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Remove use of deprecated device behavior from quantum_volume_errors. #5198

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merged 3 commits into from
Apr 5, 2022
Merged

Remove use of deprecated device behavior from quantum_volume_errors. #5198

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0e917d2
Remove use of deprecated device behavior from quantum_volume_errors.
MichaelBroughton Apr 5, 2022
70c7c12
Dave feedback.
MichaelBroughton Apr 5, 2022
8c7be46
Merge branch 'master' into device_volume
CirqBot Apr 5, 2022
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271 changes: 140 additions & 131 deletions examples/advanced/quantum_volume_errors.ipynb
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@@ -1,134 +1,143 @@
{
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Line #18.        compiler=compiler,

Think you can just make this cirq_google.optimized_for_xmonnow andremove compiler lambda?


Reply via ReviewNB

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