"
- ]
- },
- "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(nrows=1, ncols=len(metrics), figsize=(min(16, 8 * len(metrics)), 7))\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 = [(2, 5), (2, 6), (2, 7), (2, 8), (3, 8), (3, 7), (3, 6), (3, 5), (4, 5), (4, 6)]\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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- " │ │ │ │ │ │ │ │ │ │ │\n",
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- " │ │ │ │ │ │\n",
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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",
- "\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, processor_id=processor_id, 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)\n",
- " 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([to_ground_state_prob(*res) for res in raw_results]).reshape(\n",
- " trials, len(cycle_values)\n",
- ")"
- ]
- },
- {
- "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(\n",
- " loschmidt_echo_batch, xeb_options\n",
- ")\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([to_ground_state_prob(*res) for res in xeb_results]).reshape(\n",
- " trials, len(cycle_values)\n",
- ")"
- ]
- },
- {
- "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
-}
diff --git a/docs/tutorials/google/identifying_hardware_changes.ipynb b/docs/tutorials/google/identifying_hardware_changes.ipynb
index 5945ef06f74..5091c595d68 100644
--- a/docs/tutorials/google/identifying_hardware_changes.ipynb
+++ b/docs/tutorials/google/identifying_hardware_changes.ipynb
@@ -322,7 +322,7 @@
"id": "9sY5bMW9f-Oo"
},
"source": [
- "Note: The parallel XEB errors are scaled in pxeb_results. This is because the collected fidelities are the estimated depolarization fidelities, not the Pauli error metrics available from the calibration data. See the [XEB Theory](../../noise/qcvv/xeb_theory.ipynb#fidelities) tutorial for an explanation why, and [Calibration Metrics](../../google/calibration.md) for more information on the difference between these values."
+ "Note: The parallel XEB errors are scaled in pxeb_results. This is because the collected fidelities are the estimated depolarization fidelities, not the Pauli error metrics available from the calibration data. See the [XEB Theory](../../noise/qcvv/xeb_theory.ipynb#fidelities) tutorial for an explanation of why."
]
},
{
@@ -525,7 +525,7 @@
"\n",
"* You need to map your actual circuit's logical qubits to your selected hardware qubits. This is in general a difficult problem, and the best solution can depend on the specific structure of the circuit to be run. Take a look at the [Qubit Picking with Loschmidt Echoes](./echoes.ipynb) tutorial, which estimates the error rates of gates for your specific circuit. Also, consider [Best Practices#qubit picking](../../google/best_practices.ipynb#qubit_picking) for additional advice on this.\n",
"* The [Optimization, Alignment, and Spin Echoes](./spin_echoes.ipynb) tutorial provides resources on how you can improve the reliability of your circuit by: optimizing away redundant or low-impact gates, aligning gates into moments with others of the same type, and preventing decay on idle qubits with by adding spin echoes.\n",
- "* Other than for qubit picking, you should also use calibration for error compensation. The [Coherent vs incoherent noise with XEB](../../noise/qcvv/coherent_vs_incoherent_xeb.ipynb), [XEB Calibration Example](../../noise/qcvv/xeb_calibration_example.ipynb), [Parallel XEB](../../noise/qcvv/parallel_xeb.ipynb) and [Isolated XEB](../../noise/qcvv/isolated_xeb.ipynb) tutorials demonstrate how to run a classical optimizer on collected two-qubit gate characterization data, identity the true unitary matrix implemented by each gate, and add [Virtual Pauli Z gates](../../hardware/devices.ipynb) to compensate for the identified error, improving the reliability of your circuit.\n",
+ "* Other than for qubit picking, you should also use calibration for error compensation. The [Coherent vs incoherent noise with XEB](../../noise/qcvv/coherent_vs_incoherent_xeb.ipynb), [Parallel XEB](../../noise/qcvv/parallel_xeb.ipynb) and [Isolated XEB](../../noise/qcvv/isolated_xeb.ipynb) tutorials demonstrate how to run a classical optimizer on collected two-qubit gate characterization data, identity the true unitary matrix implemented by each gate, and add [Virtual Pauli Z gates](../../hardware/devices.ipynb) to compensate for the identified error, improving the reliability of your circuit.\n",
"* You are also free to use the characterization data to improve the performance of large batches of experiment circuits. In this case you'd want to prepare your characterization ahead of running all your circuits, and use the data to compensate each circuit, right before running them."
]
}
![](\"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",
- "