/
test_drivers.py
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/
test_drivers.py
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import unittest # noqa: E999
import numpy as np
from pygsti.forwardsims.mapforwardsim import MapForwardSimulator
from numpy.linalg import norm
import pygsti
from pygsti.modelpacks import smq1Q_XY as std
from ..testutils import BaseTestCase, compare_files, temp_files, regenerate_references
class DriversTestCase(BaseTestCase):
def setUp(self):
super(DriversTestCase, self).setUp()
self.model = std.target_model()
self.germs = std.germs(lite=True)
self.prep_fiducials = std.prep_fiducials()[0:4]
self.meas_fiducials = std.meas_fiducials()[0:3]
self.maxLens = [1,2]
self.op_labels = list(self.model.operations.keys())
self.lsgstStrings = pygsti.circuits.create_lsgst_circuit_lists(
self.op_labels, self.prep_fiducials, self.meas_fiducials, self.germs, self.maxLens)
datagen_gateset = self.model.copy()
datagen_gateset = datagen_gateset.depolarize(op_noise=0.05, spam_noise=0.1)
self.ds = pygsti.data.simulate_data(
datagen_gateset, self.lsgstStrings[-1],
num_samples=1000,sample_error='binomial', seed=100)
class TestDriversMethods(DriversTestCase):
def test_longSequenceGST_fiducialPairReduction(self):
ds = self.ds
maxLens = self.maxLens
#Make list-of-lists of GST operation sequences
fullStructs = pygsti.circuits.create_lsgst_circuit_lists(
self.model, self.prep_fiducials, self.meas_fiducials, self.germs, self.maxLens)
lens = [ len(strct) for strct in fullStructs ]
self.assertEqual(lens, [19, 33])
#Global FPR
fidPairs = pygsti.alg.find_sufficient_fiducial_pairs(
self.model, self.prep_fiducials, self.meas_fiducials, self.germs,
search_mode="random", n_random=10, seed=1234,
verbosity=0, mem_limit=int(2*(1024)**3), minimum_pairs=2,
test_lengths = (64, 512))
gfprStructs = pygsti.circuits.create_lsgst_circuit_lists(
self.model, self.prep_fiducials, self.meas_fiducials, self.germs, maxLens,
fid_pairs=fidPairs)
lens = [ len(strct) for strct in gfprStructs ]
#self.assertEqual(lens, [92,100,130]) #,163,196,229]
#can't test reliably b/c "random" above
# means different answers on different systems
gfprExperiments = pygsti.circuits.create_lsgst_circuits(
self.model, self.prep_fiducials, self.prep_fiducials, self.germs, maxLens,
fid_pairs=fidPairs)
result = pygsti.run_long_sequence_gst_base(ds, self.model, gfprStructs, verbosity=0,
disable_checkpointing = True,
advanced_options= {'max_iterations':3})
pygsti.report.construct_standard_report(result, title ="GFPR report", verbosity=0).write_html(temp_files + "/full_report_GFPR")
#Per-germ FPR
fidPairsDict = pygsti.alg.find_sufficient_fiducial_pairs_per_germ(
self.model, self.prep_fiducials, self.meas_fiducials, self.germs,
search_mode="random", constrain_to_tp=True,
n_random=10, seed=1234, verbosity=0,
mem_limit=int(2*(1024)**3))
pfprStructs = pygsti.circuits.create_lsgst_circuit_lists(
self.model, self.prep_fiducials, self.meas_fiducials, self.germs, maxLens,
fid_pairs=fidPairsDict) #note: fidPairs arg can be a dict too!
lens = [ len(strct) for strct in pfprStructs ]
#self.assertEqual(lens, [92,99,138]) # ,185,233,281]
#can't test reliably b/c "random" above
# means different answers on different systems
pfprExperiments = pygsti.circuits.create_lsgst_circuits(
self.model, self.prep_fiducials, self.meas_fiducials, self.germs, maxLens,
fid_pairs=fidPairsDict)
result = pygsti.run_long_sequence_gst_base(ds, self.model, pfprStructs, verbosity=0,
disable_checkpointing = True,
advanced_options= {'max_iterations':3})
pygsti.report.construct_standard_report(result, title="PFPR report", verbosity=0).write_html(temp_files + "/full_report_PFPR")
def test_longSequenceGST_randomReduction(self):
ds = self.ds
ts = "whole germ powers"
maxLens = self.maxLens
#Without fixed initial fiducial pairs
fidPairs = None
reducedLists = pygsti.circuits.create_lsgst_circuit_lists(
self.model.operations.keys(), self.prep_fiducials, self.meas_fiducials, self.germs,
maxLens, fidPairs, ts, keep_fraction=0.25, keep_seed=1234)
result = self.runSilent(pygsti.run_long_sequence_gst_base,
ds, self.model, reducedLists,
advanced_options={'truncScheme': ts},
disable_checkpointing=True)
#create a report...
pygsti.report.construct_standard_report(result, title="RFPR report", verbosity=0).write_html(temp_files + "/full_report_RFPR")
def test_longSequenceGST_CPTP(self):
ds = self.ds
target_model = self.model
target_model.set_all_parameterizations("CPTPLND")
maxLens = self.maxLens
result = self.runSilent(pygsti.run_long_sequence_gst,
ds, target_model, self.prep_fiducials, self.meas_fiducials,
self.germs, maxLens, disable_checkpointing=True,
advanced_options= {'max_iterations':3})
#create a report...
pygsti.report.construct_standard_report(result, title="CPTP Gates report", verbosity=0).write_html(temp_files + "/full_report_CPTPGates")
def test_longSequenceGST_Sonly(self):
ds = self.ds
target_model = self.model.copy()
target_model.set_all_parameterizations("S")
maxLens = self.maxLens
result = self.runSilent(pygsti.run_long_sequence_gst,
ds, target_model, self.prep_fiducials, self.meas_fiducials,
self.germs, maxLens, disable_checkpointing=True, advanced_options= {'max_iterations':3})
#create a report...
pygsti.report.construct_standard_report(result, title="SGates report", verbosity=0).write_html(temp_files + "/full_report_SGates")
def test_longSequenceGST_GLND(self):
#General Lindbladian parameterization (allowed to be non-CPTP)
ds = self.ds
target_model = self.model.copy()
#No set_all_parameterizations option for this one, since it probably isn't so useful
for lbl,gate in target_model.operations.items():
target_model.operations[lbl] = pygsti.modelmembers.operations.convert(gate, "GLND", "gm")
target_model.default_gauge_group = pygsti.models.gaugegroup.UnitaryGaugeGroup(target_model.state_space, "gm")
#Lindblad gates only know how to do unitary transforms currently, even though
# in the non-cptp case it they should be able to transform generally.
maxLens = self.maxLens
result = self.runSilent(pygsti.run_long_sequence_gst,
ds, target_model, self.prep_fiducials, self.meas_fiducials,
self.germs, maxLens, disable_checkpointing=True, advanced_options= {'max_iterations':3})
#create a report...
pygsti.report.construct_standard_report(result, title="GLND report", verbosity=0).write_html( temp_files + "/full_report_GLND")
def test_longSequenceGST_HplusS(self):
ds = self.ds
target_model = self.model.copy()
target_model.set_all_parameterizations("H+S")
maxLens = self.maxLens
result = self.runSilent(pygsti.run_long_sequence_gst,
ds, target_model, self.prep_fiducials, self.meas_fiducials,
self.germs, maxLens, disable_checkpointing=True, advanced_options= {'max_iterations':3})
#create a report...
pygsti.report.construct_standard_report(result, title= "HpS report", verbosity=0).write_html(temp_files + "/full_report_HplusSGates")
def test_longSequenceGST_badfit(self):
ds = self.ds
#lower bad-fit threshold to zero to trigger bad-fit additional processing
maxLens = self.maxLens
result = self.runSilent(pygsti.run_long_sequence_gst,
ds, self.model.copy(), self.prep_fiducials, self.meas_fiducials,
self.germs, maxLens, advanced_options={'bad_fit_threshold': -100, 'max_iterations':3},
disable_checkpointing=True)
pygsti.report.construct_standard_report(result, title="badfit report", verbosity=0).write_html(temp_files + "/full_report_badfit")
def test_stdpracticeGST(self):
ds = self.ds
mdl_guess = self.model.copy()
mdl_guess = mdl_guess.depolarize(op_noise=0.01,spam_noise=0.01)
#lower bad-fit threshold to zero to trigger bad-fit additional processing
maxLens = self.maxLens
result = self.runSilent(pygsti.run_stdpractice_gst,
ds, self.model.copy(), self.prep_fiducials, self.meas_fiducials,
self.germs, maxLens, modes=['CPTPLND','Test','Target'],
models_to_test = {"Test": mdl_guess},
comm=None, mem_limit=None, verbosity=0,
disable_checkpointing=True, advanced_options= {'max_iterations':3})
pygsti.report.construct_standard_report(result, title= "Std Practice Test Report", verbosity=2).write_html(temp_files + "/full_report_stdpractice")
def test_bootstrap(self):
"""Test bootstrap model generation"""
ds = self.ds
tp_target = self.model.copy()
tp_target.set_all_parameterizations("full TP")
mdl = pygsti.run_lgst(ds, self.prep_fiducials, self.meas_fiducials, target_model=tp_target, svd_truncate_to=4, verbosity=0)
default_maxLens = [2**k for k in range(4)]
circuits = pygsti.circuits.create_lsgst_circuits(
self.op_labels, self.prep_fiducials, self.meas_fiducials, self.germs,
default_maxLens, fid_pairs=None, trunc_scheme="whole germ powers")
ds_defaultMaxLens = pygsti.data.simulate_data(
mdl, circuits, num_samples=10000, sample_error='round')
bootgs_p_defaultMaxLens = \
pygsti.drivers.create_bootstrap_models(
2, ds_defaultMaxLens, 'parametric', self.prep_fiducials, self.meas_fiducials,
self.germs, default_maxLens, input_model=mdl, target_model=tp_target,
return_data=False) #test when max_lengths == None ?
def test_GST_checkpointing(self):
ds= self.ds
maxLens = self.maxLens
target_model = self.model.copy()
#Make list-of-lists of GST operation sequences
fullStructs = pygsti.circuits.create_lsgst_circuit_lists(
target_model, self.prep_fiducials, self.meas_fiducials, self.germs, maxLens)
#Test GateSetTomographyCheckpoint:
#First run from scratch:
result_gst = pygsti.run_long_sequence_gst_base(ds, target_model.copy(), fullStructs, verbosity=0,
checkpoint_path= temp_files + '/checkpoint_testing/GateSetTomography',
advanced_options= {'max_iterations':3})
#double check that we can read in this checkpoint object correctly:
gst_checkpoint = pygsti.protocols.GateSetTomographyCheckpoint.read(temp_files + '/checkpoint_testing/GateSetTomography_iteration_0.json')
#run GST using this checkpoint
result_gst_warmstart = pygsti.run_long_sequence_gst_base(ds, target_model.copy(), fullStructs, verbosity=0,
checkpoint = gst_checkpoint,
checkpoint_path= temp_files + '/checkpoint_testing/GateSetTomography',
advanced_options= {'max_iterations':3})
diff = norm(result_gst.estimates['GateSetTomography'].models['final iteration estimate'].to_vector()-
result_gst_warmstart.estimates['GateSetTomography'].models['final iteration estimate'].to_vector())
#Assert that this gives the same result as before:
self.assertTrue(diff<=1e-10)
def test_ModelTest_checkpointing(self):
ds = self.ds
maxLens = self.maxLens
target_model = self.model.copy()
#Next test ModelTestCheckpoint
#First run from scratch:
result_modeltest = pygsti.run_model_test(target_model.copy(), ds, target_model.create_processor_spec(),
self.prep_fiducials, self.meas_fiducials, self.germs,
maxLens, verbosity=0,
checkpoint_path= temp_files + '/checkpoint_testing/ModelTest')
#double check that we can read in this checkpoint object correctly:
model_test_checkpoint = pygsti.protocols.ModelTestCheckpoint.read(temp_files + '/checkpoint_testing/ModelTest_iteration_0.json')
#run GST using this checkpoint
result_modeltest_warmstart = pygsti.run_model_test(target_model.copy(), ds,target_model.create_processor_spec(),
self.prep_fiducials, self.meas_fiducials, self.germs,
maxLens, verbosity=0,
checkpoint = model_test_checkpoint,
checkpoint_path= temp_files + '/checkpoint_testing/ModelTest')
diff = norm(np.array(result_modeltest.estimates['ModelTest'].parameters['model_test_values'])-
np.array(result_modeltest_warmstart.estimates['ModelTest'].parameters['model_test_values']))
#Assert that this gives the same result as before:
self.assertTrue(diff<=1e-10)
def test_StandardGST_checkpointing(self):
ds= self.ds
maxLens = self.maxLens
#Finally test StandardGSTCheckpoint
#First run from scratch:
mdl_guess = self.model.copy()
mdl_guess = mdl_guess.depolarize(op_noise=0.01,spam_noise=0.01)
result_standardgst = pygsti.run_stdpractice_gst(ds, self.model.copy(), self.prep_fiducials, self.meas_fiducials,
self.germs, maxLens, modes=['full TP','CPTPLND','Test','Target'],
models_to_test = {"Test": mdl_guess},
comm=None, mem_limit=None, verbosity=0,
checkpoint_path= temp_files + '/checkpoint_testing/StandardGST',
advanced_options= {'max_iterations':3})
#double check that we can read in this checkpoint object correctly:
standardgst_checkpoint = pygsti.protocols.StandardGSTCheckpoint.read(temp_files + '/checkpoint_testing/StandardGST_CPTPLND_iteration_1.json')
#run GST using this checkpoint
result_standardgst_warmstart = pygsti.run_stdpractice_gst(ds, self.model.copy(), self.prep_fiducials, self.meas_fiducials,
self.germs, maxLens, modes=['full TP','CPTPLND','Test','Target'],
models_to_test = {"Test": mdl_guess},
comm=None, mem_limit=None, verbosity=0,
checkpoint = standardgst_checkpoint,
checkpoint_path= temp_files + '/checkpoint_testing/StandardGST',
advanced_options= {'max_iterations':3})
#Assert that this gives the same result as before:
#diff = norm(result_standardgst.estimates['CPTPLND'].models['final iteration estimate'].to_vector()-
# result_standardgst_warmstart.estimates['CPTPLND'].models['final iteration estimate'].to_vector())
diff = pygsti.tools.logl(result_standardgst.estimates['CPTPLND'].models['final iteration estimate'], ds)- \
pygsti.tools.logl(result_standardgst_warmstart.estimates['CPTPLND'].models['final iteration estimate'], ds)
diff1 = norm(result_standardgst.estimates['full TP'].models['final iteration estimate'].to_vector()-
result_standardgst_warmstart.estimates['full TP'].models['final iteration estimate'].to_vector())
self.assertTrue(abs(diff)<=1e-6)
self.assertTrue(diff1<=1e-10)
if __name__ == "__main__":
unittest.main(verbosity=2)