Functions for Model Testing

This tutorial covers different methods of comparing data to given (fixed) QIP models. This is distinct from model-based tomography, which finds the best-fitting model for a data set within a space of models set by a Model object's parameterization. You might use this as a tool alongside or separate from GST. Perhaps you suspect that a given noisy QIP model is compatible with your data - model testing is the way to find out. Because there is no optimization involved, model testing requires much less time than GST does, and doens't place any requirements on which circuits are used in performing the test (though some circuits will give a more precise result).

Setup

First, after some usual imports, we'll create some test data based on a depolarized and rotated version of a standard 1-qubit model consisting of $I$ (the identity), $X(\pi/2)$ and $Y(\pi/2)$ gates.

In [ ]:
import pygsti
import numpy as np
import scipy
from scipy import stats
from pygsti.modelpacks import smq1Q_XYI
In [ ]:
datagen_model = smq1Q_XYI.target_model().depolarize(op_noise=0.05, spam_noise=0.1).rotate((0.05,0,0.03))
exp_list = pygsti.construction.make_lsgst_experiment_list(
    smq1Q_XYI.target_model(), smq1Q_XYI.prep_fiducials(), smq1Q_XYI.meas_fiducials(),
    smq1Q_XYI.germs(), [1,2,4,8,16,32,64])
ds = pygsti.construction.generate_fake_data(datagen_model, exp_list, n_samples=1000,
                                             sample_error='binomial', seed=100)

Step 1: Construct a test model

After we have some data, the first step is creating a model or models that we want to test. This just means creating a Model object containing the operations (including SPAM) found in the data set. We'll create several models that are meant to look like guesses (some including more types of noise) of the true underlying model.

In [ ]:
target_model = smq1Q_XYI.target_model()
test_model1 = target_model.copy()
test_model2 = target_model.depolarize(op_noise=0.07, spam_noise=0.07)
test_model3 = target_model.depolarize(op_noise=0.07, spam_noise=0.07).rotate( (0.02,0.02,0.02) )

Step 2: Test it!

There are three different ways to test a model. Note that in each case the default behavior (and the only behavior demonstrated here) is to never gauge-optimize the test Model. (Whenever gauge-optimized versions of an Estimate are useful for comparisons with other estimates, copies of the test Model are used without actually performing any modification of the original Model.)

Method1: do_model_test

First, you can do it "from scratch" by calling do_model_test, which has a similar signature as do_long_sequence_gst and folows its pattern of returning a Results object. The "estimateLabel" advanced option, which names the Estimate within the returned Results object, can be particularly useful.

In [ ]:
# creates a Results object with a "default" estimate
results = pygsti.do_model_test(test_model1, ds, target_model, 
                               smq1Q_XYI.prep_fiducials(), smq1Q_XYI.meas_fiducials(), smq1Q_XYI.germs(),
                               [1,2,4,8,16,32,64]) 

# creates a Results object with a "default2" estimate
results2 = pygsti.do_model_test(test_model2, ds, target_model, 
                               smq1Q_XYI.prep_fiducials(), smq1Q_XYI.meas_fiducials(), smq1Q_XYI.germs(),
                               [1,2,4,8,16,32,64], advanced_options={'estimate_label': 'default2'}) 

# creates a Results object with a "default3" estimate
results3 = pygsti.do_model_test(test_model3, ds, target_model, 
                               smq1Q_XYI.prep_fiducials(), smq1Q_XYI.meas_fiducials(), smq1Q_XYI.germs(),
                               [1,2,4,8,16,32,64], advanced_options={'estimate_label': 'default3'})

Like any other set of Results objects which share the same DataSet and operation sequences, we can collect all of these estimates into a single Results object and easily make a report containing all three.

In [ ]:
results.add_estimates(results2)
results.add_estimates(results3)

pygsti.report.construct_standard_report(
    results, title="Model Test Example Report", verbosity=1
).write_html("../tutorial_files/modeltest_report", auto_open=True, verbosity=1)

Method 2: add_model_test

Alternatively, you can add a model-to-test to an existing Results object. This is convenient when running GST via do_long_sequence_gst or do_stdpractice_gst has left you with a Results object and you also want to see how well a hand-picked model fares. Since the Results object already contains a DataSet and list of sequences, all you need to do is provide a Model. This is accomplished using the add_model_test method of a Results object.

In [ ]:
#Create some GST results using do_stdpractice_gst
gst_results = pygsti.do_stdpractice_gst(ds, target_model, 
                                        smq1Q_XYI.prep_fiducials(), smq1Q_XYI.meas_fiducials(), smq1Q_XYI.germs(),
                                        [1,2,4,8,16,32,64])

#Add a model to test
gst_results.add_model_test(target_model, test_model3, estimate_key='MyModel3')

#Create a report to see that we've added an estimate labeled "MyModel3"
pygsti.report.construct_standard_report(
    gst_results, title="GST with Model Test Example Report 1", verbosity=1
).write_html("../tutorial_files/gstwithtest_report1", auto_open=True, verbosity=1)

Method 3: models_to_test argument

Finally, yet another way to perform model testing alongside GST is by using the models_to_test argument of do_stdpractice_gst. This essentially combines calls to do_stdpractice_gst and Results.add_model_test (demonstrated above) with the added control of being able to specify the ordering of the estimates via the modes argument. To important remarks are in order:

  1. You must specify the names (keys of the models_to_test argument) of your test models in the comma-delimited string that is the modes argument. Just giving a dictionary of Models as models_to_test will not automatically test those models in the returned Results object.

  2. You don't actually need to run any GST modes, and can use do_stdpractice_gst in this way to in one call create a single Results object containing multiple model tests, with estimate names that you specify. Thus do_stdpractice_gst can replace the multiple do_model_test calls (with "estimateLabel" advanced options) followed by collecting the estimates using Results.add_estimates demonstrated under "Method 1" above.

In [ ]:
gst_results = pygsti.do_stdpractice_gst(ds, target_model, smq1Q_XYI.prep_fiducials(), smq1Q_XYI.meas_fiducials(), smq1Q_XYI.germs(),
                                       [1,2,4,8,16,32,64], modes="TP,Test2,Test3,Target", # You MUST 
                                       models_to_test={'Test2': test_model2, 'Test3': test_model3})

pygsti.report.construct_standard_report(
    gst_results, title="GST with Model Test Example Report 2", verbosity=1
).write_html("../tutorial_files/gstwithtest_report2", auto_open=True, verbosity=1)

Thats it! Now that you know more about model-testing you may want to go back to the overview of pyGST applications.