"
]
},
"metadata": {
"needs_background": "light",
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"processor_id = \"\" #@param {type:\"string\"}\n",
"metrics = \"parallel_p00_error, two_qubit_sqrt_iswap_gate_xeb_pauli_error_per_cycle\" #@param {type:\"string\"}\n",
"metrics = [m.strip() for m in metrics.split(sep=\",\")]\n",
"\n",
"from matplotlib.colors import LogNorm\n",
"\n",
"\n",
"_, axes = plt.subplots(\n",
" nrows=1, ncols=len(metrics), figsize=(min(16, 8 * len(metrics)), 7)\n",
")\n",
"\n",
"\n",
"calibration = cg.get_engine_calibration(processor_id=processor_id)\n",
"for i, metric in enumerate(metrics):\n",
" calibration.heatmap(metric).plot(\n",
" ax=axes[i] if len(metrics) > 1 else axes, \n",
" collection_options={\"norm\": LogNorm()}, \n",
" annotation_format=\"0.3f\", \n",
" annotation_text_kwargs = {\"size\": \"small\"}\n",
");"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "T9U6qwhmBLh3"
},
"source": [
"Using this report as a guide, we select a good set of qubits."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "v8rxDM_5AF4p"
},
"outputs": [],
"source": [
"# Select qubit indices here.\n",
"qubit_indices = [\n",
" (2, 5), (2, 6), (2, 7), (2, 8), (3, 8), \n",
" (3, 7), (3, 6), (3, 5), (4, 5), (4, 6) \n",
"]\n",
"qubits = [cirq.GridQubit(*idx) for idx in qubit_indices]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JDTOQHi1FNQ1"
},
"source": [
"An example random circuit on these qubits (used as the forward operations of the Loschmidt echo) is shown below.\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"id": "AhziVqFiFaFy"
},
"outputs": [
{
"data": {
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" └──────────────────────────────────────────┘ └────────────────────────────┘ └──────────────────────────────────────────┘ └────────────────────────────┘ └──────────────────────────────────────────┘ └────────────────────────────┘"
]
},
"execution_count": 10,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
],
"source": [
"create_random_circuit(qubits, cycles=10, seed=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AByUxOocFMCx"
},
"source": [
"## Set up XEB calibration"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ir3eBfioDx3y"
},
"source": [
"Now we specify the cycle depths and other options for XEB calibration below. Note that all `cirq.FSimGate` parameters are characterized by default."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"id": "by6KJHuxp9-I"
},
"outputs": [],
"source": [
"xeb_options = cg.LocalXEBPhasedFSimCalibrationOptions(\n",
" cycle_depths=(5, 25, 50, 100),\n",
" n_processes=1,\n",
" fsim_options=cirq.experiments.XEBPhasedFSimCharacterizationOptions(\n",
" characterize_theta=False,\n",
" characterize_zeta=True,\n",
" characterize_chi=True,\n",
" characterize_gamma=True,\n",
" characterize_phi=False,\n",
" ),\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pxJTETNmQTg9"
},
"source": [
"## Run a Loschmidt echo benchmark"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ig0EjlmDIRm1"
},
"source": [
"Note: See the [Loschmidt echo tutorial](https://quantumai.google/cirq/tutorials/google/echoes) for background about this benchmark."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"id": "YquQdZSqQXCq"
},
"outputs": [],
"source": [
"\"\"\"Setup the Loschmidt echo experiment.\"\"\"\n",
"cycle_values = range(0, 40 + 1, 4)\n",
"nreps = 20_000\n",
"trials = 10\n",
"\n",
"sampler = cg.get_engine_sampler(\n",
" project_id=project_id,\n",
" processor_id=processor_id, \n",
" gate_set_name=\"sqrt_iswap\",\n",
")\n",
"\n",
"loschmidt_echo_batch = [\n",
" create_loschmidt_echo_circuit(qubits, cycles=c, seed=trial)\n",
" for trial in range(trials) for c in cycle_values\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "E95hBywZswC6"
},
"source": [
"### Without calibration"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "w7jLxisKsyn8"
},
"source": [
"First we run the Loschmidt echo without calibration."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"id": "fCt5Z9Basy_n"
},
"outputs": [],
"source": [
"# Run on the engine.\n",
"raw_results = sampler.run_batch(programs=loschmidt_echo_batch, repetitions=nreps)\n",
"\n",
"# Convert measurements to survival probabilities.\n",
"raw_probs = np.array(\n",
" [to_ground_state_prob(*res) for res in raw_results]\n",
").reshape(trials, len(cycle_values))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "iyxRKi0DszLu"
},
"source": [
"### With XEB calibration"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pWF_c_jrxH_m"
},
"source": [
"Now we perform XEB calibration."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"id": "eUWl3GHuqHxl"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 45/45 [00:46<00:00, 1.04s/it]\n",
"100%|██████████| 45/45 [01:50<00:00, 2.45s/it]\n",
"100%|██████████| 45/45 [01:00<00:00, 1.34s/it]\n",
"100%|██████████| 45/45 [02:24<00:00, 3.20s/it]\n"
]
}
],
"source": [
"# Get characterization requests.\n",
"characterization_requests = cg.prepare_characterization_for_operations(loschmidt_echo_batch, xeb_options)\n",
"\n",
"# Characterize the requests on the engine.\n",
"characterizations = cg.run_calibrations(characterization_requests, sampler)\n",
"\n",
"# Make compensations to circuits in the Loschmidt echo batch.\n",
"xeb_calibrated_batch = [\n",
" cg.make_zeta_chi_gamma_compensation_for_moments(circuit, characterizations).circuit\n",
" for circuit in loschmidt_echo_batch\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FH9Lvt7gxIw8"
},
"source": [
"And run the XEB calibrated batch below."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"id": "Ibl9odPdrosP"
},
"outputs": [],
"source": [
"# Run on the engine.\n",
"xeb_results = sampler.run_batch(programs=xeb_calibrated_batch, repetitions=nreps)\n",
"\n",
"# Convert measurements to survival probabilities.\n",
"xeb_probs = np.array(\n",
" [to_ground_state_prob(*res) for res in xeb_results]\n",
").reshape(trials, len(cycle_values))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ynCBsf4-s3GJ"
},
"source": [
"### Compare results"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FV5HLCAD4hHh"
},
"source": [
"The next cell plots the results."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"id": "7hNzY6K1vh0V"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light",
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"plt.semilogy(cycle_values, np.average(raw_probs, axis=0), lw=3, label=\"No calibration\")\n",
"plt.semilogy(cycle_values, np.average(xeb_probs, axis=0), lw=3, label=\"XEB calibration\")\n",
"\n",
"plt.xlabel(\"Cycles\")\n",
"plt.ylabel(\"Survival probability\")\n",
"plt.legend();"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8NVknuvzd6Ex"
},
"source": [
"A smaller (in magnitude) slope indicates lower two-qubit gate errors. You should see that XEB calibration produces lower errors than no calibration in the Loschmidt echo benchmark."
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "xeb_calibration_example.ipynb",
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
![](\"https://quantumai.google/site-assets/images/buttons/quantumai_logo_1x.png\"){kind=logo}
View on QuantumAI\n",
" ![](\"https://quantumai.google/site-assets/images/buttons/colab_logo_1x.png\"){kind=logo}
Run in Google Colab\n",
" ![](\"https://quantumai.google/site-assets/images/buttons/github_logo_1x.png\"){kind=logo}
View source on GitHub\n",
" ![](\"https://quantumai.google/site-assets/images/buttons/download_icon_1x.png\"){kind=icon}
Download notebook\n",
"