def test_build_up_breadth(self):
germs = germsel.build_up_breadth(self.neighbors, self.germ_set, **self.options)
# TODO assert correctness
algorithm_kwargs = dict(
germsList=self.germ_set,
**self.options
)
germs_driver = germsel.generate_germs(
self.mdl_target_noisy, randomize=False, algorithm='greedy',
algorithm_kwargs=algorithm_kwargs
)
def test_grasp_germ_set_optimization(self):
soln = germsel.grasp_germ_set_optimization(
self.neighbors, self.germ_set, alpha=0.1, **self.options
)
# TODO assert correctness
best, initial, local = germsel.grasp_germ_set_optimization(
self.neighbors, self.germ_set, alpha=0.1, returnAll=True,
**self.options
)
# TODO shouldn't this pass?
# self.assertEqual(soln, best)
algorithm_kwargs = dict(
germsList=self.germ_set,
**self.options
)
soln_driver = germsel.generate_germs(
self.mdl_target_noisy, randomize=False, algorithm='grasp',
algorithm_kwargs=algorithm_kwargs
)
def test_optimize_integer_germs_slack_with_fixed_slack(self):
finalGerms = germsel.optimize_integer_germs_slack(
self.mdl_target_noisy, self.germ_set, fixedSlack=0.1,
verbosity=4
)
# TODO assert correctness
finalGerms_all, weights, scores = germsel.optimize_integer_germs_slack(
self.mdl_target_noisy, self.germ_set, fixedSlack=0.1,
returnAll=True, verbosity=4
)
self.assertEqual(finalGerms, finalGerms_all)
algorithm_kwargs = dict(
germsList=self.germ_set,
fixedSlack=0.1
)
finalGerms_driver = germsel.generate_germs(
self.mdl_target_noisy, randomize=False, algorithm='slack',
algorithm_kwargs=algorithm_kwargs, verbosity=4
)
self.assertEqual(finalGerms_driver, finalGerms)
def test_auto_experiment_desgin(self):
# Let's construct a 1-qubit $X(\pi/2)$, $Y(\pi/2)$, $I$ gateset for which we will need to find germs and fiducials.
gs_target = constr.build_gateset([2], [('Q0',)], ['Gi', 'Gx', 'Gy'],
["I(Q0)", "X(pi/2,Q0)", "Y(pi/2,Q0)"],
prepLabels=['rho0'], prepExpressions=["0"],
effectLabels=['E0'], effectExpressions=["1"],
spamdefs={'plus': ('rho0', 'E0'),
'minus': ('rho0', 'remainder')})
# ## Hands-off
# We begin by demonstrating the most hands-off approach.
# We can generate a germ set simply by providing the target gateset.
germs = germsel.generate_germs(gs_target)
# In the same way we can generate preparation and measurement fiducials.
prepFiducials, measFiducials = fidsel.generate_fiducials(gs_target)
# Now that we have germs and fiducials, we can construct the list of experiments we need to perform in
# order to do GST. The only new things to provide at this point are the sizes for the experiments we want
# to perform (in this case we want to perform between 0 and 256 gates between fiducial pairs, going up
# by a factor of 2 at each stage).
maxLengths = [0] + [2**n for n in range(8 + 1)]
listOfExperiments = constr.make_lsgst_experiment_list(gs_target.gates.keys(), prepFiducials,
measFiducials, germs, maxLengths)
# The list of `GateString` that the previous function gave us isn't necessarily the most readable
# form to present the information in, so we can write the experiment list out to an empty data
# file to be filled in after the experiments are performed.
graspGerms = germsel.generate_germs(gs_target, algorithm='grasp', algorithm_kwargs={'iterations': 1})
slackPrepFids, slackMeasFids = fidsel.generate_fiducials(gs_target, algorithm='slack',
algorithm_kwargs={'slackFrac': 0.25})
max([len(germ) for germ in germs])
germsMaxLength5 = germsel.generate_germs(gs_target, maxGermLength=5)
max([len(germ) for germ in germsMaxLength5])
germsMaxLength3 = germsel.generate_germs(gs_target, maxGermLength=3)
uniformPrepFids, uniformMeasFids = fidsel.generate_fiducials(gs_target, maxFidLength=3,
algorithm='grasp',
algorithm_kwargs={'iterations': 100})
incompletePrepFids, incompleteMeasFids = fidsel.generate_fiducials(gs_target, maxFidLength=1)
nonSingletonGerms = germsel.generate_germs(gs_target, forceSingletons=False, maxGermLength=4,
algorithm='grasp', algorithm_kwargs={'iterations': 5})
omitIdentityPrepFids, omitIdentityMeasFids = fidsel.generate_fiducials(gs_target, omitIdentity=False,
gatesToOmit=['Gi'])
def test_generate_germs_raises_on_bad_algorithm(self):
with self.assertRaises(ValueError):
germsel.generate_germs(self.mdl_target_noisy, algorithm='foobar')
def test_generate_germs_with_candidate_germ_counts(self):
germs = germsel.generate_germs(
self.mdl_target_noisy, randomize=False,
candidateGermCounts={3: 'all upto', 4: 10, 5: 10, 6: 10}
)
def test_auto_experiment_design(self):
# Let's construct a 1-qubit $X(\pi/2)$, $Y(\pi/2)$, $I$ gateset for which we will need to find germs and fiducials.
gs_target = constr.build_gateset([2], [('Q0',)], ['Gi', 'Gx', 'Gy'],
["I(Q0)", "X(pi/2,Q0)", "Y(pi/2,Q0)"])
# ## Hands-off
# We begin by demonstrating the most hands-off approach.
# We can generate a germ set simply by providing the target gateset. (and seed so it's deterministic)
germs = germsel.generate_germs(gs_target, seed=2017)
# In the same way we can generate preparation and measurement fiducials.
prepFiducials, measFiducials = fidsel.generate_fiducials(gs_target)
#test returnAll - this just prints more info...
p,m = fidsel.generate_fiducials(gs_target, algorithm_kwargs={'returnAll': True})
#test invalid algorithm
with self.assertRaises(ValueError):
fidsel.generate_fiducials(gs_target, algorithm='foobar')
# Now that we have germs and fiducials, we can construct the list of experiments we need to perform in
# order to do GST. The only new things to provide at this point are the sizes for the experiments we want
# to perform (in this case we want to perform between 0 and 256 gates between fiducial pairs, going up
# by a factor of 2 at each stage).
maxLengths = [0] + [2**n for n in range(8 + 1)]
listOfExperiments = constr.make_lsgst_experiment_list(gs_target.gates.keys(), prepFiducials,
measFiducials, germs, maxLengths)
# The list of `GateString` that the previous function gave us isn't necessarily the most readable
# form to present the information in, so we can write the experiment list out to an empty data
# file to be filled in after the experiments are performed.
graspGerms = germsel.generate_germs(gs_target, algorithm='grasp',
seed=2017, numGSCopies=2,
candidateGermCounts={3: 'all upto', 4:10, 5:10, 6:10},
candidateSeed=2017,
algorithm_kwargs={'iterations': 1})
slackPrepFids, slackMeasFids = fidsel.generate_fiducials(gs_target, algorithm='slack',
algorithm_kwargs={'slackFrac': 0.25})
fidsel.generate_fiducials(gs_target, algorithm='slack') # slacFrac == 1.0 if don't specify either slackFrac or fixedSlack
germsMaxLength3 = germsel.generate_germs(gs_target, candidateGermCounts={3: 'all upto'}, seed=2017)
uniformPrepFids, uniformMeasFids = fidsel.generate_fiducials(gs_target, maxFidLength=3,
algorithm='grasp',
algorithm_kwargs={'iterations': 100})
incompletePrepFids, incompleteMeasFids = fidsel.generate_fiducials(gs_target, maxFidLength=1)
nonSingletonGerms = germsel.generate_germs(gs_target, numGSCopies=2, force=None, candidateGermCounts={4: 'all upto'},
algorithm='grasp', algorithm_kwargs={'iterations': 5},
seed=2017)
omitIdentityPrepFids, omitIdentityMeasFids = fidsel.generate_fiducials(gs_target, omitIdentity=False,
gatesToOmit=['Gi'])
def test_auto_experiment_desgin(self):
# Let's construct a 1-qubit $X(\pi/2)$, $Y(\pi/2)$, $I$ gateset for which we will need to find germs and fiducials.
gs_target = constr.build_gateset([2], [('Q0', )], ['Gi', 'Gx', 'Gy'],
["I(Q0)", "X(pi/2,Q0)", "Y(pi/2,Q0)"],
prepLabels=['rho0'],
prepExpressions=["0"],
effectLabels=['E0'],
effectExpressions=["1"],
spamdefs={
'plus': ('rho0', 'E0'),
'minus': ('rho0', 'remainder')
})
# ## Hands-off
# We begin by demonstrating the most hands-off approach.
# We can generate a germ set simply by providing the target gateset.
germs = germsel.generate_germs(gs_target)
# In the same way we can generate preparation and measurement fiducials.
prepFiducials, measFiducials = fidsel.generate_fiducials(gs_target)
# Now that we have germs and fiducials, we can construct the list of experiments we need to perform in
# order to do GST. The only new things to provide at this point are the sizes for the experiments we want
# to perform (in this case we want to perform between 0 and 256 gates between fiducial pairs, going up
# by a factor of 2 at each stage).
maxLengths = [0] + [2**n for n in range(8 + 1)]
listOfExperiments = constr.make_lsgst_experiment_list(
gs_target.gates.keys(), prepFiducials, measFiducials, germs,
maxLengths)
# The list of `GateString` that the previous function gave us isn't necessarily the most readable
# form to present the information in, so we can write the experiment list out to an empty data
# file to be filled in after the experiments are performed.
graspGerms = germsel.generate_germs(gs_target,
algorithm='grasp',
algorithm_kwargs={'iterations': 1})
slackPrepFids, slackMeasFids = fidsel.generate_fiducials(
gs_target, algorithm='slack', algorithm_kwargs={'slackFrac': 0.25})
max([len(germ) for germ in germs])
germsMaxLength5 = germsel.generate_germs(gs_target, maxGermLength=5)
max([len(germ) for germ in germsMaxLength5])
germsMaxLength3 = germsel.generate_germs(gs_target, maxGermLength=3)
uniformPrepFids, uniformMeasFids = fidsel.generate_fiducials(
gs_target,
maxFidLength=3,
algorithm='grasp',
algorithm_kwargs={'iterations': 100})
incompletePrepFids, incompleteMeasFids = fidsel.generate_fiducials(
gs_target, maxFidLength=1)
nonSingletonGerms = germsel.generate_germs(
gs_target,
force=None,
maxGermLength=4,
algorithm='grasp',
algorithm_kwargs={'iterations': 5})
omitIdentityPrepFids, omitIdentityMeasFids = fidsel.generate_fiducials(
gs_target, omitIdentity=False, gatesToOmit=['Gi'])
