{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "5fc87052", "metadata": { "execution": { "iopub.execute_input": "2025-06-13T13:19:44.517990Z", "iopub.status.busy": "2025-06-13T13:19:44.517589Z", "iopub.status.idle": "2025-06-13T13:19:44.524469Z", "shell.execute_reply": "2025-06-13T13:19:44.523562Z" }, "nbsphinx": "hidden" }, "outputs": [], "source": [ "# Copyright 2025 Keysight Technologies Inc." ] }, { "cell_type": "raw", "id": "87bf0519", "metadata": { "lines_to_next_cell": 0, "raw_mimetype": "text/restructuredtext" }, "source": [ "\n", ".. _qubit_spec:\n", "\n", "Qubit spectroscopy\n", "==================\n", "\n", "Quantum computers store information in the discrete energy levels of qudits. To\n", "process this information, they perform operations that alter the energetic state\n", "of these qudits. For example, by exciting the electromagnetic field in the vicinity\n", "of a qudit at the frequency corresponding to the difference between the energy\n", "levels, quantum computers can drive a transition between two energy levels of the\n", "qudit and perform operations such as Pauli-X gates.\n", "\n", "When assembling a quantum computer, it is important to learn the energy gap between\n", "different levels of the available qudits to be able to perform accurate quantum\n", "operations. This is typically done by running a spectroscopy experiment.\n", "\n", "In order to learn the energy gap between the ground state and the first excited state\n", "of a qudit, a spectroscopy experiment undertakes the following steps:\n", "\n", " #. Initialize the qudit to the ground state.\n", "\n", " #. Apply a control pulse at a frequency :math:`\\omega` on the target qudit.\n", "\n", " #. Measure the population of the qudit in the excited state.\n", "\n", " #. Repeat the above steps for different values of :math:`\\omega`.\n", "\n", " #. Select the frequency which maximizes the population in the excited state.\n", "\n", "The population of the excited state gives a resonance curve, where the energy gap\n", "can be deduced from the frequency with the highest population in the excited state.\n", "\n", ".. image:: ../figures/spectroscopy_experiment.png\n", " :align: center\n", " :width: 120%\n", "\n", ".. note::\n", "\n", " The above steps can be applied directly to any two levels of a qudit, for example\n", " to learn the energy gap between the first and the second excited states in the\n", " figure above.\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "37a5ed05", "metadata": { "execution": { "iopub.execute_input": "2025-06-13T13:19:44.528621Z", "iopub.status.busy": "2025-06-13T13:19:44.528122Z", "iopub.status.idle": "2025-06-13T13:19:48.537947Z", "shell.execute_reply": "2025-06-13T13:19:48.537212Z" }, "lines_to_next_cell": 2 }, "outputs": [], "source": [ "import keysight.qcs as qcs\n", "import numpy as np" ] }, { "cell_type": "raw", "id": "02ffdf2c", "metadata": { "lines_to_next_cell": 0, "raw_mimetype": "text/restructuredtext" }, "source": [ "We start by initializing a qubit and defining an empty channel mapper to create a new\n", "instance of the :py:class:`~keysight.qcs.experiments.QubitSpectroscopy` class. We load\n", "a pre-defined :py:class:`~keysight.qcs.programs.CalibrationSet` that contains a\n", "configuration for up to 10 qubits and linkers for the ``RX`` and measurement\n", "gates.\n", "\n", "Because the purpose of this experiment is to calibrate a parameter, namely the control\n", "frequency of the qubit, the :py:class:`~keysight.qcs.experiments.QubitSpectroscopy` is\n", "a subclass of the more generic\n", ":py:class:`~keysight.qcs.experiments.CalibrationExperiment` which requires an\n", "operation name to be passed on instantiation. This name must match the name with which\n", "the associated :py:class:`~keysight.qcs.programs.ParameterizedLinker` is stored in\n", "the calibration set.\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "08f6e131", "metadata": { "execution": { "iopub.execute_input": "2025-06-13T13:19:48.542529Z", "iopub.status.busy": "2025-06-13T13:19:48.542076Z", "iopub.status.idle": "2025-06-13T13:19:49.085930Z", "shell.execute_reply": "2025-06-13T13:19:49.085206Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "spectroscopy_exp.compiled_program.render(\n", " channel_subplots=False,\n", " lo_frequency=5e9,\n", " sweep_index=5,\n", " sample_rate=5e9,\n", ")" ] }, { "cell_type": "raw", "id": "fb7d17ea", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "To execute this experiment, we can simply run" ] }, { "cell_type": "code", "execution_count": 7, "id": "35243ec7", "metadata": { "execution": { "iopub.execute_input": "2025-06-13T13:19:49.880704Z", "iopub.status.busy": "2025-06-13T13:19:49.880094Z", "iopub.status.idle": "2025-06-13T13:19:49.971920Z", "shell.execute_reply": "2025-06-13T13:19:49.971096Z" } }, "outputs": [], "source": [ "if run_on_hw:\n", " spectroscopy_exp.execute()\n", "else:\n", " # load in a previously executed version of this experiment\n", " spectroscopy_exp = qcs.load(\"QubitSpectroscopy.qcs\")" ] }, { "cell_type": "raw", "id": "18168b90", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "For the purposes of this demonstration, we added a second \"ancilla\" qubit to the\n", "Ramsey program and connected the physical output channels for our qubit to\n", "the digizer associated with the ancilla to allow us to capture the control pulse." ] }, { "cell_type": "code", "execution_count": 8, "id": "853f11e0", "metadata": { "execution": { "iopub.execute_input": "2025-06-13T13:19:49.978572Z", "iopub.status.busy": "2025-06-13T13:19:49.977508Z", "iopub.status.idle": "2025-06-13T13:19:49.992672Z", "shell.execute_reply": "2025-06-13T13:19:49.991772Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "spectroscopy_exp.plot_trace(channels=qcs.Qudits(1, \"ancilla\"))" ] }, { "cell_type": "raw", "id": "5426680d", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Here we can see the frequencies of the control pulse being updated at each point in\n", "the sweep. Note that our local oscillator (LO) frequency was set to 5 GHz for this\n", "example." ] }, { "cell_type": "raw", "id": "a40d55d2", "metadata": { "lines_to_next_cell": 2, "raw_mimetype": "text/restructuredtext" }, "source": [ "Fitting and calibration workflow\n", "--------------------------------\n", "\n", "Let's walk through how to extract the qubit resonance frequency from the Qubit\n", "Spectroscopy experiment and how to update the corresponding variables in our\n", "calibration set. For this example, we load in an experiment with simulated data\n", "that was imported at the beginning of the file:" ] }, { "cell_type": "code", "execution_count": 10, "id": "7ae64752", "metadata": { "execution": { "iopub.execute_input": "2025-06-13T13:19:50.270548Z", "iopub.status.busy": "2025-06-13T13:19:50.269970Z", "iopub.status.idle": "2025-06-13T13:19:50.304153Z", "shell.execute_reply": "2025-06-13T13:19:50.303346Z" } }, "outputs": [], "source": [ "spectroscopy_exp = SimulatedSpectroscopyExperiment(calibration_set, qubits)" ] }, { "cell_type": "code", "execution_count": 11, "id": "3013ab04", "metadata": { "execution": { "iopub.execute_input": "2025-06-13T13:19:50.307918Z", "iopub.status.busy": "2025-06-13T13:19:50.307546Z", "iopub.status.idle": "2025-06-13T13:19:50.466688Z", "shell.execute_reply": "2025-06-13T13:19:50.465661Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "IQ Value: %{y}
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-0.026996+0.027023j \n", "\n", "[1 rows x 100 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "spectroscopy_exp.get_iq()" ] }, { "cell_type": "raw", "id": "e6fdc7c9", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "The :py:meth:`~keysight.qcs.experiments.Experiment.fit` method takes this I/Q data\n", "and fits it to a complex lorentzian model, as specified by the built-in\n", ":py:class:`~keysight.qcs.analysis.ComplexLorentzian`.\n", "\n", "The result of the fit is an :py:class:`~keysight.qcs.analysis.EstimateCollection`,\n", "which contains individual :py:class:`~keysight.qcs.analysis.Estimate`\\s for each\n", "qubit that was fitted." ] }, { "cell_type": "code", "execution_count": 13, "id": "59366c30", "metadata": { "execution": { "iopub.execute_input": "2025-06-13T13:19:50.581188Z", "iopub.status.busy": "2025-06-13T13:19:50.580862Z", "iopub.status.idle": "2025-06-13T13:19:50.625004Z", "shell.execute_reply": "2025-06-13T13:19:50.624190Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "EstimateCollection(1)\n", "Estimate(amplitude=0.0004928379821974705, resonance=5079874827.71701, kappa=20192703.44965815, offset_real=-0.026998524628909344, ...)\n" ] } ], "source": [ "ec = spectroscopy_exp.fit()\n", "print(ec)\n", "print(ec.estimates[0])" ] }, { "cell_type": "raw", "id": "78c21348", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "The fitted and the pre-processed data can be visualized by calling the\n", ":py:meth:`~keysight.qcs.experiments.Experiment.plot` method:" ] }, { "cell_type": "code", "execution_count": 14, "id": "5fa0ded8", "metadata": { "execution": { "iopub.execute_input": "2025-06-13T13:19:50.628571Z", "iopub.status.busy": "2025-06-13T13:19:50.628237Z", "iopub.status.idle": "2025-06-13T13:19:50.797001Z", "shell.execute_reply": "2025-06-13T13:19:50.796331Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "spectroscopy_exp.plot()" ] }, { "cell_type": "raw", "id": "b110443d", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "We can then get the calibration value associated to this Spectroscopy Experiment by\n", "calling\n", ":py:meth:`~keysight.qcs.experiments.QubitSpectroscopy.get_updated_calibration_values`.\n", "This method will compute the new values for the calibration variable(s) of this\n", "experiment using the fit results." ] }, { "cell_type": "code", "execution_count": 15, "id": "784a107b", "metadata": { "execution": { "iopub.execute_input": "2025-06-13T13:19:50.803501Z", "iopub.status.busy": "2025-06-13T13:19:50.802945Z", "iopub.status.idle": "2025-06-13T13:19:50.847319Z", "shell.execute_reply": "2025-06-13T13:19:50.846463Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { "text/plain": [ "{'xy_pulse_frequencies': 5079874827.71701}" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "spectroscopy_exp.get_updated_calibration_values()" ] }, { "cell_type": "raw", "id": "729ff8bc", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "To check the current state of that variable in the calibration_set to confirm\n", "its update, one can do:" ] }, { "cell_type": "code", "execution_count": 16, "id": "bb6999c6", "metadata": { "execution": { "iopub.execute_input": "2025-06-13T13:19:50.851919Z", "iopub.status.busy": "2025-06-13T13:19:50.851574Z", "iopub.status.idle": "2025-06-13T13:19:50.860937Z", "shell.execute_reply": "2025-06-13T13:19:50.860093Z" } }, "outputs": [ { "data": { "text/plain": [ "array([5.1e+09])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "spectroscopy_exp.calibration_set.variables.xy_pulse_frequencies.value" ] }, { "cell_type": "raw", "id": "7592150a", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "The update is then done as follows:" ] }, { "cell_type": "code", "execution_count": 17, "id": "127f3ad4", "metadata": { "execution": { "iopub.execute_input": "2025-06-13T13:19:50.864759Z", "iopub.status.busy": "2025-06-13T13:19:50.864464Z", "iopub.status.idle": "2025-06-13T13:19:50.909026Z", "shell.execute_reply": "2025-06-13T13:19:50.907639Z" } }, "outputs": [ { "data": { "text/plain": [ "array([5.07987483e+09])" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "spectroscopy_exp.set_updated_calibration_values()\n", "spectroscopy_exp.calibration_set.variables.xy_pulse_frequencies.value" ] }, { "cell_type": "raw", "id": "82efe04e", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "The last line printing the new updated value, thus confirming that the update\n", "has been successful." ] } ], "metadata": { "jupytext": { "cell_metadata_filter": "raw_mimetype,nbsphinx,-all", "main_language": "python", "notebook_metadata_filter": "-all", "text_representation": { "extension": ".py", "format_name": "percent" } }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.15" } }, "nbformat": 4, "nbformat_minor": 5 }