diff --git a/recirq/random_circuit_sampling/rcs_experiment_demonstration.ipynb b/recirq/random_circuit_sampling/rcs_experiment_demonstration.ipynb new file mode 100644 index 00000000..f6234264 --- /dev/null +++ b/recirq/random_circuit_sampling/rcs_experiment_demonstration.ipynb @@ -0,0 +1,226 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Parallel Random Circuit Sampling (RCS) & XEB Analysis\n", + "\n", + "This notebook demonstrates the `RCSExperiment` and `RCSResults` framework for generating, executing, and analyzing Random Circuit Sampling experiments." + ], + "metadata": { + "id": "TKUuvR37up68" + } + }, + { + "cell_type": "markdown", + "source": [ + "#Imports" + ], + "metadata": { + "id": "RMz52FOW-8DO" + } + }, + { + "cell_type": "code", + "source": [ + "import cirq\n", + "import cirq_google\n", + "import qsimcirq\n", + "import recirq.random_circuit_sampling as rcs\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ], + "metadata": { + "id": "dKWboTyH-ssA" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Define the patches\n", + "\n", + "Select sets of connected qubits to serve as patches. These patches must be disjoint and should not share any common qubits." + ], + "metadata": { + "id": "tGtt-E53Acam" + } + }, + { + "cell_type": "code", + "source": [ + "patch_1 = cirq.GridQubit.rect(4, 3, top=0, left=0)\n", + "patch_2 = cirq.GridQubit.rect(4, 3, top=0, left=3)\n", + "patch_3 = cirq.GridQubit.rect(4, 3, top=4, left=0)\n", + "patches = [patch_1, patch_2, patch_3]" + ], + "metadata": { + "id": "kK-PGBsNAcAT" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Set up and run the experiment\n", + "\n", + "Specify the qubit patches and the desired circuit depths for XEB calculation. Additionally, define the number of random circuit instances per configuration and the specific tiling pattern for the experiment. While the framework supports characterizing the $fSim$ gate parameters ($\\theta, \\phi, \\zeta, \\chi, \\gamma$), this step is bypassed here as we are performing an ideal noiseless simulation." + ], + "metadata": { + "id": "c0zvsbl9AnSx" + } + }, + { + "cell_type": "code", + "source": [ + "experiment = rcs.RCSExperiment(\n", + " patches=patches,\n", + " depths=[30, 50, 70, 90],\n", + " num_instances=3,\n", + " pattern_name=\"staggered\",\n", + " seed= 2026\n", + ")" + ], + "metadata": { + "id": "YCL7PrIoAfmW" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "results = experiment.run(sampler=cirq.Simulator(), n_repetitions=10000, characterize=False)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0yCdLPwQAqut", + "outputId": "427cc8d3-be63-4a56-a391-d434b0c69422" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Running 12 zipped instances on 3 parallel patches...\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Calculate linear XEB fidelities\n", + "\n", + "To calculate fidelities via noiseless simulation, we can employ the standard `cirq.Simulator()`. For experiments involving larger system sizes, the more performance-optimized `qsimcirq.QSimSimulator()` can be used to improve execution efficiency." + ], + "metadata": { + "id": "sgg6y0xU2hZJ" + } + }, + { + "cell_type": "code", + "source": [ + "fidelities = results.fidelities_lin(simulator=qsimcirq.QSimSimulator())" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lIKch40S1fX9", + "outputId": "898ba6d2-f0aa-4995-8dc0-fe14ab4397f1" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "XEB Analysis: 100%|████████████████████████| 36/36 [00:01<00:00, 21.14it/s]\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Plot the results\n", + "\n", + "Visualize the XEB results for each patch across the specified circuit depths. The plot reflects the mean fidelity calculated across all random circuit instances for each (patch, depth) configuration." + ], + "metadata": { + "id": "OGrm6xgBAxR4" + } + }, + { + "cell_type": "code", + "source": [ + "fig, ax = plt.subplots(figsize=(8, 6))\n", + "for i in range(len(patches)):\n", + " x = experiment.depths\n", + " y = [np.mean(fidelities[(i, d)]) for d in x]\n", + " ax.plot(x, y, marker='o', markersize=8, linewidth=2, label=f'Patch {i}')\n", + "\n", + "ax.set_ylim(-0.1, 1.1)\n", + "ax.set_xlim(min(experiment.depths) - 2, max(experiment.depths) + 2)\n", + "ax.set_xlabel('Cycle Depth', fontsize=14)\n", + "ax.set_ylabel('Linear XEB Fidelity', fontsize=14)\n", + "ax.set_title('Parallel RCS', fontsize=16, pad=20)\n", + "ax.legend(frameon=False, fontsize=12)\n", + "ax.tick_params(\n", + " axis='both',\n", + " direction='in',\n", + " top=True,\n", + " right=True,\n", + " labelsize=12,\n", + " width=1.2,\n", + " length=6\n", + ")\n", + "for spine in ax.spines.values():\n", + " spine.set_linewidth(1.2)\n", + "plt.tight_layout()\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 607 + }, + "id": "iJpv_xBQqUwy", + "outputId": "0f0c67e4-b270-4c46-9017-dc22d2d3ea28" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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" 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