The pygsti package provides multiple levels of abstraction over the core Gate Set Tomography (GST) algorithms. This tutorial will show you how to run Gate Set Tomography on some simulated (generated) data, hopefully giving you an overall sense of what it takes (and how easy it is!) to run GST. For more details and options for running GST, see the GST circuits tutorial and the tutorial covering the different pyGSTi functions for running GST.
To run GST, we need three inputs:
a "target model" which describes the desired, or ideal, operations we want our experimental hardware to perform. In the example below, we use one of pyGSTi's "standard" models (see the tutorial on standard modules) - which acts on a single qubit with the following operations:
a list of circuits tailored to the target model; essentially a list of what experiments we need to run. Using a standard model makes things especially straightforward here, since the building blocks, called germ and fiducial circuits, needed to make good GST circuits have already been computed (see the tutorial on GST circuits).
data, in the form of experimental outcome counts, for each of the required sequences. In this example we'll generate "fake" or "simulated" data from a depolarized version of our ideal model. For more information about DataSet objects, see the tutorial on DataSets.
#Make print statements compatible with Python 2 and 3
from __future__ import print_function
#Import the pygsti module (always do this) and the standard XYI model
import pygsti
from pygsti.construction import std1Q_XYI
# 1) get the target Model
target_model = std1Q_XYI.target_model()
# 2) get the building blocks needed to specify which operation sequences are needed
prep_fiducials, meas_fiducials = std1Q_XYI.prepStrs, std1Q_XYI.effectStrs
germs = std1Q_XYI.germs
maxLengths = [1,2,4,8,16,32] # roughly gives the length of the sequences used by GST
# 3) generate "fake" data from a depolarized version of target_model
mdl_datagen = target_model.depolarize(op_noise=0.01, spam_noise=0.001)
listOfExperiments = pygsti.construction.make_lsgst_experiment_list(
target_model, prep_fiducials, meas_fiducials, germs, maxLengths)
ds = pygsti.construction.generate_fake_data(mdl_datagen, listOfExperiments, nSamples=1000,
sampleError="binomial", seed=1234)
#Note: from listOfExperiments we can also create an empty dataset file
# which has columns of zeros where actual data should go.
pygsti.io.write_empty_dataset("../tutorial_files/GettingStartedDataTemplate.txt", listOfExperiments,
"## Columns = 0 count, 1 count")
# After replacing the zeros with actual data, the data set can be
# loaded back into pyGSTi using the line below and used in the rest
# of this tutorial.
#ds = pygsti.io.load_dataset("tutorial_files/GettingStartedDataTemplate.txt")
Now that we have all of the inputs, we can run GST in a standard way using the do_stdpractice_gst function. For more information about this and related functions, see the GST methods tutorial. This returns a pygsti.report.Results object (see the Results tutorial), from which we can generate a report giving us a summary of the analysis.
#Run GST and create a report
results = pygsti.do_stdpractice_gst(ds, target_model, prep_fiducials, meas_fiducials,
germs, maxLengths, verbosity=3)
pygsti.report.create_standard_report(results, filename="../tutorial_files/gettingStartedReport",
title="GST Overview Tutorial Example Report", verbosity=2)
You can now open the file ../tutorial_files/gettingStartedReport/main.html in your browser (Firefox works best) to view the report. That's it! You've just run GST!
In the cell above, results is a Results object, which is used to generate a HTML report. For more information see the Results object tutorial and report generation tutorial.