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testCalcMethods1Q.py
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testCalcMethods1Q.py
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from __future__ import print_function
#quiet down matplotlib!
import logging
mpl_logger = logging.getLogger('matplotlib')
mpl_logger.setLevel(logging.WARNING)
import unittest
import numpy as np
import scipy.linalg as spl
import pygsti
import pygsti.construction as pc
from pygsti.construction import std1Q_XYI as std
from pygsti.construction import std1Q_XY
from pygsti.objects import Label as L
from pygsti.io import json
import sys, os
from ..testutils import BaseTestCase, compare_files, temp_files
#Mimics a function that used to be in pyGSTi, replaced with build_standard_cloudnoise_model
def build_XYCNOT_cloudnoise_model(nQubits, geometry="line", cnot_edges=None,
maxIdleWeight=1, maxSpamWeight=1, maxhops=0,
extraWeight1Hops=0, extraGateWeight=0, sparse=False,
roughNoise=None, sim_type="matrix", parameterization="H+S",
spamtype="lindblad", addIdleNoiseToAllGates=True,
errcomp_type="gates", return_clouds=False, verbosity=0):
#from pygsti.construction import std1Q_XY # the base model for 1Q gates
#from pygsti.construction import std2Q_XYICNOT # the base model for 2Q (CNOT) gate
#
#tgt1Q = std1Q_XY.target_model()
#tgt2Q = std2Q_XYICNOT.target_model()
#Gx = tgt1Q.operations['Gx']
#Gy = tgt1Q.operations['Gy']
#Gcnot = tgt2Q.operations['Gcnot']
#gatedict = _collections.OrderedDict([('Gx',Gx),('Gy',Gy),('Gcnot',Gcnot)])
availability = {}; nonstd_gate_unitaries = {}
if cnot_edges is not None: availability['Gcnot'] = cnot_edges
return pc.build_standard_cloudnoise_model(nQubits, ['Gx','Gy','Gcnot'], nonstd_gate_unitaries, availability,
None, geometry, maxIdleWeight, maxSpamWeight, maxhops,
extraWeight1Hops, extraGateWeight, sparse,
roughNoise, sim_type, parameterization,
spamtype, addIdleNoiseToAllGates,
errcomp_type, True, return_clouds, verbosity)
class CalcMethods1QTestCase(BaseTestCase):
@classmethod
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=0.1, spam_noise=0.001)
cls.listOfExperiments = pygsti.construction.make_lsgst_experiment_list(
std.target_model(), std.prepStrs, std.effectStrs, std.germs, cls.maxLengths)
#RUN BELOW FOR DATAGEN (UNCOMMENT to regenerate) (SAVE)
if os.environ.get('PYGSTI_REGEN_REF_FILES','no').lower() in ("yes","1","true","v2"): # "v2" to only gen version-dep files
ds = pygsti.construction.generate_fake_data(cls.mdl_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 # can't just change this now - see opLabels below
cls.mdl_redmod_datagen = build_XYCNOT_cloudnoise_model(cls.nQubits, geometry="line", maxIdleWeight=1, maxhops=1,
extraWeight1Hops=0, extraGateWeight=1, sparse=False,
sim_type="matrix", verbosity=1, roughNoise=(1234,0.01))
#Create a reduced set of fiducials and germs
opLabels = [ ('Gx',0), ('Gy',0) ] # 1Q gate labels
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(
opLabels, cls.redmod_fiducials, cls.redmod_fiducials,
cls.redmod_germs, cls.redmod_maxLs)
#RUN BELOW FOR DATAGEN (UNCOMMENT to regenerate) (SAVE)
if os.environ.get('PYGSTI_REGEN_REF_FILES','no').lower() in ("yes","1","true","v2"): # "v2" to only gen version-dep files
redmod_ds = pygsti.construction.generate_fake_data(cls.mdl_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.Circuit(('Gx','Gy'))
# now Circuit adds qubit labels... pygsti.obj.Circuit(layer_labels=('Gx','Gy'), num_lines=1) # 1-qubit circuit
cls.circuit3 = pygsti.obj.Circuit(layer_labels=[ ('Gxpi',0), ('Gypi',1),('Gcnot',1,2)], num_lines=3) # 3-qubit circuit
os.chdir(origDir) # return to original directory
def assert_outcomes(self, probs, expected):
for k,v in probs.items():
self.assertAlmostEqual(v, expected[k])
## GST using "full" (non-embedded/composed) gates
# All of these calcs use dense matrices; While sparse operation matrices (as Maps) could be used,
# they'd need to enter as a sparse basis to a LindbladDenseOp (maybe add this later?)
def test_stdgst_matrix(self):
# Using matrix-based calculations
target_model = std.target_model()
target_model.set_all_parameterizations("CPTP")
target_model.set_simtype('matrix') # the default for 1Q, so we could remove this line
results = pygsti.do_long_sequence_gst(self.ds, target_model, std.prepStrs, std.effectStrs,
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['default'].models['go0']
mdl_copy = mdl.copy()
print(mdl.strdiff(mdl_copy))
self.assertAlmostEqual( mdl.frobeniusdist(mdl_copy), 0, places=3)
#RUN BELOW LINES TO SAVE GATESET (UNCOMMENT to regenerate) (SAVE)
if os.environ.get('PYGSTI_REGEN_REF_FILES','no').lower() in ("yes","1","true"):
pygsti.io.json.dump(results.estimates['default'].models['go0'],
open(compare_files + "/test1Qcalc_std_exact.model",'w'))
print("MISFIT nSigma = ",results.estimates['default'].misfit_sigma())
self.assertAlmostEqual( results.estimates['default'].misfit_sigma(), 3.0, delta=2.0)
mdl_compare = pygsti.io.json.load(open(compare_files + "/test1Qcalc_std_exact.model"))
#gauge opt before compare
gsEstimate = results.estimates['default'].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=3)
def test_stdgst_map(self):
# Using map-based calculation
target_model = std.target_model()
target_model.set_all_parameterizations("CPTP")
target_model.set_simtype('map')
results = pygsti.do_long_sequence_gst(self.ds, target_model, std.prepStrs, std.effectStrs,
std.germs, self.maxLengths, verbosity=4)
print("MISFIT nSigma = ",results.estimates['default'].misfit_sigma())
self.assertAlmostEqual( results.estimates['default'].misfit_sigma(), 3.0, delta=2.0)
mdl_compare = pygsti.io.json.load(open(compare_files + "/test1Qcalc_std_exact.model"))
gsEstimate = results.estimates['default'].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)
# with low tolerance (1e-6), "map" tends to go for more iterations than "matrix",
# resulting in a model that isn't exactly the same as the "matrix" one
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()
target_model.set_all_parameterizations("H+S terms")
target_model.set_simtype('termorder:1') # this is the default set by set_all_parameterizations above
results = pygsti.do_long_sequence_gst(self.ds, target_model, std.prepStrs, std.effectStrs,
std.germs, self.maxLengths, verbosity=1)
#RUN BELOW LINES TO SAVE GATESET (UNCOMMENT to regenerate) (SAVE)
if os.environ.get('PYGSTI_REGEN_REF_FILES','no').lower() in ("yes","1","true"):
pygsti.io.json.dump(results.estimates['default'].models['go0'],
open(compare_files + "/test1Qcalc_std_terms.model",'w'))
print("MISFIT nSigma = ",results.estimates['default'].misfit_sigma())
self.assertAlmostEqual( results.estimates['default'].misfit_sigma(), 5, delta=1.0)
mdl_compare = pygsti.io.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['default'].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['default'].models['go0'].to_vector()
- mdl_compare.to_vector()))
self.assertAlmostEqual( np.linalg.norm(results.estimates['default'].models['go0'].to_vector()
- mdl_compare.to_vector()), 0, places=3)
# ## GST using "reduced" models
# Reduced, meaning that we use composed and embedded gates to form a more complex error model with
# shared parameters and qubit connectivity graphs. Calculations *can* use dense matrices and matrix calcs,
# but usually will use sparse mxs and map-based calcs.
def test_reducedmod_matrix(self):
# Using dense matrices and matrix-based calcs
target_model = build_XYCNOT_cloudnoise_model(self.nQubits, geometry="line", maxIdleWeight=1, maxhops=1,
extraWeight1Hops=0, extraGateWeight=1, sparse=False,
sim_type="matrix", verbosity=1)
print("Num params = ",target_model.num_params())
target_model.from_vector(self.rand_start25)
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) (SAVE)
if os.environ.get('PYGSTI_REGEN_REF_FILES','no').lower() in ("yes","1","true"):
pygsti.io.json.dump(results.estimates['default'].models['go0'],
open(compare_files + "/test1Qcalc_redmod_exact.model",'w'))
print("MISFIT nSigma = ",results.estimates['default'].misfit_sigma())
self.assertAlmostEqual( results.estimates['default'].misfit_sigma(), 0.0, delta=1.0)
#mdl_compare = pygsti.io.json.load( open(compare_files + "/test1Qcalc_redmod_exact.model"))
#self.assertAlmostEqual( results.estimates['default'].models['go0'].frobeniusdist(mdl_compare), 0, places=3)
#NO frobeniusdist for implicit models (yet)
def test_reducedmod_map1(self):
# Using dense embedded matrices and map-based calcs (maybe not really necessary to include?)
target_model = build_XYCNOT_cloudnoise_model(self.nQubits, geometry="line", maxIdleWeight=1, maxhops=1,
extraWeight1Hops=0, extraGateWeight=1, sparse=False,
sim_type="map", errcomp_type='gates', verbosity=1)
print("Num params = ",target_model.num_params())
target_model.from_vector(self.rand_start25)
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(), 0.0, delta=1.0)
#mdl_compare = pygsti.io.json.load( open(compare_files + "/test1Qcalc_redmod_exact.model"))
#self.assertAlmostEqual( results.estimates['default'].models['go0'].frobeniusdist(mdl_compare), 0, places=1)
#NO frobeniusdist for implicit models (yet)
#Note: models aren't necessarily exactly equal given gauge freedoms that we don't know
# how to optimizize over exactly - so this is a very loose test...
def test_reducedmod_map1_errorgens(self):
# Using dense embedded matrices and map-based calcs (same as above)
# but w/*errcomp_type=errogens* Model (maybe not really necessary to include?)
target_model = build_XYCNOT_cloudnoise_model(self.nQubits, geometry="line", maxIdleWeight=1, maxhops=1,
extraWeight1Hops=0, extraGateWeight=1, sparse=False,
sim_type="map", errcomp_type='errorgens', verbosity=1)
print("Num params = ",target_model.num_params())
target_model.from_vector(self.rand_start25)
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(), 0.0, delta=1.0)
#Note: we don't compare errorgens models to a reference model yet...
def test_reducedmod_map2(self):
# Using sparse embedded matrices and map-based calcs
target_model = build_XYCNOT_cloudnoise_model(self.nQubits, geometry="line", maxIdleWeight=1, maxhops=1,
extraWeight1Hops=0, extraGateWeight=1, sparse=True,
sim_type="map", errcomp_type='gates', verbosity=1)
print("Num params = ",target_model.num_params())
target_model.from_vector(self.rand_start25)
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(), 0.0, delta=1.0)
mdl_compare = pygsti.io.json.load( open(compare_files + "/test1Qcalc_redmod_exact.model"))
self.assertAlmostEqual( np.linalg.norm(results.estimates['default'].models['go0'].to_vector()
- mdl_compare.to_vector()), 0, places=1)
#Note: models aren't necessarily exactly equal given gauge freedoms that we don't know
# how to optimizize over exactly - so this is a very loose test...
def test_reducedmod_map2_errorgens(self):
# Using sparse embedded matrices and map-based calcs (same as above)
# but w/*errcomp_type=errogens* Model (maybe not really necessary to include?)
target_model = build_XYCNOT_cloudnoise_model(self.nQubits, geometry="line", maxIdleWeight=1, maxhops=1,
extraWeight1Hops=0, extraGateWeight=1, sparse=True,
sim_type="map", errcomp_type='errorgens', verbosity=1)
print("Num params = ",target_model.num_params())
target_model.from_vector(self.rand_start25)
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(), 0.0, delta=1.0)
#Note: we don't compare errorgens models to a reference model yet...
def test_reducedmod_svterm(self):
# Using term-based calcs using map-based state-vector propagation
target_model = build_XYCNOT_cloudnoise_model(self.nQubits, geometry="line", maxIdleWeight=1, maxhops=1,
extraWeight1Hops=0, extraGateWeight=1, sparse=False, verbosity=1,
sim_type="termorder:1", parameterization="H+S terms", errcomp_type='gates')
print("Num params = ",target_model.num_params())
target_model.from_vector(self.rand_start36)
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) (SAVE)
if os.environ.get('PYGSTI_REGEN_REF_FILES','no').lower() in ("yes","1","true"):
pygsti.io.json.dump(results.estimates['default'].models['go0'],
open(compare_files + "/test1Qcalc_redmod_terms.model",'w'))
print("MISFIT nSigma = ",results.estimates['default'].misfit_sigma())
self.assertAlmostEqual( results.estimates['default'].misfit_sigma(), 0.0, delta=1.0)
mdl_compare = pygsti.io.json.load( open(compare_files + "/test1Qcalc_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_svterm_errogens(self):
# Using term-based calcs using map-based state-vector propagation (same as above)
# but w/errcomp_type=errogens Model
target_model = build_XYCNOT_cloudnoise_model(self.nQubits, geometry="line", maxIdleWeight=1, maxhops=1,
extraWeight1Hops=0, extraGateWeight=1, sparse=False, verbosity=1,
sim_type="termorder:1", parameterization="H+S terms", errcomp_type='errorgens')
print("Num params = ",target_model.num_params())
target_model.from_vector(self.rand_start36)
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(), 0.0, delta=1.0)
#Note: we don't compare errorgens models to a reference model yet...
def test_reducedmod_cterm(self):
# Using term-based calcs using map-based stabilizer-state propagation
target_model = build_XYCNOT_cloudnoise_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", errcomp_type='gates')
print("Num params = ",target_model.num_params())
target_model.from_vector(self.rand_start36)
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(), 0.0, delta=1.0)
mdl_compare = pygsti.io.json.load( open(compare_files + "/test1Qcalc_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_cterm_errorgens(self):
# Using term-based calcs using map-based stabilizer-state propagation (same as above)
# but w/errcomp_type=errogens Model
target_model = build_XYCNOT_cloudnoise_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", errcomp_type='errorgens')
print("Num params = ",target_model.num_params())
target_model.from_vector(self.rand_start36)
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(), 0.0, delta=1.0)
#Note: we don't compare errorgens models to a reference model yet...
# ### Circuit Simulation
def test_circuitsim_densitymx(self):
# Density-matrix simulation (of superoperator gates)
# These are the typical type of simulations used within GST.
# The probability calculations can be done in a matrix- or map-based way.
#Using simple "std" models (which are all density-matrix/superop type)
mdl = std.target_model()
probs1 = mdl.probs(self.circuit1)
#self.circuit1.simulate(mdl) # calls probs - same as above line
print(probs1)
gs2 = std.target_model()
gs2.set_simtype("map")
probs1 = gs2.probs(self.circuit1)
#self.circuit1.simulate(gs2) # calls probs - same as above line
print(probs1)
self.assert_outcomes(probs1, {('0',): 0.5, ('1',): 0.5} )
#Using n-qubit models
mdl = pygsti.construction.build_standard_localnoise_model(
self.csim_nQubits, ['Gi','Gxpi','Gypi','Gcnot'], sim_type="matrix", ensure_composed_gates=False)
probs1 = mdl.probs(self.circuit3)
mdl = pygsti.construction.build_standard_localnoise_model(
self.csim_nQubits, ['Gi','Gxpi','Gypi','Gcnot'], sim_type="map", ensure_composed_gates=False)
probs2 = mdl.probs(self.circuit3)
expected = { ('000',): 0.0,
('001',): 0.0,
('010',): 0.0,
('011',): 0.0,
('100',): 0.0,
('101',): 0.0,
('110',): 0.0,
('111',): 1.0 }
print(probs1)
print(probs2)
self.assert_outcomes(probs1, expected)
self.assert_outcomes(probs2, expected)
def test_circuitsim_statevec(self):
# State-vector simulation (of unitary gates)
# This can be done with matrix- or map-based calculations.
#Unitary model in pygsti (from scratch, since "std" modules don't include them)
sigmax = np.array([[0,1],[1,0]])
sigmay = np.array([[0,-1.0j],[1.0j,0]])
sigmaz = np.array([[1,0],[0,-1]])
def Uop(exp):
return np.array(spl.expm(-1j * exp/2),complex) # 2x2 unitary matrix operating on single qubit in [0,1] basis
#Create a model with unitary gates and state vectors (instead of the usual superoperators and density mxs)
mdl = pygsti.obj.ExplicitOpModel(['Q0'],evotype='statevec',sim_type="matrix")
mdl.operations['Gi'] = pygsti.obj.StaticDenseOp( np.identity(2,'complex') )
mdl.operations['Gx'] = pygsti.obj.StaticDenseOp(Uop(np.pi/2 * sigmax))
mdl.operations['Gy'] = pygsti.obj.StaticDenseOp(Uop(np.pi/2 * sigmay))
mdl.preps['rho0'] = pygsti.obj.StaticSPAMVec( [1,0], 'statevec')
mdl.povms['Mdefault'] = pygsti.obj.UnconstrainedPOVM(
{'0': pygsti.obj.StaticSPAMVec( [1,0], 'statevec'),
'1': pygsti.obj.StaticSPAMVec( [0,1], 'statevec')})
probs1 = mdl.probs(self.circuit1)
#self.circuit1.simulate(mdl) # calls probs - same as above line
print(probs1)
self.assert_outcomes(probs1, {('0',): 0.5, ('1',): 0.5} )
gs2 = mdl.copy()
gs2.set_simtype("map")
gs2.probs(self.circuit1)
#self.circuit1.simulate(gs2) # calls probs - same as above line
#Using n-qubit models
mdl = pygsti.construction.build_standard_localnoise_model(
self.csim_nQubits, ['Gi','Gxpi','Gypi','Gcnot'], evotype="statevec", sim_type="matrix", ensure_composed_gates=False)
probs1 = mdl.probs(self.circuit3)
mdl = pygsti.construction.build_standard_localnoise_model(
self.csim_nQubits, ['Gi','Gxpi','Gypi','Gcnot'], evotype="statevec", sim_type="map", ensure_composed_gates=False)
probs2 = mdl.probs(self.circuit3)
expected = { ('000',): 0.0,
('001',): 0.0,
('010',): 0.0,
('011',): 0.0,
('100',): 0.0,
('101',): 0.0,
('110',): 0.0,
('111',): 1.0 }
print(probs1)
print(probs2)
self.assert_outcomes(probs1, expected)
self.assert_outcomes(probs2, expected)
def test_circuitsim_svterm(self):
# ### Density-matrix simulation (of superoperator gates) using map/matrix-based terms calcs
# In this mode, "term calcs" use many state-vector propagation paths to simulate density
# matrix propagation up to some desired order (in the assumed-to-be-small error rates).
mdl = std.target_model()
mdl.set_simtype('termorder:1') # 1st-order in error rates
mdl.set_all_parameterizations("H+S terms")
probs1 = mdl.probs(self.circuit1)
#self.circuit1.simulate(mdl) # calls probs - same as above line
print(probs1)
self.assert_outcomes(probs1, {('0',): 0.5, ('1',): 0.5} )
#Using n-qubit models ("H+S terms" parameterization constructs embedded/composed gates containing LindbladTermGates, etc.)
mdl = pygsti.construction.build_standard_localnoise_model(
self.csim_nQubits, ['Gi','Gxpi','Gypi','Gcnot'], sim_type="termorder:1",
parameterization="H+S terms", ensure_composed_gates=False)
probs1 = mdl.probs(self.circuit3)
probs2 = self.circuit3.simulate(mdl) # calls probs - same as above line
print(probs1)
print(probs2)
self.assert_outcomes(probs1, { ('000',): 0.0,
('001',): 0.0,
('010',): 0.0,
('011',): 0.0,
('100',): 0.0,
('101',): 0.0,
('110',): 0.0,
('111',): 1.0 } )
self.assert_outcomes(probs2, { ('111',): 1.0 } ) # only returns nonzero outcomes by default
def test_circuitsim_stabilizer(self):
# Stabilizer-state simulation (of Clifford gates) using map-based calc
c0 = pygsti.obj.Circuit(layer_labels=(), num_lines=1) # 1-qubit circuit
c1 = pygsti.obj.Circuit(layer_labels=(('Gx',0),), num_lines=1)
c2 = pygsti.obj.Circuit(layer_labels=(('Gx',0),('Gx',0)), num_lines=1)
c3 = pygsti.obj.Circuit(layer_labels=(('Gx',0),('Gx',0),('Gx',0),('Gx',0)), num_lines=1)
mdl = pygsti.construction.build_standard_localnoise_model(
1, ['Gi','Gx','Gy'], parameterization="clifford", ensure_composed_gates=False)
probs0 = mdl.probs(c0)
probs1 = mdl.probs(c1)
probs2 = mdl.probs(c2)
probs3 = mdl.probs(c3)
self.assert_outcomes(probs0, {('0',): 1.0, ('1',): 0.0} )
self.assert_outcomes(probs1, {('0',): 0.5, ('1',): 0.5} )
self.assert_outcomes(probs2, {('0',): 0.0, ('1',): 1.0} )
self.assert_outcomes(probs3, {('0',): 1.0, ('1',): 0.0} )
def test_circuitsim_stabilizer_1Qcheck(self):
from pygsti.construction import std1Q_XYI as stdChk
maxLengths = [1,2,4]
listOfExperiments = pygsti.construction.make_lsgst_experiment_list(
stdChk.target_model(), stdChk.prepStrs, stdChk.effectStrs, stdChk.germs, maxLengths)
#listOfExperiments = pygsti.construction.circuit_list([ ('Gcnot','Gxi') ])
#listOfExperiments = pygsti.construction.circuit_list([ ('Gxi','Gcphase','Gxi','Gix') ])
mdl_normal = stdChk.target_model()
mdl_clifford = stdChk.target_model()
#print(mdl_clifford['Gcnot'])
self.assertTrue(stdChk.target_model()._evotype == "densitymx")
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.probs(opstr)
p_clifford = mdl_clifford.probs(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_circuitsim_cterm(self):
# Density-matrix simulation (of superoperator gates) using stabilizer-based term calcs
c0 = pygsti.obj.Circuit(layer_labels=(), num_lines=1) # 1-qubit circuit
c1 = pygsti.obj.Circuit(layer_labels=(('Gx',0),), num_lines=1)
c2 = pygsti.obj.Circuit(layer_labels=(('Gx',0),('Gx',0)), num_lines=1)
c3 = pygsti.obj.Circuit(layer_labels=(('Gx',0),('Gx',0),('Gx',0),('Gx',0)), num_lines=1)
mdl = pygsti.construction.build_standard_localnoise_model(
1, ['Gi','Gx','Gy'], sim_type="termorder:1", parameterization="H+S clifford terms", ensure_composed_gates=False)
probs0 = mdl.probs(c0)
probs1 = mdl.probs(c1)
probs2 = mdl.probs(c2)
probs3 = mdl.probs(c3)
self.assert_outcomes(probs0, {('0',): 1.0, ('1',): 0.0} )
self.assert_outcomes(probs1, {('0',): 0.5, ('1',): 0.5} )
self.assert_outcomes(probs2, {('0',): 0.0, ('1',): 1.0} )
self.assert_outcomes(probs3, {('0',): 1.0, ('1',): 0.0} )
if __name__ == "__main__":
unittest.main(verbosity=2)