2.38212605e-01\n", + " -9.08531803e-03 3.46616855e-02 -2.16504323e-02 5.02887915e-03\n", + " 1.33609536e-01 5.30798004e-02 6.85355614e-02 1.05791261e-01\n", + " 1.87550683e-01 -1.55408740e-03 -6.00949646e-11 1.95028115e-02\n", + " -2.25056138e-01 2.78627598e-01]\n", + "Expectation values for pub 9: [ 0.43409768 0.01270225 0.01904894 0.02976117 0.05483579 0.2358548\n", + " 0.07315758 0.11883017 0.17312561 0.19313185 0.20067673 0.18721224\n", + " 0.20454689 0.06456691 0.03700694 -0.00269324 -0.00993189 0.09017418\n", + " 0.09698546 0.09502621 0.00183771 0.00928075 0.03671777 -0.00225219\n", + " 0.02371339 0.02999577 0.00753556 0.06412855 0.20834158 0.09586917\n", + " 0.30116776 0.3296292 0.14967638 0.31080033 0.12303067 0.2059081\n", + " 0.11246393 0.00912594 0.00381899 0.00415078 0.0903131 0.04109809\n", + " 0.11913365 0.25530225 0.23492658 0.21872152 0.0720249 0.04340217\n", + " -0.1132719 0.42943067]\n", + "Expectation values for pub 10: [0.71325903 0.32871469 0.48871994 0.2647161 0.22513349 0.77952901\n", + " 0.6910616 0.48347016 0.42510833 0.48017195 0.59496192 0.53061209\n", + " 0.56925636 0.41739504 0.21828665 0.46988069 0.25514002 0.13293033\n", + " 0.25724876 0.46797782 0.43302269 0.57338642 0.46634665 0.75945245\n", + " 0.42852461 1.18334116 1.13802324 0.90661326 0.76710916 1.02381697\n", + " 0.7452687 0.75380432 0.18716159 0.19132201 0.23552893 0.19143329\n", + " 0.66171487 0.28643306 0.53577577 0.0267596 0.08757409 0.26966249\n", + " 0.3921535 0.34149912 0.18071977 0.38285744 0.62309644 0.13015639\n", + " 0.66189864 0.79779705]\n", + "Expectation values for pub 11: [0.89111982 0.72557921 0.78777358 0.76035696 0.54386474 1.18064528\n", + " 0.81279895 1.11888804 0.83459676 1.00654168 0.91007769 1.13099822\n", + " 1.14930674 1.33673167 1.38846321 1.26046134 1.05537447 1.22753174\n", + " 0.97538996 0.87945715 1.01532685 1.08434259 1.30619497 1.10664762\n", + " 1.03702092 0.87660169 1.26090444 1.07041063 1.01022208 0.33033208\n", + " 0.63610088 0.49490708 0.60254808 0.03960303 0.07902912 0.63519402\n", + " 0.48399441 1.13894445 0.99592351 0.71961002 0.5300121 0.84605501\n", + " 0.56938121 0.96201701 0.81907649 0.94406781 0.87813229 1.08457021\n", + " 0.95256732 0.95394832]\n" + ] + } + ], + "source": [ + "# Code to grab all of the expectation values. Anisotropic case: \n", + "# Exercise 7: Parse all the data\n", + "\n", + "from qiskit_ibm_runtime import QiskitRuntimeService\n", + "\n", + "service = QiskitRuntimeService(\n", + " channel='ibm_quantum',\n", + " instance='events/qgss/24-8',\n", + " token='***'\n", + ")\n", + "\n", + "job = service.job('ctngwjt4gzyg008zg940')\n", + "\n", + "job_result = job.result()\n", + "\n", + "for idx, pub_result in enumerate(job_result):\n", + " print(f\"Expectation values for pub {idx}: {pub_result.data.evs}\")\n", + " \n", + "#pub_result = job.result()[11]\n", + "#values = pub_result.data.evs\n", + "#print(f\"Expectation values for pub [11]: {values}\")\n", + " \n", + "# https://docs.quantum.ibm.com/api/qiskit-ibm-runtime/qiskit_ibm_runtime.QiskitRuntimeService#jobs\n", + "# https://docs.quantum.ibm.com/guides/monitor-job" + ] + }, + { + "cell_type": "markdown", + "id": "f2d1624a-1c26-4001-b624-f8656bb6c620", + "metadata": {}, + "source": [ + "### Exercise: 8 Compute and plot your results\n", + "\n", + "In this final exercise, you will use the `PrimitiveResult` data and obtain the average $\\langle Z_i \\rangle$ over each site as a function of the field strength $h$. Once the averages have been obtained, store them in a dictionary called `avg_z_data` whose keys correspond to each phase. Finally, plot this data using a tool such as matplotlib to view the results side by side." + ] + }, + { + "cell_type": "markdown", + "id": "f363d216-d9ea-44e5-8134-6f1de6571969", + "metadata": {}, + "source": [ + "The two phases are the *Anisotropic* phase ($\\Delta = -5$) and the *XXX* phase ($\\Delta = 1$).\n", + "\n", + "# Run Anisotropic" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1692a322-071b-4085-ab68-bb2ae75db792", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Anisotropic i= 0\n", + "Anisotropic i= 1\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Anisotropic i= 2\n", + "Anisotropic i= 3\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Anisotropic i= 4\n", + "Anisotropic i= 5\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Anisotropic i= 6\n", + "Anisotropic i= 7\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Anisotropic i= 8\n", + "Anisotropic i= 9\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Anisotropic i= 10\n", + "Anisotropic i= 11\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Your code goes here\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "from qiskit_ibm_runtime import QiskitRuntimeService\n", + "\n", + "service = QiskitRuntimeService(\n", + " channel='ibm_quantum',\n", + " instance='events/qgss/24-8',\n", + " token='***'\n", + ")\n", + "job = service.job('ctngwjt4gzyg008zg940')\n", + "\n", + "job_result = job.result()\n", + "xgrid = np.linspace(0, 49, 50)\n", + "\n", + "num_subplots = 2\n", + "\n", + "for i in range(12):\n", + " print(\"Anisotropic i=\",i)\n", + " pub_result = job.result()[i]\n", + " values = pub_result.data.evs\n", + " #Produces list with 50 values for 50 spins.\n", + "\n", + " errors = pub_result.data.stds\n", + "\n", + " # plotting graph\n", + " \n", + " if i%2 == 0:\n", + " plt.figure(figsize=(7,2))\n", + " plt.subplot(1, num_subplots,1)\n", + " plt.plot(xgrid, values, marker='.')\n", + " plt.ylim(-1.6, 2.5)\n", + " plt.xlabel('Observables')\n", + " plt.ylabel('Values')\n", + " \n", + " if i%2 == 1:\n", + " plt.subplot(1, num_subplots,2)\n", + " plt.plot(xgrid, values, marker='.')\n", + " plt.ylim(-1.6, 2.5)\n", + " plt.xlabel('Observables')\n", + " plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "4985f4ec-fd5b-4574-b4b5-0320c00c99e5", + "metadata": {}, + "source": [ + "## Run XXX\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "0bba0aca-3a36-4283-8f97-f048f6fa474c", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/.qbraid/environments/qbraid_000000/pyenv/lib/python3.11/site-packages/qiskit/qpy/interface.py:305: UserWarning: The qiskit version used to generate the provided QPY file, 1.1.1, is newer than the current qiskit version 1.1.0. This may result in an error if the QPY file uses instructions not present in this current qiskit version\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "XXX i= 0\n", + "XXX i= 1\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "XXX i= 4\n", + "XXX i= 5\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "XXX i= 6\n", + "XXX i= 7\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "XXX i= 10\n", + "XXX i= 11\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Code to grab all of the expectation values\n", + "# Exercise 7: Parse all the data\n", + "# XXX run\n", + "all_job_data = {}\n", + "\n", + "from qiskit_ibm_runtime import QiskitRuntimeService\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "\n", + "service = QiskitRuntimeService(\n", + " channel='ibm_quantum',\n", + " instance='events/qgss/24-8',\n", + " token='***'\n", + ")\n", + "job = service.job('ctq9bwzx7b5g0080rwdg')\n", + "\n", + "job_result = job.result()\n", + "\n", + "#for idx, pub_result in enumerate(job_result):\n", + " #print(f\"Expectation values for pub {idx}: {pub_result.data.evs}\")\n", + " \n", + "#pub_result = job.result()[11]\n", + "#values = pub_result.data.evs\n", + "#print(f\"Expectation values for pub [11]: {values}\")\n", + "\n", + "# Plot results\n", + "avg_z_data = {}\n", + "\n", + "xgrid = np.linspace(0, 49, 50)\n", + "\n", + "num_subplots = 2\n", + "\n", + "for i in range(12):\n", + " print(\"XXX i=\",i)\n", + " pub_result = job.result()[i]\n", + " values = pub_result.data.evs\n", + " #Produces list with 50 values for 50 spins.\n", + "\n", + " errors = pub_result.data.stds\n", + "\n", + " # plotting graph\n", + "\n", + " if i%2 == 0:\n", + " plt.figure(figsize=(7,2))\n", + " plt.subplot(1, num_subplots,1)\n", + " plt.plot(xgrid, values, marker='.')\n", + " plt.ylim(-1.6, 2.5)\n", + " plt.xlabel('Observables')\n", + " plt.ylabel('Values')\n", + " \n", + " if i%2 == 1:\n", + " plt.subplot(1, num_subplots,2)\n", + " plt.plot(xgrid, values, marker='.')\n", + " plt.ylim(-1.6, 2.5)\n", + " plt.xlabel('Observables')\n", + " plt.show()\n", + " \n", + "# https://docs.quantum.ibm.com/api/qiskit-ibm-runtime/qiskit_ibm_runtime.QiskitRuntimeService#jobs\n", + "# https://docs.quantum.ibm.com/guides/monitor-job" + ] + }, + { + "cell_type": "markdown", + "id": "d5bb82d1-25ff-4183-a69a-96b482358079", + "metadata": {}, + "source": [ + "# Another interpretation of the question." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a19218fd-7c08-43da-a2cb-7bd0fb5c2abc", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "XXX calculation is done. Wait for Anisotropic calculation\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# The question is phrased: In this final exercise, you will use the `PrimitiveResult` data\n", + "# and obtain the average $\\langle Z_i \\rangle$ over each site as a function of the field strength $h$.\n", + "# This question suggests that the expectation value (which is an average) should be computed for each spin.\n", + "# This will result in 12 graphs for XXX and 12 graphs for Anisotropic (see above.)\n", + "# If, however, you interpret average $\\langle Z_i \\rangle$ as the average of the expectation values\n", + "# over all spins, you will get the following:\n", + "\n", + "# First run first two cells\n", + "# The two phases are the *Anisotropic* phase ($\\Delta = -5$) and the *XXX* phase ($\\Delta = 1$).\n", + "\n", + "# XXX results\n", + "service = QiskitRuntimeService(\n", + " channel='ibm_quantum',\n", + " instance='events/qgss/24-8',\n", + " token='***'\n", + ")\n", + "job = service.job('ctq9bwzx7b5g0080rwdg')\n", + "job_result = job.result()\n", + "\n", + "h_vals = np.linspace(0., np.pi/2, 12)\n", + "averagesXXX = []\n", + "\n", + "for i in range(12):\n", + " h_val = h_vals[i]\n", + " pub_result = job.result()[i]\n", + " values = pub_result.data.evs\n", + " #print(\"i=\",i, \"len:\", len(values), \"h_val:\", h_val)\n", + " average = np.average( values )\n", + " averagesXXX.append( average )\n", + " \n", + "plt.figure(figsize=(7,2))\n", + "plt.plot(h_vals, averagesXXX, marker='.', color='k')\n", + "plt.ylim(-1.2, 1.2)\n", + "plt.xlabel('h')\n", + "plt.ylabel('Ave')\n", + "plt.title('(black) XXX, (green) Anisotropic: Ave of expectation values across all spins \\n I do not think this is what the question wants.')\n", + "print('XXX calculation is done. Wait for Anisotropic calculation')\n", + "\n", + "# Anisotropic results\n", + "service = QiskitRuntimeService(\n", + " channel='ibm_quantum',\n", + " instance='events/qgss/24-8',\n", + " token='***'\n", + ")\n", + "job = service.job('ctngwjt4gzyg008zg940')\n", + "job_result = job.result()\n", + "\n", + "averagesAniso = []\n", + "\n", + "for i in range(12):\n", + " h_val = h_vals[i]\n", + " pub_result = job.result()[i]\n", + " values = pub_result.data.evs\n", + " #print(\"i=\",i, \"len:\", len(values), \"h_val:\", h_val)\n", + " average = np.average( values )\n", + " averagesAniso.append( average )\n", + " \n", + "plt.plot(h_vals, averagesAniso, marker='.', color='g')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "e8a4b236-7797-4cde-b310-19b18f8e8e9d", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "

© Copyright IBM 2017, 2024.

This code is licensed under the Apache License, Version 2.0. You may
obtain a copy of this license in the LICENSE.txt file in the root directory
of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.

Any modifications or derivative works of this code must retain this
copyright notice, and modified files need to carry a notice indicating
that they have been altered from the originals.

" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import datetime\n", + "from IPython.display import HTML, display\n", + "\n", + "\n", + "def qiskit_copyright(line=\"\", cell=None):\n", + " \"\"\"IBM copyright\"\"\"\n", + " now = datetime.datetime.now()\n", + "\n", + " html = \"
\"\n", + " html += \"

© Copyright IBM 2017, %s.

\" % now.year\n", + " html += \"

This code is licensed under the Apache License, Version 2.0. You may
\"\n", + " html += \"obtain a copy of this license in the LICENSE.txt file in the root directory
\"\n", + " html += \"of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.\"\n", + "\n", + " html += \"

Any modifications or derivative works of this code must retain this
\"\n", + " html += \"copyright notice, and modified files need to carry a notice indicating
\"\n", + " html += \"that they have been altered from the originals.

\"\n", + " html += \"
\"\n", + " return display(HTML(html))\n", + "\n", + "\n", + "qiskit_copyright()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}