def test_circuitsim_stabilizer_1Qcheck(self):
from pygsti.modelpacks import smq1Q_XYI as stdChk
maxLengths = [1, 2, 4]
listOfExperiments = pygsti.circuits.create_lsgst_circuits(
stdChk.target_model(), stdChk.prep_fiducials(),
stdChk.meas_fiducials(), stdChk.germs(), maxLengths)
#listOfExperiments = pygsti.circuits.to_circuits([ ('Gcnot','Gxi') ])
#listOfExperiments = pygsti.circuits.to_circuits([ ('Gxi','Gcphase','Gxi','Gix') ])
mdl_normal = stdChk.target_model()
self.assertTrue(mdl_normal._evotype == "densitymx")
mdl_clifford = stdChk.target_model("static clifford",
evotype='stabilizer')
mdl_clifford.sim = "map" # only map sim works for stabilizer evotype
#print(mdl_clifford['Gcnot'])
#mdl_clifford.set_all_parameterizations('static unitary') # reduces dim...
#self.assertTrue(mdl_clifford._evotype == "statevec")
#mdl_clifford.set_all_parameterizations('clifford')
self.assertTrue(mdl_clifford._evotype == "stabilizer")
for opstr in listOfExperiments:
#print(str(opstr))
p_normal = mdl_normal.probabilities(opstr)
p_clifford = mdl_clifford.probabilities(opstr)
#p_clifford = bprobs[opstr]
for outcm in p_normal.keys():
if abs(p_normal[outcm] - p_clifford[outcm]) > 1e-8:
print(str(opstr), " ERR: \n", p_normal, "\n", p_clifford)
self.assertTrue(False)
print("Done checking %d sequences!" % len(listOfExperiments))
def test_stdgst_prunedpath(self):
# Using term-based (path integral) calculation with path pruning
# This performs a map-based unitary evolution along each path.
target_model = std.target_model("static unitary", evotype='statevec')
target_model.set_all_parameterizations("H+S")
target_model.sim = pygsti.forwardsims.TermForwardSimulator(
mode='pruned',
max_order=3,
desired_perr=0.01,
allowed_perr=0.1,
max_paths_per_outcome=1000,
perr_heuristic='meanscaled',
max_term_stages=5)
target_model.from_vector(
1e-10 * np.ones(target_model.num_params)
) # to seed term calc (starting with perfect zeros causes trouble)
results = pygsti.run_long_sequence_gst(self.ds,
target_model,
std.prep_fiducials(),
std.meas_fiducials(),
std.germs(),
self.maxLengths,
verbosity=3)
#RUN BELOW LINES TO SAVE GATESET (SAVE)
if regenerate_references():
pygsti.serialization.json.dump(
results.estimates[results.name].models['go0'],
open(compare_files + "/test1Qcalc_std_prunedpath.model", 'w'))
print("MISFIT nSigma = ",
results.estimates[results.name].misfit_sigma())
self.assertAlmostEqual(results.estimates[results.name].misfit_sigma(),
1,
delta=1.0)
def test_stdgst_map(self):
# Using map-based calculation
target_model = std.target_model()
target_model.set_all_parameterizations("CPTP")
target_model.sim = 'map'
results = pygsti.run_long_sequence_gst(self.ds,
target_model,
std.prep_fiducials(),
std.meas_fiducials(),
std.germs(),
self.maxLengths,
verbosity=4)
print("MISFIT nSigma = ",
results.estimates[results.name].misfit_sigma())
self.assertAlmostEqual(results.estimates[results.name].misfit_sigma(),
1.0,
delta=2.0)
mdl_compare = pygsti.serialization.json.load(
open(compare_files + "/test1Qcalc_std_exact.model"))
gsEstimate = results.estimates[results.name].models['go0'].copy()
gsEstimate.set_all_parameterizations("full")
gsEstimate = pygsti.algorithms.gaugeopt_to_target(
gsEstimate, mdl_compare)
self.assertAlmostEqual(gsEstimate.frobeniusdist(mdl_compare),
0,
places=0)
def test_stdgst_terms(self):
# Using term-based (path integral) calculation
# This performs a map-based unitary evolution along each path.
target_model = std.target_model("static unitary", evotype='statevec')
target_model.set_all_parameterizations("H+S")
target_model.sim = pygsti.forwardsims.TermForwardSimulator(
mode='taylor-order', max_order=1)
target_model._print_gpindices()
target_model.from_vector(
1e-10 * np.ones(target_model.num_params)
) # to seed term calc (starting with perfect zeros causes trouble)
results = pygsti.run_long_sequence_gst(self.ds,
target_model,
std.prep_fiducials(),
std.meas_fiducials(),
std.germs(),
self.maxLengths,
verbosity=4)
#RUN BELOW LINES TO SAVE GATESET (SAVE)
if regenerate_references():
pygsti.serialization.json.dump(
results.estimates[results.name].models['go0'],
open(compare_files + "/test1Qcalc_std_terms.model", 'w'))
print("MISFIT nSigma = ",
results.estimates[results.name].misfit_sigma())
self.assertAlmostEqual(results.estimates[results.name].misfit_sigma(),
1,
delta=1.0)
mdl_compare = pygsti.serialization.json.load(
open(compare_files + "/test1Qcalc_std_terms.model"))
# can't easily gauge opt b/c term-based models can't be converted to "full"
#mdl_compare.set_all_parameterizations("full")
#
#gsEstimate = results.estimates[results.name].models['go0'].copy()
#gsEstimate.set_all_parameterizations("full")
#gsEstimate = pygsti.algorithms.gaugeopt_to_target(gsEstimate, mdl_compare)
#self.assertAlmostEqual( gsEstimate.frobeniusdist(mdl_compare), 0, places=0)
#A direct vector comparison works if python (&numpy?) versions are identical, but
# gauge freedoms make this incorrectly fail in other cases - so just check sigmas
print("VEC DIFF = ",
(results.estimates[results.name].models['go0'].to_vector() -
mdl_compare.to_vector()))
self.assertAlmostEqual(np.linalg.norm(
results.estimates[results.name].models['go0'].to_vector() -
mdl_compare.to_vector()),
0,
places=1)
def test_MPI_probs(self):
comm = self.ralloc.comm
#Create some model
mdl = std.target_model()
mdl.kick(0.1, seed=1234)
#Get some operation sequences
maxLengths = [1, 2, 4]
circuits = pygsti.circuits.create_lsgst_circuits(
list(std.target_model().operations.keys()), std.prep_fiducials(),
std.meas_fiducials(), std.germs(), maxLengths)
#Check all-spam-label bulk probabilities
def compare_prob_dicts(a, b, indices=None):
for opstr in circuits:
for outcome in a[opstr].keys():
if indices is None:
assert (np.linalg.norm(a[opstr][outcome] -
b[opstr][outcome]) < 1e-6)
else:
for i in indices:
assert (np.linalg.norm(a[opstr][outcome][i] -
b[opstr][outcome][i]) <
1e-6)
# non-split tree => automatically adjusts wrt_block_size to accomodate
# the number of processors
serial = mdl.sim.bulk_probs(circuits, clip_to=(-1e6, 1e6))
parallel = mdl.sim.bulk_probs(circuits,
clip_to=(-1e6, 1e6),
resource_alloc=self.ralloc)
if comm is None or comm.rank == 0: # output is only given on root proc
compare_prob_dicts(serial, parallel)
serial = mdl.sim.bulk_dprobs(circuits)
parallel = mdl.sim.bulk_dprobs(circuits, resource_alloc=self.ralloc)
if comm is None or comm.rank == 0: # output is only given on root proc
compare_prob_dicts(serial, parallel)
serial = mdl.sim.bulk_hprobs(circuits)
parallel = mdl.sim.bulk_hprobs(circuits, resource_alloc=self.ralloc)
if comm is None or comm.rank == 0: # output is only given on root proc
compare_prob_dicts(serial, parallel, (0, 1, 2))
def test_stdgst_matrix(self):
# Using matrix-based calculations
target_model = std.target_model()
target_model.set_all_parameterizations("CPTP")
target_model.sim = 'matrix' # the default for 1Q, so we could remove this line
results = pygsti.run_long_sequence_gst(self.ds,
target_model,
std.prep_fiducials(),
std.meas_fiducials(),
std.germs(),
self.maxLengths,
verbosity=4)
#CHECK that copy gives identical models - this is checked by other
# unit tests but here we're using a true "GST model" - so do it again:
print("CHECK COPY")
mdl = results.estimates[results.name].models['go0']
mdl_copy = mdl.copy()
print(mdl.strdiff(mdl_copy))
self.assertAlmostEqual(mdl.frobeniusdist(mdl_copy), 0, places=2)
#RUN BELOW LINES TO SAVE GATESET (SAVE)
if regenerate_references():
pygsti.serialization.json.dump(
results.estimates[results.name].models['go0'],
open(compare_files + "/test1Qcalc_std_exact.model", 'w'))
print("MISFIT nSigma = ",
results.estimates[results.name].misfit_sigma())
self.assertAlmostEqual(results.estimates[results.name].misfit_sigma(),
1.0,
delta=2.0)
mdl_compare = pygsti.serialization.json.load(
open(compare_files + "/test1Qcalc_std_exact.model"))
#gauge opt before compare
gsEstimate = results.estimates[results.name].models['go0'].copy()
gsEstimate.set_all_parameterizations("full")
gsEstimate = pygsti.algorithms.gaugeopt_to_target(
gsEstimate, mdl_compare)
print(gsEstimate.strdiff(mdl_compare))
self.assertAlmostEqual(gsEstimate.frobeniusdist(mdl_compare),
0,
places=1)
def run_fills(self, sim, natoms, nparams):
comm = self.ralloc.comm
#Create some model
mdl = std.target_model()
mdl.kick(0.1, seed=1234)
#Get some operation sequences
maxLengths = [1]
circuits = pygsti.circuits.create_lsgst_circuits(
list(std.target_model().operations.keys()), std.prep_fiducials(),
std.meas_fiducials(), std.germs(), maxLengths)
nP = mdl.num_params
if sim == 'map':
mdl.sim = pygsti.forwardsims.MapForwardSimulator(
num_atoms=natoms, param_blk_sizes=(nparams, nparams))
elif sim == 'matrix':
mdl.sim = pygsti.forwardsims.MatrixForwardSimulator(
num_atoms=natoms, param_blk_sizes=(nparams, nparams))
else:
raise RuntimeError(
"Improper sim type passed by test_fills_generator")
serial_layout = mdl.sim.create_layout(circuits,
array_types=('E', 'EP', 'EPP'),
derivative_dimension=nP)
nE = serial_layout.num_elements
nC = len(circuits)
vp_serial = np.empty(nE, 'd')
vdp_serial = np.empty((nE, nP), 'd')
vhp_serial = np.empty((nE, nP, nP), 'd')
mdl.sim.bulk_fill_probs(vp_serial, serial_layout)
mdl.sim.bulk_fill_dprobs(vdp_serial, serial_layout)
mdl.sim.bulk_fill_hprobs(vhp_serial, serial_layout)
#Note: when there are multiple atoms, the serial_layout returned above may not preserve
# the original circuit ordering, i.e., serial_layout.circuits != circuits. The global
# layout does have this property, and so we use it to avoid having to lookup which circuit
# is which index in serial_layout. No gathering is needed, since there only 1 processor.
global_serial_layout = serial_layout.global_layout
#Use a parallel layout to compute the same probabilities & their derivatives
local_layout = mdl.sim.create_layout(circuits,
array_types=('E', 'EP', 'EPP'),
derivative_dimension=nP,
resource_alloc=self.ralloc)
vp_local = local_layout.allocate_local_array('e', 'd')
vdp_local = local_layout.allocate_local_array('ep', 'd')
vhp_local = local_layout.allocate_local_array('epp', 'd')
mdl.sim.bulk_fill_probs(vp_local, local_layout)
mdl.sim.bulk_fill_dprobs(vdp_local, local_layout)
mdl.sim.bulk_fill_hprobs(vhp_local, local_layout)
#Gather data to produce arrays for the "global" layout (global_parallel_layout should be the same on all procs)
# but only on proc 0
global_parallel_layout = local_layout.global_layout
vp_global_parallel = local_layout.gather_local_array('e', vp_local)
vdp_global_parallel = local_layout.gather_local_array('ep', vdp_local)
vhp_global_parallel = local_layout.gather_local_array('epp', vhp_local)
#Free the local arrays when we're done with them (they could be shared mem)
local_layout.free_local_array(vp_local)
local_layout.free_local_array(vdp_local)
local_layout.free_local_array(vhp_local)
#Compare the two, but note that different layouts may order the elements differently,
# so we can't just compare the arrays directly - we have to use the layout to map
# circuit index -> element indices:
if comm is None or comm.rank == 0:
for i, opstr in enumerate(circuits):
assert (np.linalg.norm(vp_global_parallel[
global_parallel_layout.indices_for_index(i)] - vp_serial[
global_serial_layout.indices_for_index(i)]) < 1e-6)
assert (np.linalg.norm(vdp_global_parallel[
global_parallel_layout.indices_for_index(i)] - vdp_serial[
global_serial_layout.indices_for_index(i)]) < 1e-6)
assert (np.linalg.norm(vhp_global_parallel[
global_parallel_layout.indices_for_index(i)] - vhp_serial[
global_serial_layout.indices_for_index(i)]) < 1e-6)
def test_creation(self):
gst.GateSetTomographyDesign(smq1Q_XYI.processor_spec(),
smq1Q_XYI.prep_fiducials(),
smq1Q_XYI.meas_fiducials(),
smq1Q_XYI.germs(), [1, 2])
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.mdl_datagen = std.target_model().depolarize(op_noise=0.03,
spam_noise=0.001)
cls.listOfExperiments = pygsti.circuits.create_lsgst_circuits(
std.target_model(), std.prep_fiducials(), std.meas_fiducials(),
std.germs(), cls.maxLengths)
#RUN BELOW FOR DATAGEN (SAVE)
if regenerate_references():
ds = pygsti.data.simulate_data(cls.mdl_datagen,
cls.listOfExperiments,
num_samples=1000,
sample_error="multinomial",
seed=1234)
ds.save(compare_files + "/calcMethods1Q.dataset")
#DEBUG TEST- was to make sure data files have same info -- seemed ultimately unnecessary
#ds_swp = pygsti.objects.DataSet(file_to_load_from=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.read_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.data.DataSet(file_to_load_from=compare_files +
"/calcMethods1Q.dataset")
#Reduced model GST dataset
cls.nQubits = 1 # can't just change this now - see op_labels below
cls.mdl_redmod_datagen = build_XYCNOT_cloudnoise_model(
cls.nQubits,
geometry="line",
maxIdleWeight=1,
maxhops=1,
extraWeight1Hops=0,
extraGateWeight=1,
simulator="matrix",
verbosity=1,
roughNoise=(1234, 0.01))
#Create a reduced set of fiducials and germs
op_labels = [L('Gx', 0), L('Gy', 0)] # 1Q gate labels
fids1Q = std1Q_XY.fiducials[
1:2] # for speed, just take 1 non-empty fiducial
cls.redmod_fiducials = [
Circuit([], line_labels=(0, ))
] # special case for empty fiducial (need to change line label)
for i in range(cls.nQubits):
cls.redmod_fiducials.extend(
pygsti.circuits.manipulate_circuits(fids1Q,
[((L('Gx'), ),
(L('Gx', i), )),
((L('Gy'), ),
(L('Gy', i), ))]))
#print(redmod_fiducials, "Fiducials")
cls.redmod_germs = pygsti.circuits.to_circuits([(gl, )
for gl in op_labels])
cls.redmod_maxLs = [1]
expList = pygsti.circuits.create_lsgst_circuits(
op_labels, cls.redmod_fiducials, cls.redmod_fiducials,
cls.redmod_germs, cls.redmod_maxLs)
#RUN BELOW FOR DATAGEN (SAVE)
if regenerate_references():
redmod_ds = pygsti.data.simulate_data(cls.mdl_redmod_datagen,
expList,
1000,
"round",
seed=1234)
redmod_ds.save(compare_files + "/calcMethods1Q_redmod.dataset")
cls.redmod_ds = pygsti.data.DataSet(file_to_load_from=compare_files +
"/calcMethods1Q_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_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.circuits.Circuit((('Gxpi2', 0), ('Gypi2', 0)))
# now Circuit adds qubit labels... pygsti.circuits.Circuit(layer_labels=('Gx','Gy'), num_lines=1) # 1-qubit circuit
cls.circuit3 = pygsti.circuits.Circuit(layer_labels=[('Gxpi', 0),
('Gypi', 1),
('Gcnot', 1, 2)],
num_lines=3) # 3-qubit circuit
os.chdir(origDir) # return to original directory