def test_reducedmod_map2(self):
# Using sparse embedded matrices and map-based calcs
gs_target = pc.build_nqnoise_gateset(self.nQubits,
geometry="line",
maxIdleWeight=1,
maxhops=1,
extraWeight1Hops=0,
extraGateWeight=1,
sparse=True,
sim_type="map",
verbosity=1)
gs_target.from_vector(self.rand_start206)
results = pygsti.do_long_sequence_gst(
self.redmod_ds,
gs_target,
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)
gs_compare = pygsti.io.json.load(
open(compare_files + "/test2Qcalc_redmod_exact.gateset"))
self.assertAlmostEqual(np.linalg.norm(
results.estimates['default'].gatesets['go0'].to_vector() -
gs_compare.to_vector()),
0,
places=1)
def test_reducedmod_cterm(self):
# Using term-based calcs using map-based stabilizer-state propagation
gs_target = pc.build_nqnoise_gateset(
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")
gs_target.from_vector(self.rand_start228)
results = pygsti.do_long_sequence_gst(
self.redmod_ds,
gs_target,
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)
gs_compare = pygsti.io.json.load(
open(compare_files + "/test2Qcalc_redmod_terms.gateset"))
self.assertAlmostEqual(np.linalg.norm(
results.estimates['default'].gatesets['go0'].to_vector() -
gs_compare.to_vector()),
0,
places=3)
def test_reducedmod_map1(self):
# Using dense embedded matrices and map-based calcs (maybe not really necessary to include?)
gs_target = pc.build_nqnoise_gateset(self.nQubits,
geometry="line",
maxIdleWeight=1,
maxhops=1,
extraWeight1Hops=0,
extraGateWeight=1,
sparse=False,
sim_type="map",
verbosity=1)
print("Num params = ", gs_target.num_params())
gs_target.from_vector(self.rand_start25)
results = pygsti.do_long_sequence_gst(
self.redmod_ds,
gs_target,
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(),
0.0,
delta=1.0)
gs_compare = pygsti.io.json.load(
open(compare_files + "/test1Qcalc_redmod_exact.gateset"))
self.assertAlmostEqual(
results.estimates['default'].gatesets['go0'].frobeniusdist(
gs_compare),
0,
places=1)
def test_greedy_sequenceselection(self):
nQubits = 1
maxLengths = [1, 2]
cnot_edges = []
gs_datagen = pc.build_nqnoise_gateset(nQubits,
"line",
cnot_edges,
maxIdleWeight=1,
maxhops=0,
extraWeight1Hops=0,
extraGateWeight=0,
sparse=True,
verbosity=1,
sim_type="map",
parameterization="H+S",
gateNoise=(1234, 0.01),
prepNoise=(456, 0.01),
povmNoise=(789, 0.01))
cache = {}
gss = pygsti.construction.create_nqubit_sequences(nQubits,
maxLengths,
'line',
cnot_edges,
maxIdleWeight=1,
maxhops=0,
extraWeight1Hops=0,
extraGateWeight=0,
verbosity=4,
cache=cache,
algorithm="greedy")
#expList = gss.allstrs #[ tup[0] for tup in expList_tups]
#RUN to save list
#pygsti.io.json.dump(gss, open(compare_files + "/nqubit_1Q_seqs.json",'w'))
compare_gss = pygsti.io.json.load(
open(compare_files + "/nqubit_1Q_seqs.json"))
#expList_tups_mod = [tuple( etup[0:3] + ('XX','XX')) for etup in expList_tups ]
#for etup in expList_tups:
# etup_mod = tuple( etup[0:3] + ('XX','XX'))
# if etup_mod not in compare_tups:
# print("Not found: ", etup)
#
# #if (etup[0] != ctup[0]) or (etup[1] != ctup[1]) or (etup[2] != ctup[2]):
# # print("Mismatch:",(etup[0] != ctup[0]), (etup[1] != ctup[1]), (etup[2] != ctup[2]))
# # print(etup); print(ctup)
# # print(tuple(etup[0]))
# # print(tuple(ctup[0]))
self.assertEqual(set(gss.allstrs), set(compare_gss.allstrs))
def test_2Q_terms(self):
gss = pygsti.io.json.load(open(compare_files + "/nqubit_2Q_seqs.json"))
expList = gss.allstrs
ds = pygsti.io.json.load(
open(compare_files + "/nqubit_2Q_dataset.json"))
print(len(expList), " sequences")
nQubits = 2
maxLengths = [1, 2]
cnot_edges = [(i, i + 1)
for i in range(nQubits - 1)] #only single direction
#OLD
#lsgstLists = []; lst = []
#for L in maxLengths:
# for tup in expList_tups:
# if tup[1] == L: lst.append( tup[0] )
# lsgstLists.append(lst[:]) # append *running* list
lsgstLists = gss # can just use gss as input to pygsti.do_long_sequence_gst_base
gs_to_optimize = pc.build_nqnoise_gateset(nQubits,
"line",
cnot_edges,
maxIdleWeight=2,
maxhops=1,
extraWeight1Hops=0,
extraGateWeight=1,
verbosity=1,
sim_type="termorder:1",
parameterization="H+S terms",
sparse=False)
#RUN to create cache
#calc_cache = {}
#gs_to_optimize.set_simtype("termorder:1",calc_cache)
#gs_to_optimize.bulk_probs(lsgstLists[-1])
#pygsti.io.json.dump(calc_cache, open(compare_files + '/nqubit_2Qterms.cache','w'))
#Just load precomputed cache (we test do_long_sequence_gst_base here, not cache computation)
calc_cache = pygsti.io.json.load(
open(compare_files + '/nqubit_2Qterms.cache'))
gs_to_optimize.set_simtype("termorder:1", calc_cache)
results = pygsti.do_long_sequence_gst_base(
ds,
gs_to_optimize,
lsgstLists,
gaugeOptParams=False,
advancedOptions={'tolerance': 1e-3},
verbosity=4)
def test_sequential_sequenceselection(self):
nQubits = 2
maxLengths = [1, 2]
cnot_edges = [(i, i + 1)
for i in range(nQubits - 1)] #only single direction
gs_datagen = pc.build_nqnoise_gateset(nQubits,
"line",
cnot_edges,
maxIdleWeight=2,
maxhops=1,
extraWeight1Hops=0,
extraGateWeight=0,
sparse=True,
verbosity=1,
sim_type="map",
parameterization="H+S",
gateNoise=(1234, 0.01),
prepNoise=(456, 0.01),
povmNoise=(789, 0.01))
cache = {}
gss = pygsti.construction.create_nqubit_sequences(
nQubits,
maxLengths,
'line',
cnot_edges,
maxIdleWeight=2,
maxhops=1,
extraWeight1Hops=0,
extraGateWeight=0,
verbosity=4,
cache=cache,
algorithm="sequential")
expList = gss.allstrs #[ tup[0] for tup in expList_tups]
#RUN to save list & dataset
#pygsti.io.json.dump(gss, open(compare_files + "/nqubit_2Q_seqs.json",'w'))
#ds = pygsti.construction.generate_fake_data(gs_datagen, expList, 1000, "multinomial", seed=1234)
#pygsti.io.json.dump(ds,open(compare_files + "/nqubit_2Q_dataset.json",'w'))
compare_gss = pygsti.io.json.load(
open(compare_files + "/nqubit_2Q_seqs.json"))
self.assertEqual(set(gss.allstrs), set(compare_gss.allstrs))
def test_autogator(self):
#Test this here b/c auto-gators are associated with parallel gate labels
gs = pc.build_nqnoise_gateset(2,
"line", [(0, 1)],
maxIdleWeight=2,
maxhops=1,
extraWeight1Hops=0,
extraGateWeight=1,
verbosity=1,
sim_type="map",
parameterization="H+S",
sparse=True)
# gs[('Gx',0)].factorgates # Composed([fullTargetOp,fullIdleErr,fullLocalErr])
self.assertEqual(
set(gs.gates.keys()),
set([
L('Gi'),
L('Gx', 0),
L('Gy', 0),
L('Gx', 1),
L('Gy', 1),
L('Gcnot', (0, 1))
]))
#But we can *compute* with gatestrings containing parallel labels...
parallelLbl = L([('Gx', 0), ('Gy', 1)])
with self.assertRaises(KeyError):
gs.gates[parallelLbl]
gstr = pygsti.obj.GateString((parallelLbl, ))
probs = gs.probs(gstr)
print(probs)
expected = {
('00', ): 0.25,
('01', ): 0.25,
('10', ): 0.25,
('11', ): 0.25
}
for k, v in probs.items():
self.assertAlmostEqual(v, expected[k])
def test_2Q(self):
gss = pygsti.io.json.load(open(compare_files + "/nqubit_2Q_seqs.json"))
expList = gss.allstrs
ds = pygsti.io.json.load(
open(compare_files + "/nqubit_2Q_dataset.json"))
print(len(expList), " sequences")
nQubits = 2
maxLengths = [1, 2]
cnot_edges = [(i, i + 1)
for i in range(nQubits - 1)] #only single direction
#OLD
#lsgstLists = []; lst = []
#for L in maxLengths:
# for tup in expList_tups:
# if tup[1] == L: lst.append( tup[0] )
# lsgstLists.append(lst[:]) # append *running* list
lsgstLists = gss # can just use gss as input to pygsti.do_long_sequence_gst_base
gs_to_optimize = pc.build_nqnoise_gateset(nQubits,
"line",
cnot_edges,
maxIdleWeight=2,
maxhops=1,
extraWeight1Hops=0,
extraGateWeight=1,
verbosity=1,
sim_type="map",
parameterization="H+S",
sparse=True)
results = pygsti.do_long_sequence_gst_base(
ds,
gs_to_optimize,
lsgstLists,
gaugeOptParams=False,
advancedOptions={'tolerance': 1e-2},
verbosity=4)
def test_reducedmod_svterm(self):
# Using term-based calcs using map-based state-vector propagation
gs_target = pc.build_nqnoise_gateset(self.nQubits,
geometry="line",
maxIdleWeight=1,
maxhops=1,
extraWeight1Hops=0,
extraGateWeight=1,
sparse=False,
verbosity=1,
sim_type="termorder:1",
parameterization="H+S terms")
print("Num params = ", gs_target.num_params())
gs_target.from_vector(self.rand_start36)
results = pygsti.do_long_sequence_gst(
self.redmod_ds,
gs_target,
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'].gatesets['go0'],
# open(compare_files + "/test1Qcalc_redmod_terms.gateset",'w'))
print("MISFIT nSigma = ", results.estimates['default'].misfit_sigma())
self.assertAlmostEqual(results.estimates['default'].misfit_sigma(),
0.0,
delta=1.0)
gs_compare = pygsti.io.json.load(
open(compare_files + "/test1Qcalc_redmod_terms.gateset"))
self.assertAlmostEqual(np.linalg.norm(
results.estimates['default'].gatesets['go0'].to_vector() -
gs_compare.to_vector()),
0,
places=3)
def test_reducedmod_matrix(self):
# Using dense matrices and matrix-based calcs
gs_target = pc.build_nqnoise_gateset(self.nQubits,
geometry="line",
maxIdleWeight=1,
maxhops=1,
extraWeight1Hops=0,
extraGateWeight=1,
sparse=False,
sim_type="matrix",
verbosity=1)
print("Num params = ", gs_target.num_params())
gs_target.from_vector(self.rand_start25)
results = pygsti.do_long_sequence_gst(
self.redmod_ds,
gs_target,
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'].gatesets['go0'],
# open(compare_files + "/test1Qcalc_redmod_exact.gateset",'w'))
print("MISFIT nSigma = ", results.estimates['default'].misfit_sigma())
self.assertAlmostEqual(results.estimates['default'].misfit_sigma(),
0.0,
delta=1.0)
gs_compare = pygsti.io.json.load(
open(compare_files + "/test1Qcalc_redmod_exact.gateset"))
self.assertAlmostEqual(
results.estimates['default'].gatesets['go0'].frobeniusdist(
gs_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 gateset
cls.maxLengths = [1]
#cls.germs = std.germs_lite
cls.germs = pygsti.construction.gatestring_list([
(gl, ) for gl in std.gs_target.gates
])
cls.gs_datagen = std.gs_target.depolarize(gate_noise=0.1,
spam_noise=0.001)
cls.listOfExperiments = pygsti.construction.make_lsgst_experiment_list(
std.gs_target, std.prepStrs, std.effectStrs, cls.germs,
cls.maxLengths)
#RUN BELOW FOR DATAGEN (UNCOMMENT to regenerate)
#ds = pygsti.construction.generate_fake_data(cls.gs_datagen, cls.listOfExperiments,
# nSamples=1000, sampleError="multinomial", seed=1234)
#ds.save(compare_files + "/calcMethods2Q.dataset%s" % cls.versionsuffix)
cls.ds = pygsti.objects.DataSet(
fileToLoadFrom=compare_files +
"/calcMethods2Q.dataset%s" % cls.versionsuffix)
cls.advOpts = {'tolerance': 1e-2}
#Reduced model GST dataset
cls.nQubits = 2
cls.gs_redmod_datagen = pc.build_nqnoise_gateset(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
gateLabels = list(cls.gs_redmod_datagen.gates.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_gatestring_list(
fids1Q, [((L('Gx'), ), (L('Gx', i), )),
((L('Gy'), ), (L('Gy', i), ))]))
#print(redmod_fiducials, "Fiducials")
cls.redmod_germs = pygsti.construction.gatestring_list([
(gl, ) for gl in gateLabels
])
cls.redmod_maxLs = [1]
expList = pygsti.construction.make_lsgst_experiment_list(
cls.gs_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.gs_redmod_datagen, expList, 1000, "round", seed=1234)
#redmod_ds.save(compare_files + "/calcMethods2Q_redmod.dataset%s" % cls.versionsuffix)
cls.redmod_ds = pygsti.objects.DataSet(
fileToLoadFrom=compare_files +
"/calcMethods2Q_redmod.dataset%s" % cls.versionsuffix)
#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
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(CalcMethods1QTestCase, 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
#Standard GST dataset
cls.maxLengths = [1, 2, 4]
cls.gs_datagen = std.gs_target.depolarize(gate_noise=0.1,
spam_noise=0.001)
cls.listOfExperiments = pygsti.construction.make_lsgst_experiment_list(
std.gs_target, std.prepStrs, std.effectStrs, std.germs,
cls.maxLengths)
#RUN BELOW FOR DATAGEN (UNCOMMENT to regenerate)
#ds = pygsti.construction.generate_fake_data(cls.gs_datagen, cls.listOfExperiments,
# nSamples=1000, sampleError="multinomial", seed=1234)
#ds.save(compare_files + "/calcMethods1Q.dataset%s" % cls.versionsuffix)
#DEBUG TEST- was to make sure data files have same info -- seemed ultimately unnecessary
#ds_swp = pygsti.objects.DataSet(fileToLoadFrom=compare_files + "/calcMethods1Q.datasetv3") # run in Python3
#pygsti.io.write_dataset(temp_files + "/dataset.3to2.txt", ds_swp) # run in Python3
#ds_swp = pygsti.io.load_dataset(temp_files + "/dataset.3to2.txt") # run in Python2
#ds_swp.save(compare_files + "/calcMethods1Q.dataset") # run in Python2
#assert(False),"STOP"
cls.ds = pygsti.objects.DataSet(
fileToLoadFrom=compare_files +
"/calcMethods1Q.dataset%s" % cls.versionsuffix)
#Reduced model GST dataset
cls.nQubits = 1
cls.gs_redmod_datagen = pc.build_nqnoise_gateset(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
gateLabels = list(cls.gs_redmod_datagen.gates.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_gatestring_list(
fids1Q, [((L('Gx'), ), (L('Gx', i), )),
((L('Gy'), ), (L('Gy', i), ))]))
#print(redmod_fiducials, "Fiducials")
cls.redmod_germs = pygsti.construction.gatestring_list([
(gl, ) for gl in gateLabels
])
cls.redmod_maxLs = [1]
expList = pygsti.construction.make_lsgst_experiment_list(
cls.gs_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.gs_redmod_datagen, expList, 1000, "round", seed=1234)
#redmod_ds.save(compare_files + "/calcMethods1Q_redmod.dataset%s" % cls.versionsuffix)
cls.redmod_ds = pygsti.objects.DataSet(
fileToLoadFrom=compare_files +
"/calcMethods1Q_redmod.dataset%s" % cls.versionsuffix)
#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_start25 = np.random.random(30) * 1e-6 # TODO: rename?
cls.rand_start36 = np.random.random(30) * 1e-6 # TODO: rename?
#Circuit Simulation circuits
cls.csim_nQubits = 3
cls.circuit1 = pygsti.obj.GateString(('Gx', 'Gy'))
# now Circuit adds qubit labels... pygsti.obj.Circuit(gatestring=('Gx','Gy'), num_lines=1) # 1-qubit circuit
cls.circuit3 = pygsti.obj.Circuit(gatestring=[('Gxpi', 0), ('Gypi', 1),
('Gcnot', 1, 2)],
num_lines=3) # 3-qubit circuit
os.chdir(origDir) # return to original directory