{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Examples of tables and plots available from a `Workspace`\n", "\n", "PyGSTi's `Workspace` object is first a foremost a container and factory for plots and tables. At the most basic level, it can be used to generate nice output based on quantities (e.g. `Model`, `DataSet`, etc. objects) that you've computed or loaded within a notebook. For this, it's useful to call `init_notebook_mode` with `autodisplay=True` (see below) so that you don't have to `.display()` everything - `display()` gets called automatically when a plot or table is created.\n", "\n", "## Getting some results\n", "First, let's run Gate Set Tomography (GST) on the standard 1-qubit model to get some results to play with. We generate a few `DataSet` objects and then call `do_long_sequence_gst` to run GST, generating a `Results` object (essentially a container for `Model` objects). For more details, see the tutorials [GST overview tutorial](../algorithms/GST-Overview.ipynb), the [tutorial on GST functions](../algorithms/GST-Drivers.ipynb), and the [tutorial explaining the Results object](../objects/advanced/Results.ipynb). " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pygsti\n", "from pygsti.construction import std1Q_XYI" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "#The usual GST setup: we're going to run GST on the standard XYI 1-qubit model\n", "target_model = std1Q_XYI.target_model()\n", "fiducials = std1Q_XYI.fiducials\n", "germs = std1Q_XYI.germs\n", "maxLengths = [1,2]\n", "listOfExperiments = pygsti.construction.make_lsgst_experiment_list(\n", " target_model.operations.keys(), fiducials, fiducials, germs, maxLengths)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "#Create some datasets for analysis\n", "mdl_datagen1 = target_model.depolarize(op_noise=0.1, spam_noise=0.02)\n", "mdl_datagen2 = target_model.depolarize(op_noise=0.05, spam_noise=0.01).rotate(rotate=(0.01,0.01,0.01))\n", "\n", "ds1 = pygsti.construction.generate_fake_data(mdl_datagen1, listOfExperiments, nSamples=1000,\n", " sampleError=\"binomial\", seed=1234)\n", "ds2 = pygsti.construction.generate_fake_data(mdl_datagen2, listOfExperiments, nSamples=1000,\n", " sampleError=\"binomial\", seed=1234)\n", "ds3 = ds1.copy_nonstatic(); ds3.add_counts_from_dataset(ds2); ds3.done_adding_data()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "#Run GST on all three datasets\n", "target_model.set_all_parameterizations(\"TP\")\n", "results1 = pygsti.do_long_sequence_gst(ds1, target_model, fiducials, fiducials, germs, maxLengths, verbosity=0)\n", "results2 = pygsti.do_long_sequence_gst(ds2, target_model, fiducials, fiducials, germs, maxLengths, verbosity=0)\n", "results3 = pygsti.do_long_sequence_gst(ds3, target_model, fiducials, fiducials, germs, maxLengths, verbosity=0)\n", "\n", "#make some shorthand variable names for later\n", "tgt = results1.estimates['default'].models['target']\n", "\n", "ds1 = results1.dataset\n", "ds2 = results2.dataset\n", "ds3 = results3.dataset\n", "\n", "mdl1 = results1.estimates['default'].models['go0']\n", "mdl2 = results2.estimates['default'].models['go0']\n", "mdl3 = results3.estimates['default'].models['go0']\n", "\n", "gss = results1.circuit_structs['final']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Gallery of `Workspace` plots and tables.\n", "Now that we have some results, let's create a `Workspace` and make some plots and tables.\n", "\n", "To get tables and plots to display properly, one must run `init_notebook_mode`. The `connected` argument indicates whether you want to rely on an active internet connection. If `True`, then resources will be loaded from the web (e.g. a CDN), and if you save a notebook as HTML the file size may be smaller. If `False`, then all the needed resources (except MathJax) are provided by pyGSTi, and an `offline` directory is automatically created in the same directory as your notebook. This directory contains all the necessary resources, and must \"tag along\" with the notebook and any saved-as-HTML versions of it in order for everything to work. The second argument, `autodisplay`, determines whether tables and plots are automatically displayed when they are created. If `autodisplay=False`, one must call the `display()` member function of a table or plot to display it. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "
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\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pygsti.report import workspace\n", "w = workspace.Workspace()\n", "w.init_notebook_mode(connected=False, autodisplay=True) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plots and tables are created via member functions of a `Workspace` (`w` in our case). Note that you can start typing \"`w.`\" and TAB-complete to see the different things a `Workspace` can make for you. Furthermore, pressing SHIFT-TAB after the opening parenthesis of a function, e.g. after typing \"`w.GatesVsTargetTable(`\", will bring up Jupyter's help window showing you the function signature (the arguments you need to give the function).\n", "\n", "#### The remainder of this tutorial demonstrates some of the tables and plots you can create. \n", "Note that displayed objects have a resize handle in their lower right corner." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Gate \">Entanglement Infidelity Avg. Gate Infidelity 1/2 Trace Distance 1/2 Diamond-Dist Non-unitary Ent. Infidelity Non-unitary Avg. Gate Infidelity
Gi0.0743350.0495570.0760940.076360.0740980.049398
Gx0.0764040.0509360.0768510.0769260.0763410.050894
Gy0.0755330.0503560.0757640.0757850.07550.050334
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Prep/POVM Infidelity 1/2 Trace Distance 1/2 Diamond-Dist
ρ00.0423120.043714--
Mdefault-0.041560.0424190.043467
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L 2Δ(log L) k 2Δ(log L)-k √2k Nsigma Ns Np Rating
181.293836120.2938311.045361.849231\">★★★★★
2168.649613731.649616.552951.9116831\">★★★★★
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Model 2Δ(log L) k 2Δ(log L)-k √2k Nsigma Ns Np Rating
GS1168.649613731.649616.552951.9116831\">★★★★★
GS24061.6111373924.61116.552952×10216831\">★★
GS31002.246137865.246416.5529552.316831\">★★★
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Gate Choi matrix (Pauli-Product basis basis) Eigenvalue Magnitudes
Gi$ \\begin{pmatrix}\n", "0.945004 & 0.009417e^{i0.444\\pi} & 0.006951e^{i0.454\\pi} & 0.001022e^{-i0.776\\pi} \\\\ \n", "0.009417e^{-i0.444\\pi} & 0.015282 & 0.003276e^{i0.076\\pi} & 0.002139e^{i0.155\\pi} \\\\ \n", "0.006951e^{-i0.454\\pi} & 0.003276e^{-i0.076\\pi} & 0.020808 & 0.001819e^{-i0.644\\pi} \\\\ \n", "0.001022e^{i0.776\\pi} & 0.002139e^{-i0.155\\pi} & 0.001819e^{i0.644\\pi} & 0.018906\n", " \\end{pmatrix} $\n", "
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Gx$ \\begin{pmatrix}\n", "0.482049 & 0.462895e^{i0.501\\pi} & 0.002898e^{i0.512\\pi} & 0.000548e^{-i0.538\\pi} \\\\ \n", "0.462895e^{-i0.501\\pi} & 0.481094 & 0.004182e^{i0.005\\pi} & 0.000556e^{-i0.065\\pi} \\\\ \n", "0.002898e^{-i0.512\\pi} & 0.004182e^{-i0.005\\pi} & 0.018149 & 0.00183e^{i0.502\\pi} \\\\ \n", "0.000548e^{i0.538\\pi} & 0.000556e^{i0.065\\pi} & 0.00183e^{-i0.502\\pi} & 0.018709\n", " \\end{pmatrix} $\n", "
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Gy$ \\begin{pmatrix}\n", "0.481416 & 0.003826e^{i0.509\\pi} & 0.462881e^{i0.500\\pi} & 0.00059e^{-i0.430\\pi} \\\\ \n", "0.003826e^{-i0.509\\pi} & 0.018169 & 0.003254e^{-i0.013\\pi} & 0.000502e^{-i0.573\\pi} \\\\ \n", "0.462881e^{-i0.500\\pi} & 0.003254e^{i0.013\\pi} & 0.483841 & 0.000585e^{i0.057\\pi} \\\\ \n", "0.00059e^{i0.430\\pi} & 0.000502e^{i0.573\\pi} & 0.000585e^{-i0.057\\pi} & 0.016574\n", " \\end{pmatrix} $\n", "
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Gate Error Generator Pauli Hamiltonian Projections Pauli Stochastic Projections Pauli Affine Projections
Gi
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Gate Eigenvalues ($E$) Eigenvalues
Gi$ \\begin{pmatrix}\n", "1 \\\\ \n", "0.955547 \\\\ \n", "0.94681e^{i0.008\\pi} \\\\ \n", "0.94681e^{-i0.008\\pi}\n", " \\end{pmatrix} $\n", "
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Gx$ \\begin{pmatrix}\n", "1 \\\\ \n", "0.95118 \\\\ \n", "0.949197e^{i0.502\\pi} \\\\ \n", "0.949197e^{-i0.502\\pi}\n", " \\end{pmatrix} $\n", "
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Gy$ \\begin{pmatrix}\n", "1 \\\\ \n", "0.957594 \\\\ \n", "0.948507e^{i0.502\\pi} \\\\ \n", "0.948507e^{-i0.502\\pi}\n", " \\end{pmatrix} $\n", "
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Gate Ham. Evals. Rotn. angle Rotn. axis Log Error Axis angle w/Gi Axis angle w/Gx Axis angle w/Gy
Gi$ \\begin{pmatrix}\n", "-0.006533 \\\\ \n", "0.006533\n", " \\end{pmatrix} $\n", "π0.00924π
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Gx$ \\begin{pmatrix}\n", "-0.351156 \\\\ \n", "0.351156\n", " \\end{pmatrix} $\n", "π0.496609π
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Gy$ \\begin{pmatrix}\n", "-0.353186 \\\\ \n", "0.353186\n", " \\end{pmatrix} $\n", "π0.49948π
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Gate Ham. Evals. Rotn. angle Rotn. axis Log Error Axis angle w/Gii Axis angle w/Gix Axis angle w/Giy Axis angle w/Gxi Axis angle w/Gyi Axis angle w/Gxx Axis angle w/Gyy Axis angle w/Gxy Axis angle w/Gyx
Gii$ \\begin{pmatrix}\n", "0 \\\\ \n", "0 \\\\ \n", "0 \\\\ \n", "0\n", " \\end{pmatrix} $\n", "π0π
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Gix$ \\begin{pmatrix}\n", "0.25 \\\\ \n", "-0.25 \\\\ \n", "0.25 \\\\ \n", "-0.25\n", " \\end{pmatrix} $\n", "π0.5π
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Giy$ \\begin{pmatrix}\n", "0.25 \\\\ \n", "-0.25 \\\\ \n", "0.25 \\\\ \n", "-0.25\n", " \\end{pmatrix} $\n", "π0.5π
\n", "
\n", "
\n", "
\n", "\n", "
\n", "		0--0.5π0.5π0.5π0.499987π0.25π0.25π0.499987π
Gxi$ \\begin{pmatrix}\n", "0.25 \\\\ \n", "-0.25 \\\\ \n", "0.25 \\\\ \n", "-0.25\n", " \\end{pmatrix} $\n", "π0.5π
\n", "
\n", "
\n", "
\n", "\n", "
\n", "		0--0.5π0.5π0.5π0.25π0.5π0.25π0.500008π
Gyi$ \\begin{pmatrix}\n", "-0.25 \\\\ \n", "-0.25 \\\\ \n", "0.25 \\\\ \n", "0.25\n", " \\end{pmatrix} $\n", "π0.5π
\n", "
\n", "
\n", "
\n", "\n", "
\n", "		0--0.5π0.5π0.5π0.499987π0.25π0.500008π0.25π
Gxx$ \\begin{pmatrix}\n", "-0.500005 \\\\ \n", "0.500006 \\\\ \n", "-4\\times 10^{-7} \\\\ \n", "0\n", " \\end{pmatrix} $\n", "π0.707114π
\n", "
\n", "
\n", "
\n", "\n", "
\n", "		0.003505--0.25π0.499987π0.25π0.499987π0.499982π0.33333π0.33333π
Gyy$ \\begin{pmatrix}\n", "-0.500557 \\\\ \n", "0.500557 \\\\ \n", "0 \\\\ \n", "0\n", " \\end{pmatrix} $\n", "π0.707895π
\n", "
\n", "
\n", "
\n", "\n", "
\n", "		0--0.5π0.25π0.5π0.25π0.499982π0.33334π0.333323π
Gxy$ \\begin{pmatrix}\n", "-0.500007 \\\\ \n", "0.500006 \\\\ \n", "0.000001 \\\\ \n", "0\n", " \\end{pmatrix} $\n", "π0.707116π
\n", "
\n", "
\n", "
\n", "\n", "
\n", "		0.00368--0.500009π0.25π0.25π0.500008π0.33333π0.33334π0.500009π
Gyx$ \\begin{pmatrix}\n", "0.499981 \\\\ \n", "-0.49998 \\\\ \n", "-0.000002 \\\\ \n", "0\n", " \\end{pmatrix} $\n", "π0.707079π
\n", "
\n", "
\n", "
\n", "\n", "
\n", "		0.003891--0.25π0.499987π0.500008π0.25π0.33333π0.333323π0.500009π
\n", "
\n", "\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# import importlib\n", "# importlib.reload(pygsti.report.workspace)\n", "# importlib.reload(pygsti.report.workspaceplots)\n", "# importlib.reload(pygsti.report.workspacetables)\n", "\n", "#Note 2Q angle decompositions\n", "from pygsti.construction import std2Q_XXYYII\n", "from pygsti.construction import std2Q_XYCNOT\n", "\n", "w.GateDecompTable(std2Q_XXYYII.target_model(), std2Q_XXYYII.target_model())\n", "\n", "import scipy\n", "I = np.array([[1,0],[0,1]],'complex')\n", "X = np.array([[0,1],[1,0]],'complex')\n", "Y = np.array([[0,1j],[-1j,0]],'complex')\n", "XX = np.kron(X,X)\n", "YY = np.kron(Y,Y)\n", "IX = np.kron(I,X)\n", "XI = np.kron(X,I)\n", "testU = scipy.linalg.expm(-1j*np.pi/2*XX)\n", "testS = pygsti.unitary_to_process_mx(testU)\n", "testS = pygsti.change_basis(testS,\"std\",\"pp\")\n", "\n", "#mdl_decomp = std2Q_XYCNOT.target_model()\n", "#mdl_decomp.operations['Gtest'] = testS\n", "#w.GateDecompTable(mdl_decomp, mdl_decomp)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Gate \">Entanglement Infidelity Avg. Gate Infidelity 1/2 Trace Distance 1/2 Diamond-Dist Non-unitary Ent. Infidelity Non-unitary Avg. Gate Infidelity
Gi0.0743350.0495570.0760940.076360.0740980.049398
Gx0.0764040.0509360.0768510.0769260.0763410.050894
Gy0.0755330.0503560.0757640.0757850.07550.050334
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