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testCore.py
512 lines (395 loc) · 31 KB
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testCore.py
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import unittest
import pygsti
from pygsti.construction import std1Q_XYI as std
from pygsti.baseobjs.basis import Basis
from pygsti.objects import Label as L
import numpy as np
from scipy import polyfit
import sys, os
from ..testutils import compare_files, temp_files
from .basecase import AlgorithmsBase
class TestCoreMethods(AlgorithmsBase):
def test_gram(self):
ds = self.ds
#pygsti.construction.generate_fake_data(self.datagen_gateset, self.lgstStrings,
# nSamples=1000, sampleError='none')
rank,evals,target_evals = pygsti.gram_rank_and_evals(ds, self.fiducials, self.fiducials, self.gateset)
print("gram rank = ",rank)
print("gram evals = ",evals)
print("target gram evals = ",target_evals)
with self.assertRaises(ValueError):
pygsti.gram_rank_and_evals(ds, self.fiducials, self.fiducials, None) #no target
def test_LGST(self):
ds = self.ds
#pygsti.construction.generate_fake_data(self.datagen_gateset, self.lgstStrings, nSamples=1000,
# sampleError='binomial', seed=None)
print("GG0 = ",self.gateset.default_gauge_group)
gs_lgst = pygsti.do_lgst(ds, self.fiducials, self.fiducials, self.gateset, svdTruncateTo=4, verbosity=0)
gs_lgst_verb = self.runSilent(pygsti.do_lgst, ds, self.fiducials, self.fiducials, self.gateset, svdTruncateTo=4, verbosity=10)
self.assertAlmostEqual(gs_lgst.frobeniusdist(gs_lgst_verb),0)
print("GG = ",gs_lgst.default_gauge_group)
gs_lgst_go = pygsti.gaugeopt_to_target(gs_lgst,self.gateset, {'spam':1.0, 'gates': 1.0}, checkJac=True)
gs_clgst = pygsti.contract(gs_lgst_go, "CPTP")
# RUN BELOW LINES TO SEED SAVED GATESET FILES
#pygsti.io.write_gateset(gs_lgst,compare_files + "/lgst.gateset", "Saved LGST Gateset before gauge optimization")
#pygsti.io.write_gateset(gs_lgst_go,compare_files + "/lgst_go.gateset", "Saved LGST Gateset after gauge optimization")
#pygsti.io.write_gateset(gs_clgst,compare_files + "/clgst.gateset", "Saved LGST Gateset after G.O. and CPTP contraction")
gs_lgst_compare = pygsti.io.load_gateset(compare_files + "/lgst.gateset")
gs_lgst_go_compare = pygsti.io.load_gateset(compare_files + "/lgst_go.gateset")
gs_clgst_compare = pygsti.io.load_gateset(compare_files + "/clgst.gateset")
self.assertAlmostEqual( gs_lgst.frobeniusdist(gs_lgst_compare), 0, places=5)
self.assertAlmostEqual( gs_lgst_go.frobeniusdist(gs_lgst_go_compare), 0, places=5)
self.assertAlmostEqual( gs_clgst.frobeniusdist(gs_clgst_compare), 0, places=5)
#Check for error conditions
with self.assertRaises(ValueError):
gs_lgst = pygsti.do_lgst(ds, self.fiducials, self.fiducials, None, svdTruncateTo=4, verbosity=0) #no target gateset
with self.assertRaises(ValueError):
gs_lgst = pygsti.do_lgst(ds, self.fiducials, self.fiducials, None, gateLabels=list(self.gateset.gates.keys()),
svdTruncateTo=4, verbosity=0) #no spam dict
#No need for identity vector anymore
#with self.assertRaises(ValueError):
# gs_lgst = pygsti.do_lgst(ds, self.fiducials, self.fiducials, None, gateLabels=list(self.gateset.gates.keys()),
# spamDict=self.gateset.get_reverse_spam_defs(),
# svdTruncateTo=4, verbosity=0) #no identity vector
with self.assertRaises(ValueError):
bad_fids =pygsti.construction.gatestring_list([('Gx',),('Gx',),('Gx',),('Gx',)])
gs_lgst = pygsti.do_lgst(ds, bad_fids, bad_fids, self.gateset, svdTruncateTo=4, verbosity=0) # bad fiducials (rank deficient)
with self.assertRaises(KeyError): # AB-matrix construction error
incomplete_strings = self.lgstStrings[5:] #drop first 5 strings...
bad_ds = pygsti.construction.generate_fake_data(
self.datagen_gateset, incomplete_strings,
nSamples=10, sampleError='none')
gs_lgst = pygsti.do_lgst(bad_ds, self.fiducials, self.fiducials, self.gateset,
svdTruncateTo=4, verbosity=0)
# incomplete dataset
with self.assertRaises(KeyError): # X-matrix construction error
incomplete_strings = self.lgstStrings[:-5] #drop last 5 strings...
bad_ds = pygsti.construction.generate_fake_data(
self.datagen_gateset, incomplete_strings,
nSamples=10, sampleError='none')
gs_lgst = pygsti.do_lgst(bad_ds, self.fiducials, self.fiducials, self.gateset,
svdTruncateTo=4, verbosity=0)
# incomplete dataset
#Deprecated / removed:
#LGST on an "old-style" gateset
#old_style_gateset = pygsti.construction.build_gateset(
# [2], [('Q0',)],['Gi','Gx','Gy'],
# [ "I(Q0)","X(pi/8,Q0)", "Y(pi/8,Q0)"],
# prepLabels=["rho0"], prepExpressions=["0"],
# effectLabels=["E0"], effectExpressions=["0"],
# spamdefs={'0': ('rho0','E0'),
# '1': ('remainder','remainder') } )
#gs_lgst = pygsti.do_lgst(ds, self.fiducials, self.fiducials, old_style_gateset,
# svdTruncateTo=4, verbosity=0)
def test_LGST_no_sample_error(self):
#change rep-count type so dataset can hold fractional counts for sampleError = 'none'
oldType = pygsti.objects.dataset.Repcount_type
pygsti.objects.dataset.Repcount_type = np.float64
ds = pygsti.construction.generate_fake_data(self.datagen_gateset, self.lgstStrings,
nSamples=10000, sampleError='none')
pygsti.objects.dataset.Repcount_type = oldType
gs_lgst = pygsti.do_lgst(ds, self.fiducials, self.fiducials, self.gateset, svdTruncateTo=4, verbosity=0)
print("DATAGEN:")
print(self.datagen_gateset)
print("\nLGST RAW:")
print(gs_lgst)
gs_lgst = pygsti.gaugeopt_to_target(gs_lgst,self.datagen_gateset, {'spam':1.0, 'gates': 1.0}, checkJac=False)
print("\nAfter gauge opt:")
print(gs_lgst)
print(gs_lgst.strdiff(self.datagen_gateset))
self.assertAlmostEqual( gs_lgst.frobeniusdist(self.datagen_gateset), 0, places=4)
def test_eLGST(self):
ds = self.ds
#pygsti.construction.generate_fake_data(self.datagen_gateset, self.lsgstStrings[-1],
# nSamples=1000,sampleError='binomial', seed=100)
assert(pygsti.obj.GateSet._pcheck)
gs_lgst = pygsti.do_lgst(ds, self.fiducials, self.fiducials, self.gateset, svdTruncateTo=4, verbosity=0)
gs_lgst._check_paramvec()
gs_lgst_go = pygsti.gaugeopt_to_target(gs_lgst,self.gateset, {'spam':1.0, 'gates': 1.0}, checkJac=True)
gs_lgst_go._check_paramvec()
gs_clgst = pygsti.contract(gs_lgst_go, "CPTP")
gs_clgst._check_paramvec()
self.gateset._check_paramvec()
_,gs_single_exlgst = pygsti.do_exlgst(ds, gs_clgst, self.elgstStrings[0], self.fiducials, self.fiducials,
self.gateset, regularizeFactor=1e-3, svdTruncateTo=4,
verbosity=0)
gs_single_exlgst._check_paramvec()
_,gs_single_exlgst_verb = self.runSilent(pygsti.do_exlgst, ds, gs_clgst, self.elgstStrings[0], self.fiducials, self.fiducials,
self.gateset, regularizeFactor=1e-3, svdTruncateTo=4,
verbosity=10)
gs_single_exlgst_verb._check_paramvec()
self.assertAlmostEqual(gs_single_exlgst.frobeniusdist(gs_single_exlgst_verb),0)
gs_exlgst = pygsti.do_iterative_exlgst(ds, gs_clgst, self.fiducials, self.fiducials, self.elgstStrings,
targetGateset=self.gateset, svdTruncateTo=4, verbosity=0)
all_minErrs, all_gs_exlgst_tups = pygsti.do_iterative_exlgst(
ds, gs_clgst, self.fiducials, self.fiducials, [ [gs.tup for gs in gsList] for gsList in self.elgstStrings],
targetGateset=self.gateset, svdTruncateTo=4, verbosity=0, returnAll=True, returnErrorVec=True)
gs_exlgst_verb = self.runSilent(pygsti.do_iterative_exlgst, ds, gs_clgst, self.fiducials, self.fiducials, self.elgstStrings,
targetGateset=self.gateset, svdTruncateTo=4, verbosity=10)
gs_exlgst_reg = pygsti.do_iterative_exlgst(ds, gs_clgst, self.fiducials, self.fiducials, self.elgstStrings,
targetGateset=self.gateset, svdTruncateTo=4, verbosity=0,
regularizeFactor=10)
self.assertAlmostEqual(gs_exlgst.frobeniusdist(gs_exlgst_verb),0)
self.assertAlmostEqual(gs_exlgst.frobeniusdist(all_gs_exlgst_tups[-1]),0)
#Run internal checks on less max-L values (so it doesn't take forever)
gs_exlgst_chk = pygsti.do_iterative_exlgst(ds, gs_clgst, self.fiducials, self.fiducials, self.elgstStrings[0:2],
targetGateset=self.gateset, svdTruncateTo=4, verbosity=0,
check_jacobian=True)
gs_exlgst_chk_verb = self.runSilent(pygsti.do_iterative_exlgst,ds, gs_clgst, self.fiducials, self.fiducials, self.elgstStrings[0:2],
targetGateset=self.gateset, svdTruncateTo=4, verbosity=10,
check_jacobian=True)
# RUN BELOW LINES TO SEED SAVED GATESET FILES
#pygsti.io.write_gateset(gs_exlgst,compare_files + "/exlgst.gateset", "Saved Extended-LGST (eLGST) Gateset")
#pygsti.io.write_gateset(gs_exlgst_reg,compare_files + "/exlgst_reg.gateset", "Saved Extended-LGST (eLGST) Gateset w/regularization")
gs_exlgst_compare = pygsti.io.load_gateset(compare_files + "/exlgst.gateset")
gs_exlgst_reg_compare = pygsti.io.load_gateset(compare_files + "/exlgst_reg.gateset")
gs_exlgst.set_all_parameterizations("full") # b/c ex-LGST sets spam to StaticSPAMVec objects (b/c they're not optimized)
gs_exlgst_reg.set_all_parameterizations("full") # b/c ex-LGST sets spam to StaticSPAMVec objects (b/c they're not optimized)
gs_exlgst_go = pygsti.gaugeopt_to_target(gs_exlgst,gs_exlgst_compare, {'spam':1.0 }, checkJac=True)
gs_exlgst_reg_go = pygsti.gaugeopt_to_target(gs_exlgst_reg,gs_exlgst_reg_compare, {'spam':1.0 }, checkJac=True)
#self.assertAlmostEqual( gs_exlgst_go.frobeniusdist(gs_exlgst_compare), 0, places=5)
#self.assertAlmostEqual( gs_exlgst_reg_go.frobeniusdist(gs_exlgst_reg_compare), 0, places=5)
def test_MC2GST(self):
ds = self.ds
#pygsti.construction.generate_fake_data(self.datagen_gateset, self.lsgstStrings[-1],
# nSamples=1000, sampleError='binomial', seed=100)
gs_lgst = pygsti.do_lgst(ds, self.fiducials, self.fiducials, self.gateset, svdTruncateTo=4, verbosity=0)
gs_lgst_go = pygsti.gaugeopt_to_target(gs_lgst,self.gateset, {'spam':1.0, 'gates': 1.0}, checkJac=True)
gs_clgst = pygsti.contract(gs_lgst_go, "CPTP")
CM = pygsti.baseobjs.profiler._get_mem_usage()
gs_single_lsgst = pygsti.do_mc2gst(ds, gs_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-4,
probClipInterval=(-1e6,1e6), regularizeFactor=1e-3,
verbosity=0) #uses regularizeFactor
gs_single_lsgst_cp = pygsti.do_mc2gst(ds, gs_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-4,
probClipInterval=(-1e6,1e6), cptp_penalty_factor=1.0,
verbosity=0) #uses cptp_penalty_factor
gs_single_lsgst_sp = pygsti.do_mc2gst(ds, gs_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-4,
probClipInterval=(-1e6,1e6), spam_penalty_factor=1.0,
verbosity=0) #uses spam_penalty_factor
gs_single_lsgst_cpsp = pygsti.do_mc2gst(ds, gs_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-4,
probClipInterval=(-1e6,1e6), cptp_penalty_factor=1.0,
spam_penalty_factor=1.0, verbosity=0) #uses both penalty factors
gs_single_lsgst_cpsp = self.runSilent(pygsti.do_mc2gst, ds, gs_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-4,
probClipInterval=(-1e6,1e6), cptp_penalty_factor=1.0,
spam_penalty_factor=1.0, verbosity=10) #uses both penalty factors w/verbosity high
gs_single_lsgst_cp = self.runSilent(pygsti.do_mc2gst, ds, gs_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-4,
probClipInterval=(-1e6,1e6), cptp_penalty_factor=1.0,
verbosity=10) #uses cptp_penalty_factor w/verbosity high
gs_single_lsgst_sp = self.runSilent(pygsti.do_mc2gst, ds, gs_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-4,
probClipInterval=(-1e6,1e6), spam_penalty_factor=1.0,
verbosity=10) #uses spam_penalty_factor w/verbosity high
gs_lsgst = pygsti.do_iterative_mc2gst(ds, gs_clgst, self.lsgstStrings, verbosity=0,
minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6),
memLimit=CM + 1024**3)
all_minErrs, all_gs_lsgst_tups = pygsti.do_iterative_mc2gst(
ds, gs_clgst, [ [gs.tup for gs in gsList] for gsList in self.lsgstStrings],
minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6), returnAll=True, returnErrorVec=True)
gs_lsgst_verb = self.runSilent(pygsti.do_iterative_mc2gst, ds, gs_clgst, self.lsgstStrings, verbosity=10,
minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6),
memLimit=CM + 1024**3)
gs_lsgst_reg = self.runSilent(pygsti.do_iterative_mc2gst,ds, gs_clgst,
self.lsgstStrings, verbosity=10,
minProbClipForWeighting=1e-6,
probClipInterval=(-1e6,1e6),
regularizeFactor=10, memLimit=CM + 1024**3)
self.assertAlmostEqual(gs_lsgst.frobeniusdist(gs_lsgst_verb),0)
self.assertAlmostEqual(gs_lsgst.frobeniusdist(all_gs_lsgst_tups[-1]),0)
#Run internal checks on less max-L values (so it doesn't take forever)
gs_lsgst_chk = pygsti.do_iterative_mc2gst(ds, gs_clgst, self.lsgstStrings[0:2], verbosity=0,
minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6),
check=True, check_jacobian=True)
gs_lsgst_chk_verb = self.runSilent(pygsti.do_iterative_mc2gst, ds, gs_clgst, self.lsgstStrings[0:2], verbosity=10,
minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6),
check=True, check_jacobian=True, memLimit=CM + 1024**3)
#Other option variations - just make sure they run at this point
gs_lsgst_chk_opts = pygsti.do_iterative_mc2gst(ds, gs_clgst, self.lsgstStrings[0:2], verbosity=0,
minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6),
useFreqWeightedChiSq=True, gateStringSetLabels=["Set1","Set2"],
gatestringWeightsDict={ ('Gx',): 2.0 } )
aliased_list = [ pygsti.obj.GateString( [ (x if x != L("Gx") else L("GA1")) for x in gs]) for gs in self.lsgstStrings[0] ]
gs_withA1 = gs_clgst.copy(); gs_withA1.gates["GA1"] = gs_clgst.gates["Gx"]
del gs_withA1.gates["Gx"] # otherwise gs_withA1 will have Gx params that we have no knowledge of!
gs_lsgst_chk_opts2 = pygsti.do_mc2gst(ds, gs_withA1, aliased_list, minProbClipForWeighting=1e-6,
probClipInterval=(-1e2,1e2), verbosity=10,
gateLabelAliases={ L('GA1'): (L('Gx'),) })
#Check with small but ok memlimit -- not anymore since new mem estimation uses current memory, making this non-robust
#self.runSilent(pygsti.do_mc2gst,ds, gs_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-6,
# probClipInterval=(-1e6,1e6), regularizeFactor=1e-3,
# verbosity=10, memLimit=CM + 1024**3)
#Check errors:
with self.assertRaises(MemoryError):
pygsti.do_mc2gst(ds, gs_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-6,
probClipInterval=(-1e6,1e6), regularizeFactor=1e-3,
verbosity=0, memLimit=1)
with self.assertRaises(AssertionError):
pygsti.do_mc2gst(ds, gs_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-6,
probClipInterval=(-1e6,1e6), regularizeFactor=1e-3,
verbosity=0, cptp_penalty_factor=1.0) #can't specify both cptp_penalty_factor and regularizeFactor
# RUN BELOW LINES TO SEED SAVED GATESET FILES
#pygsti.io.write_gateset(gs_lsgst,compare_files + "/lsgst.gateset", "Saved LSGST Gateset")
#pygsti.io.write_gateset(gs_lsgst_reg,compare_files + "/lsgst_reg.gateset", "Saved LSGST Gateset w/Regularization")
gs_lsgst_compare = pygsti.io.load_gateset(compare_files + "/lsgst.gateset")
gs_lsgst_reg_compare = pygsti.io.load_gateset(compare_files + "/lsgst_reg.gateset")
gs_lsgst_go = pygsti.gaugeopt_to_target(gs_lsgst, gs_lsgst_compare, {'spam':1.0}, checkJac=True)
gs_lsgst_reg_go = pygsti.gaugeopt_to_target(gs_lsgst_reg, gs_lsgst_reg_compare, {'spam':1.0}, checkJac=True)
self.assertAlmostEqual( gs_lsgst_go.frobeniusdist(gs_lsgst_compare), 0, places=4)
self.assertAlmostEqual( gs_lsgst_reg_go.frobeniusdist(gs_lsgst_reg_compare), 0, places=4)
# RUN BELOW LINES TO SEED SAVED GATESET FILES
#gs_lsgst_go = pygsti.gaugeopt_to_target(gs_lsgst, self.gateset, {'spam':1.0})
#pygsti.io.write_gateset(gs_lsgst_go,compare_files + "/analysis.gateset", "Saved LSGST Analysis Gateset")
#print("DEBUG: analysis.gateset = "); print(gs_lgst_go)
def test_MLGST(self):
ds = self.ds
#pygsti.construction.generate_fake_data(self.datagen_gateset, self.lsgstStrings[-1],
# nSamples=1000, sampleError='binomial', seed=100)
gs_lgst = pygsti.do_lgst(ds, self.fiducials, self.fiducials, self.gateset, svdTruncateTo=4, verbosity=0)
gs_lgst_go = pygsti.gaugeopt_to_target(gs_lgst,self.gateset, {'spam':1.0, 'gates': 1.0}, checkJac=True)
gs_clgst = pygsti.contract(gs_lgst_go, "CPTP")
gs_clgst = gs_clgst.depolarize(gate_noise=0.02, spam_noise=0.02) # just to avoid infinity objective funct & jacs below
CM = pygsti.baseobjs.profiler._get_mem_usage()
gs_single_mlgst = pygsti.do_mlgst(ds, gs_clgst, self.lsgstStrings[0], minProbClip=1e-4,
probClipInterval=(-1e2,1e2), verbosity=0)
#this test often gives an assetion error "finite Jacobian has inf norm!" on Travis CI Python 3 case
try:
gs_single_mlgst_cpsp = pygsti.do_mlgst(ds, gs_clgst, self.lsgstStrings[0], minProbClip=1e-4,
probClipInterval=(-1e2,1e2), cptp_penalty_factor=1.0,
spam_penalty_factor=1.0, verbosity=10) #uses both penalty factors w/verbosity > 0
except ValueError: pass # ignore when assertions in customlm.py are disabled
except AssertionError:
pass # just ignore for now. FUTURE: see what we can do in custom LM about scaling large jacobians...
try:
gs_single_mlgst_cp = pygsti.do_mlgst(ds, gs_clgst, self.lsgstStrings[0], minProbClip=1e-4,
probClipInterval=(-1e2,1e2), cptp_penalty_factor=1.0,
verbosity=10)
except ValueError: pass # ignore when assertions in customlm.py are disabled
except AssertionError:
pass # just ignore for now. FUTURE: see what we can do in custom LM about scaling large jacobians...
try:
gs_single_mlgst_sp = pygsti.do_mlgst(ds, gs_clgst, self.lsgstStrings[0], minProbClip=1e-4,
probClipInterval=(-1e2,1e2), spam_penalty_factor=1.0,
verbosity=10)
except ValueError: pass # ignore when assertions in customlm.py are disabled
except AssertionError:
pass # just ignore for now. FUTURE: see what we can do in custom LM about scaling large jacobians...
gs_mlegst = pygsti.do_iterative_mlgst(ds, gs_clgst, self.lsgstStrings, verbosity=0,
minProbClip=1e-4, probClipInterval=(-1e2,1e2),
memLimit=CM + 1024**3)
maxLogL, all_gs_mlegst_tups = pygsti.do_iterative_mlgst(
ds, gs_clgst, [ [gs.tup for gs in gsList] for gsList in self.lsgstStrings],
minProbClip=1e-4, probClipInterval=(-1e2,1e2), returnAll=True, returnMaxLogL=True)
gs_mlegst_verb = self.runSilent(pygsti.do_iterative_mlgst, ds, gs_clgst, self.lsgstStrings, verbosity=10,
minProbClip=1e-4, probClipInterval=(-1e2,1e2),
memLimit=CM + 1024**3)
self.assertAlmostEqual(gs_mlegst.frobeniusdist(gs_mlegst_verb),0, places=5)
self.assertAlmostEqual(gs_mlegst.frobeniusdist(all_gs_mlegst_tups[-1]),0,places=5)
#Run internal checks on less max-L values (so it doesn't take forever)
gs_mlegst_chk = pygsti.do_iterative_mlgst(ds, gs_clgst, self.lsgstStrings[0:2], verbosity=0,
minProbClip=1e-4, probClipInterval=(-1e2,1e2),
check=True)
#Other option variations - just make sure they run at this point
gs_mlegst_chk_opts = pygsti.do_iterative_mlgst(ds, gs_clgst, self.lsgstStrings[0:2], verbosity=0,
minProbClip=1e-4, probClipInterval=(-1e2,1e2),
gateStringSetLabels=["Set1","Set2"], useFreqWeightedChiSq=True,
gatestringWeightsDict={ (L('Gx'),): 2.0 } )
aliased_list = [ pygsti.obj.GateString( [ (x if x != L("Gx") else L("GA1")) for x in gs]) for gs in self.lsgstStrings[0] ]
gs_withA1 = gs_clgst.copy(); gs_withA1.gates["GA1"] = gs_clgst.gates["Gx"]
del gs_withA1.gates["Gx"] # otherwise gs_withA1 will have Gx params that we have no knowledge of!
gs_mlegst_chk_opts2 = pygsti.do_mlgst(ds, gs_withA1, aliased_list, minProbClip=1e-4,
probClipInterval=(-1e2,1e2), verbosity=10,
gateLabelAliases={ L('GA1'): (L('Gx'),) })
#Other option variations - just make sure they run at this point
gs_mlegst_chk_opts3 = pygsti.do_iterative_mlgst(ds, gs_clgst, self.lsgstStrings[0:2], verbosity=0,
minProbClip=1e-4, probClipInterval=(-1e2,1e2),
gateStringSetLabels=["Set1","Set2"], useFreqWeightedChiSq=True,
gatestringWeightsDict={ (L('Gx'),): 2.0 }, alwaysPerformMLE=True )
#Forcing function used by linear response error bars
forcingfn_grad = np.ones((1,gs_clgst.num_params()), 'd')
gs_lsgst_chk_opts3 = pygsti.algorithms.core._do_mlgst_base(
ds, gs_clgst, self.lsgstStrings[0], verbosity=0,
minProbClip=1e-4, probClipInterval=(-1e2,1e2),
forcefn_grad=forcingfn_grad)
gs_lsgst_chk_opts4 = pygsti.algorithms.core._do_mlgst_base(
ds, gs_clgst, self.lsgstStrings[0], verbosity=0, poissonPicture=False,
minProbClip=1e-4, probClipInterval=(-1e2,1e2),
forcefn_grad=forcingfn_grad) # non-poisson picture
#Check with small but ok memlimit -- not anymore since new mem estimation uses current memory, making this non-robust
#self.runSilent(pygsti.do_mlgst, ds, gs_clgst, self.lsgstStrings[0], minProbClip=1e-6,
# probClipInterval=(-1e2,1e2), verbosity=4, memLimit=curMem+8500000) #invoke memory control
#non-Poisson picture - should use (-1,-1) gateset for consistency?
pygsti.do_mlgst(ds, gs_clgst, self.lsgstStrings[0], minProbClip=1e-4,
probClipInterval=(-1e2,1e2), verbosity=0, poissonPicture=False)
try:
pygsti.do_mlgst(ds, gs_clgst, self.lsgstStrings[0], minProbClip=1e-1, # 1e-1 b/c get inf Jacobians...
probClipInterval=(-1e2,1e2), verbosity=0, poissonPicture=False,
spam_penalty_factor=1.0, cptp_penalty_factor=1.0)
except ValueError: pass # ignore when assertions in customlm.py are disabled
except AssertionError:
pass # just ignore for now. FUTURE: see what we can do in custom LM about scaling large jacobians...
#Check errors:
with self.assertRaises(MemoryError):
pygsti.do_mlgst(ds, gs_clgst, self.lsgstStrings[0], minProbClip=1e-4,
probClipInterval=(-1e2,1e2),verbosity=0, memLimit=1)
# RUN BELOW LINES TO SEED SAVED GATESET FILES
#pygsti.io.write_gateset(gs_mlegst,compare_files + "/mle_gst.gateset", "Saved MLE-GST Gateset")
gs_mle_compare = pygsti.io.load_gateset(compare_files + "/mle_gst.gateset")
gs_mlegst_go = pygsti.gaugeopt_to_target(gs_mlegst, gs_mle_compare, {'spam':1.0}, checkJac=True)
self.assertAlmostEqual( gs_mlegst_go.frobeniusdist(gs_mle_compare), 0, places=4)
def test_LGST_1overSqrtN_dependence(self):
my_datagen_gateset = self.gateset.depolarize(gate_noise=0.05, spam_noise=0)
# !!don't depolarize spam or 1/sqrt(N) dependence saturates!!
nSamplesList = np.array([ 16, 128, 1024, 8192 ])
diffs = []
for nSamples in nSamplesList:
ds = pygsti.construction.generate_fake_data(my_datagen_gateset, self.lgstStrings, nSamples,
sampleError='binomial', seed=100)
gs_lgst = pygsti.do_lgst(ds, self.fiducials, self.fiducials, self.gateset, svdTruncateTo=4, verbosity=0)
gs_lgst_go = pygsti.gaugeopt_to_target(gs_lgst, my_datagen_gateset, {'spam':1.0, 'gate': 1.0}, checkJac=True)
diffs.append( my_datagen_gateset.frobeniusdist(gs_lgst_go) )
diffs = np.array(diffs, 'd')
a, b = polyfit(np.log10(nSamplesList), np.log10(diffs), deg=1)
#print "\n",nSamplesList; print diffs; print a #DEBUG
self.assertLess( a+0.5, 0.05 )
def test_model_selection(self):
ds = self.ds
#pygsti.construction.generate_fake_data(self.datagen_gateset, self.lsgstStrings[-1],
# nSamples=1000,sampleError='binomial', seed=100)
gs_lgst4 = pygsti.do_lgst(ds, self.fiducials, self.fiducials, self.gateset, svdTruncateTo=4, verbosity=0)
gs_lgst6 = pygsti.do_lgst(ds, self.fiducials, self.fiducials, self.gateset, svdTruncateTo=6, verbosity=0)
sys.stdout.flush()
self.runSilent(pygsti.do_lgst, ds, self.fiducials, self.fiducials, self.gateset, svdTruncateTo=6, verbosity=4) # test verbose prints
chiSq4 = pygsti.chi2(gs_lgst4, ds, self.lgstStrings, minProbClipForWeighting=1e-4)
chiSq6 = pygsti.chi2(gs_lgst6, ds, self.lgstStrings, minProbClipForWeighting=1e-4)
print("LGST dim=4 chiSq = ",chiSq4)
print("LGST dim=6 chiSq = ",chiSq6)
#self.assertAlmostEqual(chiSq4, 174.061524953) #429.271983052)
#self.assertAlmostEqual(chiSq6, 267012993.861, places=1) #1337.74222467) #Why is this so large??? -- DEBUG later
# Least squares GST with model selection
gs_lsgst = self.runSilent(pygsti.do_iterative_mc2gst_with_model_selection, ds, gs_lgst4, 1, self.lsgstStrings[0:3],
verbosity=10, minProbClipForWeighting=1e-3, probClipInterval=(-1e5,1e5))
# Run again with other parameters
tuple_strings = [ list(map(tuple, gsList)) for gsList in self.lsgstStrings[0:3] ] #to test tuple argument
errorVecs, gs_lsgst_wts = self.runSilent(pygsti.do_iterative_mc2gst_with_model_selection, ds, gs_lgst4,
1, tuple_strings, verbosity=10, minProbClipForWeighting=1e-3,
probClipInterval=(-1e5,1e5), gatestringWeightsDict={ ('Gx',): 2.0 },
returnAll=True, returnErrorVec=True)
# Do non-iterative to cover GateString->tuple conversion
gs_non_iterative = self.runSilent( pygsti.do_mc2gst_with_model_selection, ds,
gs_lgst4, 1, self.lsgstStrings[0],
verbosity=10, probClipInterval=(-1e5,1e5) )
# RUN BELOW LINES TO SEED SAVED GATESET FILES
#pygsti.io.write_gateset(gs_lsgst,compare_files + "/lsgstMS.gateset", "Saved LSGST Gateset with model selection")
gs_lsgst_compare = pygsti.io.load_gateset(compare_files + "/lsgstMS.gateset")
gs_lsgst_go = pygsti.gaugeopt_to_target(gs_lsgst, gs_lsgst_compare, {'spam':1.0}, checkJac=True)
self.assertAlmostEqual( gs_lsgst_go.frobeniusdist(gs_lsgst_compare), 0, places=4)
def test_miscellaneous(self):
ds = self.ds
#pygsti.construction.generate_fake_data(self.datagen_gateset, self.lgstStrings,
# nSamples=1000, sampleError='none')
strs = pygsti.construction.list_strings_lgst_can_estimate(ds, self.fiducials, self.fiducials)
self.runSilent(self.gateset.print_info) #just make sure it works
#test boundary case:
gate2Q = np.identity(16,'d')
with self.assertRaises(ValueError):
pygsti.alg.find_closest_unitary_gatemx(gate2Q) #doesn't work for > 1 qubits
if __name__ == "__main__":
unittest.main(verbosity=2)