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MichaelBroughtonMichaelBroughton
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Remove use of deprecated device behavior from quantum_volume_errors. (#5198)
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‎examples/advanced/quantum_volume_errors.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Analyzing Quantum Volume Errors\n",
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"This notebook analyzes the error rates required for achieving Quantum Volume at a particular depth. For a given m = depth = number of qubits, plot the HOG for np.logspace outputs to view when it crosses the 2/3rds probability threshold."
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]
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "8e3qhaan0Vwx"
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},
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"source": [
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"# Analyzing Quantum Volume Errors\n",
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"This notebook analyzes the error rates required for achieving Quantum Volume at a particular depth. For a given m = depth = number of qubits, plot the HOG for np.logspace outputs to view when it crosses the 2/3rds probability threshold."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "bd9529db1c0b"
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},
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"outputs": [],
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"source": [
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"try:\n",
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" import cirq\n",
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"except ImportError:\n",
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" print(\"installing cirq...\")\n",
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" !pip install --quiet cirq\n",
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" print(\"installed cirq.\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "1QezTbjO0Vw4"
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},
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"outputs": [],
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"source": [
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"import cirq\n",
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"import cirq_google\n",
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"\n",
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"# Configuration parameters. Feel free to mess with these!\n",
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"num_circuits = 10\n",
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"depth = 4\n",
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"num_samplers = 50\n",
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"repetitions = 10_000\n",
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"device=cirq_google.Sycamore\n",
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"\n",
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"print(f\"Configuration: depth {depth} with \"\n",
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" f\"{num_circuits} circuits of {num_samplers} samplers\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "rZ_7U7n90Vw5"
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},
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"outputs": [],
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"source": [
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"# Run the Quantum Volume algorithm over the above parameters.\n",
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"\n",
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"import numpy as np\n",
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"from cirq.contrib import quantum_volume, routing\n",
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"\n",
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"errors = np.logspace(-1, -4, num=num_samplers)\n",
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"samplers = [\n",
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" cirq.DensityMatrixSimulator(noise=cirq.ConstantQubitNoiseModel(\n",
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" qubit_noise_gate=cirq.DepolarizingChannel(p=error)))\n",
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" for error in errors]\n",
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"\n",
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"result = quantum_volume.calculate_quantum_volume(\n",
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" num_circuits=num_circuits,\n",
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" depth=depth,\n",
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" num_qubits=depth,\n",
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" device_graph=routing.gridqubits_to_graph_device(device.qubits),\n",
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" samplers=samplers,\n",
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" compiler=cirq_google.optimized_for_sycamore,\n",
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" repetitions=repetitions)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "CvJvkMLx0Vw6"
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},
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"outputs": [],
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"source": [
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"# Create a chart that plots the HOG rate relative to the simulated error ratio.\n",
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"\n",
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"from matplotlib import pyplot as plt\n",
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"import statistics\n",
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"\n",
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"def chunks(l, n):\n",
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" \"\"\"Yield successive n-sized chunks from l.\"\"\"\n",
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" for i in range(0, len(l), n):\n",
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" yield l[i:i + n]\n",
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" \n",
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"split = chunks([res.sampler_result for res in result], num_circuits)\n",
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"fig, axs = plt.subplots()\n",
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"axs.plot(errors,\n",
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" [statistics.mean(chunk) for chunk in split])\n",
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"\n",
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"# Line markers for asymptotic ideal heavy output probability and the ideal Heavy\n",
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"# Output Generation threshold.\n",
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"axs.axhline((1 + np.log(2)) / 2,\n",
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" color='tab:green',\n",
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" label='Asymptotic ideal',\n",
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" linestyle='dashed')\n",
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"axs.axhline(2 / 3, label='HOG threshold', color='k', linestyle='dotted')\n",
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"plt.xscale('log')\n",
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"axs.set_ybound(0.4, 1)\n",
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"axs.set_xlabel(\"error rate\")\n",
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"axs.set_ylabel(\"est. heavy output probability\")\n",
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"fig.suptitle(f'HOG probability by simulated error rate for d={depth}')"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.9"
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},
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"colab": {
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"name": "quantum_volume_errors.ipynb",
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"provenance": []
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "bd9529db1c0b"
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},
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"outputs": [],
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"source": [
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"try:\n",
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" import cirq\n",
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"except ImportError:\n",
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" print(\"installing cirq...\")\n",
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" !pip install --quiet cirq\n",
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" print(\"installed cirq.\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import cirq\n",
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"import cirq_google\n",
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"\n",
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"# Configuration parameters. Feel free to mess with these!\n",
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"num_circuits = 10\n",
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"depth = 4\n",
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"num_samplers = 50\n",
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"device = cirq_google.Bristlecone\n",
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"repetitions = 10_000\n",
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"compiler = lambda circuit: cirq_google.optimized_for_xmon(\n",
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" circuit=circuit,\n",
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" new_device=device)\n",
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"\n",
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"print(f\"Configuration: depth {depth} with \"\n",
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" f\"{num_circuits} circuits of {num_samplers} samplers\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Run the Quantum Volume algorithm over the above parameters.\n",
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"\n",
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"import numpy as np\n",
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"from cirq.contrib import quantum_volume, routing\n",
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"\n",
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"errors = np.logspace(-1, -4, num=num_samplers)\n",
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"samplers = [\n",
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" cirq.DensityMatrixSimulator(noise=cirq.ConstantQubitNoiseModel(\n",
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" qubit_noise_gate=cirq.DepolarizingChannel(p=error)))\n",
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" for error in errors]\n",
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"\n",
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"result = quantum_volume.calculate_quantum_volume(\n",
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" num_circuits=num_circuits,\n",
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" depth=depth,\n",
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" num_qubits=depth,\n",
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" device_graph=routing.gridqubits_to_graph_device(device.qubits),\n",
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" samplers=samplers,\n",
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" compiler=compiler,\n",
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" repetitions=repetitions)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create a chart that plots the HOG rate relative to the simulated error ratio.\n",
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"\n",
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"from matplotlib import pyplot as plt\n",
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"import statistics\n",
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"\n",
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"def chunks(l, n):\n",
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" \"\"\"Yield successive n-sized chunks from l.\"\"\"\n",
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" for i in range(0, len(l), n):\n",
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" yield l[i:i + n]\n",
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" \n",
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"split = chunks([res.sampler_result for res in result], num_circuits)\n",
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"fig, axs = plt.subplots()\n",
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"axs.plot(errors,\n",
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" [statistics.mean(chunk) for chunk in split])\n",
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"\n",
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"# Line markers for asymptotic ideal heavy output probability and the ideal Heavy\n",
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"# Output Generation threshold.\n",
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"axs.axhline((1 + np.log(2)) / 2,\n",
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" color='tab:green',\n",
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" label='Asymptotic ideal',\n",
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" linestyle='dashed')\n",
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"axs.axhline(2 / 3, label='HOG threshold', color='k', linestyle='dotted')\n",
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"plt.xscale('log')\n",
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"axs.set_ybound(0.4, 1)\n",
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"axs.set_xlabel(\"error rate\")\n",
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"axs.set_ylabel(\"est. heavy output probability\")\n",
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"fig.suptitle(f'HOG probability by simulated error rate for d={depth}')"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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"nbformat": 4,
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"nbformat_minor": 0
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}

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