def test_reducedmod_cterm(self):
# Using term-based calcs using map-based stabilizer-state propagation
target_model = pc.build_nqnoise_model(
self.nQubits,
geometry="line",
maxIdleWeight=1,
maxhops=1,
extraWeight1Hops=0,
extraGateWeight=1,
sparse=False,
verbosity=1,
sim_type="termorder:1",
parameterization="H+S clifford terms")
target_model.from_vector(self.rand_start228)
results = pygsti.do_long_sequence_gst(
self.redmod_ds,
target_model,
self.redmod_fiducials,
self.redmod_fiducials,
self.redmod_germs,
self.redmod_maxLs,
verbosity=4,
advancedOptions={'tolerance': 1e-3})
print("MISFIT nSigma = ", results.estimates['default'].misfit_sigma())
self.assertAlmostEqual(results.estimates['default'].misfit_sigma(),
3.0,
delta=1.0)
mdl_compare = pygsti.io.json.load(
open(compare_files + "/test2Qcalc_redmod_terms.model"))
self.assertAlmostEqual(np.linalg.norm(
results.estimates['default'].models['go0'].to_vector() -
mdl_compare.to_vector()),
0,
places=3)
def test_reducedmod_map2(self):
# Using sparse embedded matrices and map-based calcs
target_model = pc.build_nqnoise_model(self.nQubits,
geometry="line",
maxIdleWeight=1,
maxhops=1,
extraWeight1Hops=0,
extraGateWeight=1,
sparse=True,
sim_type="map",
verbosity=1)
target_model.from_vector(self.rand_start206)
results = pygsti.do_long_sequence_gst(
self.redmod_ds,
target_model,
self.redmod_fiducials,
self.redmod_fiducials,
self.redmod_germs,
self.redmod_maxLs,
verbosity=4,
advancedOptions={'tolerance': 1e-3})
print("MISFIT nSigma = ", results.estimates['default'].misfit_sigma())
self.assertAlmostEqual(results.estimates['default'].misfit_sigma(),
1.0,
delta=1.0)
mdl_compare = pygsti.io.json.load(
open(compare_files + "/test2Qcalc_redmod_exact.model"))
self.assertAlmostEqual(np.linalg.norm(
results.estimates['default'].models['go0'].to_vector() -
mdl_compare.to_vector()),
0,
places=1)
def test_reducedmod_map1(self):
# Using dense embedded matrices and map-based calcs (maybe not really necessary to include?)
target_model = pc.build_nqnoise_model(self.nQubits,
geometry="line",
max_idle_weight=1,
maxhops=1,
extra_weight_1_hops=0,
extra_gate_weight=1,
sparse=False,
sim_type="map",
verbosity=1)
target_model.from_vector(self.rand_start206)
results = pygsti.run_long_sequence_gst(
self.redmod_ds,
target_model,
self.redmod_fiducials,
self.redmod_fiducials,
self.redmod_germs,
self.redmod_maxLs,
verbosity=4,
advanced_options={'tolerance': 1e-3})
print("MISFIT nSigma = ", results.estimates['default'].misfit_sigma())
self.assertAlmostEqual(results.estimates['default'].misfit_sigma(),
1.0,
delta=1.0)
mdl_compare = pygsti.serialization.json.load(
open(compare_files + "/test2Qcalc_redmod_exact.model"))
self.assertAlmostEqual(
results.estimates['default'].models['go0'].frobeniusdist(
mdl_compare),
0,
places=1)
def test_reducedmod_svterm(self):
# Using term-based calcs using map-based state-vector propagation
target_model = pc.build_nqnoise_model(self.nQubits,
geometry="line",
max_idle_weight=1,
maxhops=1,
extra_weight_1_hops=0,
extra_gate_weight=1,
sparse=False,
verbosity=1,
sim_type="termorder:1",
parameterization="H+S terms")
target_model.from_vector(self.rand_start228)
results = pygsti.run_long_sequence_gst(
self.redmod_ds,
target_model,
self.redmod_fiducials,
self.redmod_fiducials,
self.redmod_germs,
self.redmod_maxLs,
verbosity=4,
advanced_options={'tolerance': 1e-3})
#RUN BELOW LINES TO SAVE GATESET (UNCOMMENT to regenerate)
#pygsti.io.json.dump(results.estimates['default'].models['go0'],
# open(compare_files + "/test2Qcalc_redmod_terms.model",'w'))
print("MISFIT nSigma = ", results.estimates['default'].misfit_sigma())
self.assertAlmostEqual(results.estimates['default'].misfit_sigma(),
3.0,
delta=1.0)
mdl_compare = pygsti.serialization.json.load(
open(compare_files + "/test2Qcalc_redmod_terms.model"))
self.assertAlmostEqual(np.linalg.norm(
results.estimates['default'].models['go0'].to_vector() -
mdl_compare.to_vector()),
0,
places=3)
def test_reducedmod_matrix(self):
# Using dense matrices and matrix-based calcs
target_model = pc.build_nqnoise_model(self.nQubits,
geometry="line",
maxIdleWeight=1,
maxhops=1,
extraWeight1Hops=0,
extraGateWeight=1,
sparse=False,
sim_type="matrix",
verbosity=1)
target_model.from_vector(self.rand_start206)
results = pygsti.do_long_sequence_gst(
self.redmod_ds,
target_model,
self.redmod_fiducials,
self.redmod_fiducials,
self.redmod_germs,
self.redmod_maxLs,
verbosity=4,
advancedOptions={'tolerance': 1e-3})
#RUN BELOW LINES TO SAVE GATESET (UNCOMMENT to regenerate)
#pygsti.io.json.dump(results.estimates['default'].models['go0'],
# open(compare_files + "/test2Qcalc_redmod_exact.model",'w'))
print("MISFIT nSigma = ", results.estimates['default'].misfit_sigma())
self.assertAlmostEqual(results.estimates['default'].misfit_sigma(),
1.0,
delta=1.0)
mdl_compare = pygsti.io.json.load(
open(compare_files + "/test2Qcalc_redmod_exact.model"))
self.assertAlmostEqual(
results.estimates['default'].models['go0'].frobeniusdist(
mdl_compare),
0,
places=3)
def setUpClass(cls):
"""
Handle all once-per-class (slow) computation and loading,
to avoid calling it for each test (like setUp). Store
results in class variable for use within setUp.
"""
super(CalcMethods2QTestCase, cls).setUpClass()
#Change to test_packages directory (since setUp hasn't been called yet...)
origDir = os.getcwd()
os.chdir(os.path.abspath(os.path.dirname(__file__)))
os.chdir('..') # The test_packages directory
#Note: std is a 2Q model
cls.maxLengths = [1]
#cls.germs = std.germs_lite
cls.germs = pygsti.construction.circuit_list([
(gl, ) for gl in std.target_model().operations
])
cls.mdl_datagen = std.target_model().depolarize(op_noise=0.1,
spam_noise=0.001)
cls.listOfExperiments = pygsti.construction.make_lsgst_experiment_list(
std.target_model(), std.prepStrs, std.effectStrs, cls.germs,
cls.maxLengths)
#RUN BELOW FOR DATAGEN (UNCOMMENT to regenerate)
#ds = pygsti.construction.generate_fake_data(cls.mdl_datagen, cls.listOfExperiments,
# nSamples=1000, sampleError="multinomial", seed=1234)
#ds.save(compare_files + "/calcMethods2Q.dataset")
cls.ds = pygsti.objects.DataSet(fileToLoadFrom=compare_files +
"/calcMethods2Q.dataset")
cls.advOpts = {'tolerance': 1e-2}
#Reduced model GST dataset
cls.nQubits = 2
cls.mdl_redmod_datagen = pc.build_nqnoise_model(cls.nQubits,
geometry="line",
maxIdleWeight=1,
maxhops=1,
extraWeight1Hops=0,
extraGateWeight=1,
sparse=False,
sim_type="matrix",
verbosity=1,
gateNoise=(1234, 0.01),
prepNoise=(456, 0.01),
povmNoise=(789, 0.01))
#Create a reduced set of fiducials and germs
opLabels = list(cls.mdl_redmod_datagen.operations.keys())
fids1Q = std1Q_XY.fiducials[0:2] # for speed
cls.redmod_fiducials = []
for i in range(cls.nQubits):
cls.redmod_fiducials.extend(
pygsti.construction.manipulate_circuit_list(
fids1Q, [((L('Gx'), ), (L('Gx', i), )),
((L('Gy'), ), (L('Gy', i), ))]))
#print(redmod_fiducials, "Fiducials")
cls.redmod_germs = pygsti.construction.circuit_list([
(gl, ) for gl in opLabels
])
cls.redmod_maxLs = [1]
expList = pygsti.construction.make_lsgst_experiment_list(
cls.mdl_redmod_datagen, cls.redmod_fiducials, cls.redmod_fiducials,
cls.redmod_germs, cls.redmod_maxLs)
#RUN BELOW FOR DATAGEN (UNCOMMENT to regenerate)
#redmod_ds = pygsti.construction.generate_fake_data(cls.mdl_redmod_datagen, expList, 1000, "round", seed=1234)
#redmod_ds.save(compare_files + "/calcMethods2Q_redmod.dataset")
cls.redmod_ds = pygsti.objects.DataSet(fileToLoadFrom=compare_files +
"/calcMethods2Q_redmod.dataset")
#print(len(expList)," reduced model sequences")
#Random starting points - little kick so we don't get hung up at start
np.random.seed(1234)
cls.rand_start18 = np.random.random(18) * 1e-6
cls.rand_start206 = np.random.random(206) * 1e-6
cls.rand_start228 = np.random.random(228) * 1e-6
os.chdir(origDir) # return to original directory
