def test_timed_block(self):
with timed_block('40th fibonacci'):
print(fib(40))
timeDict = defaultdict(list)
with timed_block('100th fibonacci', timeDict): # Duration saved to dict under label "100th fibonacci"
print(fib(100))
#test "preMessage" argument
with timed_block('40th fibonacci', preMessage="Hello"):
print(fib(40))
def main():
gs_target = std2Q_XYICNOT.gs_target
gs = gs_target.depolarize(gate_noise=0.1, spam_noise=0.001).rotate(0.1)
gs = gs.kick(0.1, seed=1234)
gs_target.set_all_parameterizations("TP")
gs = contract(gs, "TP")
gs.set_all_parameterizations("TP")
#del gs.spamdefs['11']
#del gs_target.spamdefs['11']
#del gs.preps['rho0']
#del gs_target.preps['rho0']
#envSettings = dict(MKL_NUM_THREADS=1, NUMEXPR_NUM_THREADS=1, OMP_NUM_THREADS=1)
print(gs.get_prep_labels())
print(gs.get_effect_labels())
print(gs.num_elements(include_povm_identity=True))
print(gs.spamdefs)
with timed_block('Basic gauge opt:'):
gs_gaugeopt = gaugeopt_to_target(
gs, gs_target,
#method="L-BFGS-B",
method="auto",
itemWeights={'spam' : 0.0001, 'gates':1.0},
spamMetric='frobenius',
gatesMetric='frobenius',
cptp_penalty_factor=1.0,
spam_penalty_factor=1.0,
comm=comm, verbosity=3, checkJac=True)
if comm is None or comm.Get_rank() == 0:
print("Final Diff = ", gs_gaugeopt.frobeniusdist(gs_target, None, 1.0, 0.0001))
print(gs_gaugeopt.strdiff(gs_target))
def main():
with open('data/example_report_results.pkl', 'rb') as infile:
results = pickle.load(infile)
with timed_block('example report creation'):
pygsti.report.create_general_report(results,
"report/exampleGenReport.html",
verbosity=0,
auto_open=False)
def main():
gs, gs_target = load()
#envSettings = dict(MKL_NUM_THREADS=1, NUMEXPR_NUM_THREADS=1, OMP_NUM_THREADS=1)
with timed_block('TP penalty gauge opt'):
gs_gaugeopt = pygsti.gaugeopt_to_target(gs, gs_target,
item_weights={'spam' : 0.0001, 'gates':1.0},
TPpenalty=1.0)
def main():
digest = pygsti.tools.smartcache.digest
native = pygsti.tools.smartcache.native_hash
iterations = 100
s = 'adfasdkfj;asldfa;ldfja;sdfja;sdfjas;dlkfja;sdfafad1*(@#&$)(@#*&$)(#&@'
l = [(s, s) for i in range(50)]
t = tuple(l)
d = dict(l)
keys = [s, l, t, d]
for key in keys:
with timed_block('digest_' + str(type(key))):
for i in range(iterations):
digest(key)
with timed_block('native_' + str(type(key))):
for i in range(iterations):
native(key)
def main():
gs, gs_target = load()
with timed_block('Gauge opt with CP Penalty:'):
gs_gaugeopt = pygsti.gaugeopt_to_target(gs,
gs_target,
itemWeights={
'spam': 0.0001,
'gates': 1.0
},
CPpenalty=1.0,
validSpamPenalty=1.0)
def main():
with open('data/full_report_results.pkl', 'rb') as infile:
results_tp, results_full = pickle.load(infile)
with timed_block('TP/Full multi report'):
ws = pygsti.report.create_general_report(
{
'TP': results_tp,
"Full": results_full
},
"tutorial_files/exampleMultiGenReport.html",
verbosity=3,
auto_open=False)
def main():
with open('data/full_report_results.pkl', 'rb') as infile:
results_tp, results_full = pickle.load(infile)
ws = pygsti.report.Workspace('data/wscache.pkl')
with timed_block('reused ws'):
pygsti.report.create_general_report(
{
'TP': results_tp,
"Full": results_full
},
"tutorial_files/exampleMultiGenReport.html",
verbosity=3,
auto_open=False,
ws=ws)
def main():
gs_target = pygsti.construction.build_gateset(
[8], [('Q0','Q1','Q2')],['Gx1','Gy1','Gx2','Gy2','Gx3','Gy3','Gcnot12','Gcnot23'],
[ "X(pi/2,Q0):I(Q1):I(Q2)", "Y(pi/2,Q0):I(Q1):I(Q2)", "I(Q0):X(pi/2,Q1):I(Q2)", "I(Q0):Y(pi/2,Q1):I(Q2)",
"I(Q0):I(Q1):X(pi/2,Q2)", "I(Q0):I(Q1):Y(pi/2,Q2)", "CX(pi,Q0,Q1):I(Q2)", "I(Q0):CX(pi,Q1,Q2)"],
prep_labels=['rho0'], prep_expressions=["0"],
effect_labels=['E0','E1','E2','E3','E4','E5','E6'], effect_expressions=["0","1","2","3","4","5","6"],
spamdefs={'upupup': ('rho0','E0'), 'upupdn': ('rho0','E1'), 'updnup': ('rho0','E2'), 'updndn': ('rho0','E3'),
'dnupup': ('rho0','E4'), 'dnupdn': ('rho0','E5'), 'dndnup': ('rho0','E6'), 'dndndn': ('rho0','remainder')},
basis="pp")
gs = load_3q()
with timed_block('Basic gauge opt (3Q)'):
gs_gaugeopt = gaugeopt_to_target(gs, gs_target,
item_weights={'spam' : 0.0001, 'gates':1.0},
spam_metric='frobenius',
gates_metric='frobenius')
def main():
gs_target = std1Q_XYI.gs_target
gs_datagen = gs_target.depolarize(gate_noise=0.1,
spam_noise=0.001).rotate(0.1)
#DEBUG
#del gs_target.spamdefs['1']
#del gs_datagen.spamdefs['1']
print(gs_datagen.get_prep_labels())
print(gs_datagen.get_effect_labels())
print(gs_datagen.num_elements(include_povm_identity=True))
print(gs_datagen.spamdefs)
with timed_block('Basic gauge opt:'):
gs_gaugeopt = gaugeopt_to_target(
gs_datagen,
gs_target,
tol=1e-7,
method="auto",
#method="L-BFGS-B",
item_weights={
'spam': 1.0,
'gates': 1.0
},
spam_metric='frobenius',
gates_metric='frobenius',
check_jac=True,
cptp_penalty_factor=1.0,
spam_penalty_factor=1.0,
comm=comm,
verbosity=3)
if comm is None or comm.Get_rank() == 0:
print("Final Diff = ",
gs_gaugeopt.frobeniusdist(gs_target, None, 1.0, 1.0))
print(gs_gaugeopt.strdiff(gs_target))
def main():
gates = ['Gi', 'Gx', 'Gy']
fiducials = pc.gatestring_list([(), ('Gx', ), ('Gy', ), ('Gx', 'Gx'),
('Gx', 'Gx', 'Gx'), ('Gy', 'Gy', 'Gy')
]) # fiducials for 1Q MUB
germs = pc.gatestring_list([('Gx', ), ('Gy', ), ('Gi', ), (
'Gx',
'Gy',
), (
'Gx',
'Gy',
'Gi',
), (
'Gx',
'Gi',
'Gy',
), (
'Gx',
'Gi',
'Gi',
), (
'Gy',
'Gi',
'Gi',
), (
'Gx',
'Gx',
'Gi',
'Gy',
), (
'Gx',
'Gy',
'Gy',
'Gi',
), (
'Gx',
'Gx',
'Gy',
'Gx',
'Gy',
'Gy',
)])
maxLengths = [1, 2, 4, 8, 16, 32, 64, 128, 256]
lsgst_lists = pc.create_lsgst_circuits(gates, fiducials, fiducials, germs,
maxLengths)
lsgst_tuple = tuple(lsgst_lists)
iterations = 1000
timeDict = dict()
with timed_block('hash_gatestring_list', timeDict):
for i in range(iterations):
hash(lsgst_tuple)
exampleUUID = uuid.uuid4()
alt_hash = pygsti.tools.smartcache.digest
with timed_block('digest_uuid', timeDict):
for i in range(iterations):
alt_hash(exampleUUID)
print('Hashing gslist of length {} takes {} seconds on average'.format(
len(lsgst_tuple), timeDict['hash_gatestring_list'] / iterations))
print('UUID digest takes {} seconds on average'.format(
timeDict['digest_uuid'] / iterations))
