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Tutorial updates for single-param wildcard and procedural error bars.
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jupyter_notebooks/Tutorials/reporting/ProceduralErrorBars.ipynb
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Procedural Error Bars\n", | ||
| "\n", | ||
| "One other way we can use the `pygsti.report.reportables` module described in the [ModelAnalysisMetrics tutorial](ModelAnalysisMetrics.ipynb) is to procedurally generate error bars for any quantity you want.\n", | ||
| "\n", | ||
| "First, let's simulate a noisy GST experiment" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import pygsti\n", | ||
| "from pygsti.modelpacks import smq1Q_XY\n", | ||
| "from pygsti.report import reportables as rptbl, modelfunction as modelfn" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "target_model = smq1Q_XY.target_model()\n", | ||
| "\n", | ||
| "L=128\n", | ||
| "edesign = smq1Q_XY.create_gst_experiment_design(L)\n", | ||
| "\n", | ||
| "noisy_model = target_model.randomize_with_unitary(.1)\n", | ||
| "noisy_model = noisy_model.depolarize(.05)\n", | ||
| "\n", | ||
| "N=64\n", | ||
| "dataset = pygsti.data.simulate_data(noisy_model,edesign,N)\n", | ||
| "\n", | ||
| "\n", | ||
| "gst_proto = pygsti.protocols.StandardGST(modes=['full TP','CPTPLND','Target'],verbosity=2)\n", | ||
| "data = pygsti.protocols.ProtocolData(edesign,dataset)\n", | ||
| "results = gst_proto.run(data)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Now let's compute error bars on the CPTP estimate, and then get a 95% confidence interval \"view\" from the `ConfidenceRegionFactory`." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "crfact = results.estimates['CPTPLND'].add_confidence_region_factory('stdgaugeopt', 'final')\n", | ||
| "crfact.compute_hessian(comm=None, mem_limit=3.0*(1024.0)**3) #optionally use multiple processors & set memlimit\n", | ||
| "crfact.project_hessian('intrinsic error')\n", | ||
| "\n", | ||
| "crf_view = results.estimates['CPTPLND'].confidence_region_factories['stdgaugeopt','final'].view(95)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Finally, we can construct `pygsti.report.ModelFunction` objects that take a function which computes some observable from a model and the extracted view from above to compute error bars on that quantity of interest.\n", | ||
| "\n", | ||
| "One common thing to check is error bars on the process matrices. The `ModelFunction` in this case only needs to return the operation:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "final_model = results.estimates['CPTPLND'].models['stdgaugeopt'].copy()" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "def get_op(model, lbl):\n", | ||
| " return model[lbl]\n", | ||
| "get_op_modelfn = modelfn.modelfn_factory(get_op)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "rptbl.evaluate(get_op_modelfn(final_model, (\"Gxpi2\", 0)), crf_view)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "rptbl.evaluate(get_op_modelfn(final_model, (\"Gypi2\", 0)), crf_view)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "But we can also create model functions that perform more complicated actions, such as computing other reportables." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# Note that when creating ModelFunctions in this way, the model where you want the quantity evaluated must be the first argument\n", | ||
| "def ddist(model, ideal_model, lbl, basis):\n", | ||
| " return rptbl.half_diamond_norm(model[lbl], ideal_model[lbl], basis)\n", | ||
| "ddist_modelfn = modelfn.modelfn_factory(ddist)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "rptbl.evaluate(ddist_modelfn(final_model, target_model, (\"Gxpi2\", 0), 'pp'), crf_view)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "rptbl.evaluate(ddist_modelfn(final_model, target_model, (\"Gypi2\", 0), 'pp'), crf_view)" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "pygsti", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.11.5" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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