{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "d2c813a8", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:15:41.500403Z", "iopub.status.busy": "2024-10-11T06:15:41.500007Z", "iopub.status.idle": "2024-10-11T06:15:41.506058Z", "shell.execute_reply": "2024-10-11T06:15:41.505197Z" }, "nbsphinx": "hidden" }, "outputs": [], "source": [ "# Copyright 2024 Keysight Technologies Inc." ] }, { "cell_type": "raw", "id": "1b8785fa", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Running a program on hardware\n", "=============================\n", "\n", "This guide gives an overview of the initial steps to set up and run a \"Hello\n", "Quantum World!\" program on hardware using QCS. We will assume that the physical\n", "configuration of the hardware is already set up and that all required software has\n", "been installed via the Test Station Manager (TSM).\n", "\n", "Setting up\n", "----------\n", "\n", "The first step is to define the software instances of the channels used to\n", "send and receive signals. In this example, we use two AWG channels and two\n", "digitizers." ] }, { "cell_type": "code", "execution_count": 2, "id": "fe522248", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:15:41.510543Z", "iopub.status.busy": "2024-10-11T06:15:41.510203Z", "iopub.status.idle": "2024-10-11T06:15:44.642019Z", "shell.execute_reply": "2024-10-11T06:15:44.641076Z" } }, "outputs": [], "source": [ "import keysight.qcs as qcs\n", "\n", "# set the following to True when connected to hardware:\n", "run_on_hw = False\n", "\n", "# instantiate channels representing two AWGs and two digitizers\n", "awgs = qcs.Channels(range(2), \"readoutawg\")\n", "digs = qcs.Channels(range(2), \"readoutreceiver\")" ] }, { "cell_type": "raw", "id": "3095f737", "metadata": { "lines_to_next_cell": 0, "raw_mimetype": "text/restructuredtext" }, "source": [ "The virtual channels above then have to be linked to the physical channels\n", "present in hardware. See :doc:`channel_mappers` for details. For the purposes of this\n", "demonstration, we load a previously created\n", ":py:class:`~keysight.qcs.channels.ChannelMapper`\\:" ] }, { "cell_type": "code", "execution_count": 3, "id": "ba4e2745", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:15:44.646095Z", "iopub.status.busy": "2024-10-11T06:15:44.645650Z", "iopub.status.idle": "2024-10-11T06:15:44.786790Z", "shell.execute_reply": "2024-10-11T06:15:44.785843Z" } }, "outputs": [], "source": [ "mapper = qcs.load(\"../../assets/channel_map.qcs\")" ] }, { "cell_type": "raw", "id": "d9153298", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Defining a program\n", "------------------\n", "\n", "In this section, we define a program to run on the hardware configuration specified\n", "above. For this example, we send a Gaussian waveform through the AWG channels and\n", "connect its output to our downconverter/digitizer channels to collect the results. The\n", "local oscillator (LO) frequency of our AWG and downconverter is set to 4 GHz." ] }, { "cell_type": "code", "execution_count": 4, "id": "130e2a47", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:15:44.790666Z", "iopub.status.busy": "2024-10-11T06:15:44.790323Z", "iopub.status.idle": "2024-10-11T06:15:44.940644Z", "shell.execute_reply": "2024-10-11T06:15:44.939883Z" }, "lines_to_next_cell": 2 }, "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", "
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\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "metadata": { "text/html": { "isolated": true } }, "output_type": "display_data" } ], "source": [ "# instantiate an empty program\n", "program = qcs.Program()\n", "\n", "# add a 4.15 GHz RF waveform with a Gaussian envelope to the program\n", "gauss = qcs.RFWaveform(80e-9, qcs.GaussianEnvelope(num_sigma=4), 1, 4.15e9)\n", "program.add_waveform(gauss, awgs)\n", "\n", "# add an acquisition to the program\n", "program.add_acquisition(80e-9, digs)\n", "\n", "# specify the number of shots\n", "program.n_shots(10)\n", "\n", "program.draw()" ] }, { "cell_type": "raw", "id": "f6d975b6", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Running on hardware\n", "-------------------\n", "\n", "To execute a program, we need to instantiate the\n", ":py:class:`~keysight.qcs.executor.HclBackend` with the\n", ":py:class:`~keysight.qcs.channels.ChannelMapper` specified above and add it to the\n", ":py:class:`~keysight.qcs.executor.Executor` as a pass." ] }, { "cell_type": "code", "execution_count": 5, "id": "f7801c18", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:15:44.944266Z", "iopub.status.busy": "2024-10-11T06:15:44.943935Z", "iopub.status.idle": "2024-10-11T06:15:45.116012Z", "shell.execute_reply": "2024-10-11T06:15:45.115076Z" } }, "outputs": [], "source": [ "if run_on_hw:\n", " # initialize the backend pass\n", " backend = qcs.HclBackend(channel_mapper=mapper)\n", " # the executor returns the program populated with results\n", " program = qcs.Executor(backend).execute(program)\n", " # (optional) export the data to an HDF5 file\n", " program.to_hdf5(\"program1.hdf5\")\n", "\n", "# we are loading a previously run program here for this example\n", "program = qcs.load(\"program1.hdf5\")" ] }, { "cell_type": "raw", "id": "795cfbbb", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ " .. note::\n", "\n", " When executing multiple programs with HCL, it is possible to execute them\n", " in sequence or in parallel. This is specified with the ``blocking``\n", " property of :py:class:`~keysight.qcs.executor.HclBackend`\\. When\n", " ``blocking = True``, programs are run sequentially and Python blocks any further\n", " execution of the current script. When ``blocking = False``, the execution exits\n", " immediately and the program continues to run in the background, enabling\n", " the execution of parallel programs on separate chassis.\n" ] }, { "cell_type": "raw", "id": "5cdca0f6", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Retrieving data\n", "---------------\n", "\n", "After a program is run, output data is stored in a database managed by QCS. Users can\n", "access the data through the program's ``get_*`` methods, or export the data to\n", "an HDF5 file for later access using the\n", ":py:meth:`~keysight.qcs.programs.Program.to_hdf5` method on the program, as shown\n", "above. A saved program is then loaded using the :py:meth:`~keysight.qcs.utils.load`\\\n", "method.\n", "\n", "The program's data can be accessed through the following methods:\n", "\n", "- :py:meth:`~keysight.qcs.programs.Program.get_trace` to access trace data,\n", "- :py:meth:`~keysight.qcs.programs.Program.get_spectrum` to calculate the power\n", " spectrum of the trace data,\n", "- :py:meth:`~keysight.qcs.programs.Program.get_iq` to access the I/Q data after\n", " demodulation on either hardware or software with a user-specified\n", " :py:class:`~keysight.qcs.channels.acquisitions.IntegrationFilter`\\,\n", "- :py:meth:`~keysight.qcs.programs.Program.get_classified` to access classified\n", " I/Q data using a user-specified classifier, such as the\n", " :py:class:`~keysight.qcs.channels.acquisitions.MinimumDistanceClassifier`\\.\n", "\n", "All these methods take an optional argument ``avg`` which when set to ``True`` will\n", "average the data over shots.\n", "\n", " .. note::\n", "\n", " If the I/Q demodulation is performed on hardware, as can be specified by\n", " setting ``hw_demod=True`` in the :py:class:`~keysight.qcs.executor.HclBackend`\\,\n", " no trace data will be stored.\n", "\n", "These methods all return Pandas dataframes:" ] }, { "cell_type": "code", "execution_count": 6, "id": "59be8ff0", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:15:45.120100Z", "iopub.status.busy": "2024-10-11T06:15:45.119755Z", "iopub.status.idle": "2024-10-11T06:15:45.258016Z", "shell.execute_reply": "2024-10-11T06:15:45.256949Z" } }, "outputs": [ { "data": { "text/html": [ "
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Individual columns in the dataframe can be accessed by indexing\n", "the dataframe with first the channel and then the shot number. Note that other than\n", "the first column index (the channel), all other column indices are strings." ] }, { "cell_type": "code", "execution_count": 7, "id": "cad7f969", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:15:45.261897Z", "iopub.status.busy": "2024-10-11T06:15:45.261307Z", "iopub.status.idle": "2024-10-11T06:15:45.288007Z", "shell.execute_reply": "2024-10-11T06:15:45.287215Z" } }, "outputs": [ { "data": { "text/plain": [ "0 0.000220\n", "1 -0.000439\n", "2 0.000000\n", "3 0.000439\n", "4 0.000220\n", " ... \n", "379 0.000220\n", "380 -0.000659\n", "381 0.000659\n", "382 0.000220\n", "383 -0.001538\n", "Name: 0, Length: 384, dtype: float64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = program.get_trace()\n", "\n", "# access trace data for channel 0, first shot\n", "df[digs[0]][\"0\"]" ] }, { "cell_type": "raw", "id": "381f7ce7", "metadata": { "lines_to_next_cell": 0, "raw_mimetype": "text/restructuredtext" }, "source": [ "This data can also be plotted using:" ] }, { "cell_type": "code", "execution_count": 8, "id": "8d82bf63", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:15:45.292033Z", "iopub.status.busy": "2024-10-11T06:15:45.291458Z", "iopub.status.idle": "2024-10-11T06:15:45.727937Z", "shell.execute_reply": "2024-10-11T06:15:45.726966Z" } }, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "dx": 2.0833333333333334e-10, "legendgroup": "readoutreceiver, labels: (0,)", "legendgrouptitle": { "text": "readoutreceiver, labels: (0,)" }, "name": "Re(V) for (Channels(labels=[0], name=readoutreceiver, absolute_phase=False),)", "type": "scatter", "x0": 0, "xaxis": "x", "y": [ 0.0002856445312500001, 4.3945312500000046e-05, -2.197265625e-05, 0.00024169921875, 0.0005712890625, 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "program.plot_trace(channel_subplots=False)" ] }, { "cell_type": "raw", "id": "8ca5aaaa", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "The trace above has the expected Gaussian envelope and oscillates with a frequency\n", "of 150 MHz, which is exactly the difference frequency between our LO frequency (4 GHz)\n", "and our pulse frequency (4.15 GHz).\n", "\n", "The ``channel_subplots`` argument in :py:meth:`qcs.programs.Program.plot_trace`\n", "determines whether or not the results from different channels are put on the same plot\n", "or whether subplots are created. By default, plotted data is averaged over shots. To\n", "disable this behavior, set ``avg=False``:" ] }, { "cell_type": "code", "execution_count": 9, "id": "19842d99", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:15:45.760801Z", "iopub.status.busy": "2024-10-11T06:15:45.760093Z", "iopub.status.idle": "2024-10-11T06:15:45.914231Z", "shell.execute_reply": "2024-10-11T06:15:45.913312Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "dx": 2.0833333333333334e-10, "legendgroup": "readoutreceiver, labels: (0,)", "legendgrouptitle": { "text": "readoutreceiver, labels: (0,)" }, "name": "Re(V) for 0", "type": "scatter", "x0": 0, "xaxis": "x", "y": [ 0.0002197265625, -0.000439453125, 0.0, 0.000439453125, 0.0002197265625, 0.0, -0.0010986328125, -0.0002197265625, -0.000439453125, -0.00087890625, 0.0006591796875, 0.0006591796875, 0.0006591796875, 0.00087890625, 0.000439453125, 0.0, -0.0010986328125, -0.0006591796875, 0.00087890625, 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} }, "yaxis": { "anchor": "x", "domain": [ 0.0, 1.0 ], "title": { "text": "Power Spectrum of Fractional Voltage" } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "program.plot_spectrum(channel_subplots=False)" ] }, { "cell_type": "raw", "id": "b25f5db1", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "In most applications, we are more interested in I/Q-demodulated and classified data\n", "rather than the full trace data. Here the trace is integrated after being multiplied\n", "by an integration filter, which allows us to extract phase, frequency and amplitude\n", "information. Classification is performed by preparing each quantum state, measuring\n", "its distribution on the I/Q plane and using a classifier to distinguish them.\n", "\n", "This demodulation can be performed either in software or hardware, and requires the\n", "use of an :py:class:`~keysight.qcs.channels.acquisitions.IntegrationFilter`\\. For the\n", "program above, we can define an integration filter using the same RF waveform and use\n", "it to retrieve software-demodulated I/Q data:" ] }, { "cell_type": "code", "execution_count": 11, "id": "9b983748", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:15:46.032801Z", "iopub.status.busy": "2024-10-11T06:15:46.032459Z", "iopub.status.idle": "2024-10-11T06:15:46.094948Z", "shell.execute_reply": "2024-10-11T06:15:46.094107Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " 0 \\\n", "(((Channels(labels=[0], name=readoutreceiver, a... -0.208354-0.091978j \n", "(((Channels(labels=[1], name=readoutreceiver, a... 0.081295-0.194943j \n", "\n", " 1 \\\n", "(((Channels(labels=[0], name=readoutreceiver, a... -0.207841-0.093049j \n", "(((Channels(labels=[1], name=readoutreceiver, a... 0.082409-0.194152j \n", "\n", " 2 \\\n", "(((Channels(labels=[0], name=readoutreceiver, a... -0.207405-0.093960j \n", "(((Channels(labels=[1], name=readoutreceiver, a... 0.083156-0.194026j \n", "\n", " 3 \\\n", "(((Channels(labels=[0], name=readoutreceiver, a... -0.206787-0.094929j \n", "(((Channels(labels=[1], name=readoutreceiver, a... 0.084314-0.193498j \n", "\n", " 4 \\\n", "(((Channels(labels=[0], name=readoutreceiver, a... -0.208112-0.091943j \n", "(((Channels(labels=[1], name=readoutreceiver, a... 0.081304-0.194818j \n", "\n", " 5 \\\n", "(((Channels(labels=[0], name=readoutreceiver, a... -0.207649-0.093948j \n", "(((Channels(labels=[1], name=readoutreceiver, a... 0.082934-0.193860j \n", "\n", " 6 \\\n", "(((Channels(labels=[0], name=readoutreceiver, a... -0.206596-0.095823j \n", "(((Channels(labels=[1], name=readoutreceiver, a... 0.084916-0.193247j \n", "\n", " 7 \\\n", "(((Channels(labels=[0], name=readoutreceiver, a... -0.207161-0.094855j \n", "(((Channels(labels=[1], name=readoutreceiver, a... 0.084050-0.193561j \n", "\n", " 8 \\\n", "(((Channels(labels=[0], name=readoutreceiver, a... -0.208213-0.092252j \n", "(((Channels(labels=[1], name=readoutreceiver, a... 0.081635-0.194723j \n", "\n", " 9 \n", "(((Channels(labels=[0], name=readoutreceiver, a... -0.208361-0.091554j \n", "(((Channels(labels=[1], name=readoutreceiver, a... 0.080858-0.194921j " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "int_filter = qcs.IntegrationFilter(gauss)\n", "\n", "program.get_iq(integration_filter=int_filter, stack_channels=True)" ] }, { "cell_type": "raw", "id": "7a967cb9", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Note that the :py:meth:`~keysight.qcs.programs.Program.get_iq` method can\n", "stack the channel index as rows in the dataframe, to allow users to spot correlations\n", "in the data more easily.\n", "\n", "To enable I/Q demodulation in hardware, we need to pass the integration filter\n", "to the program's acquisition, as shown in :doc:`sweep_examples`\\." ] } ], "metadata": { "jupytext": { "cell_metadata_filter": "nbsphinx,raw_mimetype,-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": "3.9.6" } }, "nbformat": 4, "nbformat_minor": 5 }