def test_eLGST(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.specs, self.gateset, svdTruncateTo=4, verbosity=0) gs_lgst_go = pygsti.optimize_gauge(gs_lgst,"target",targetGateset=self.gateset, spamWeight=1.0, gateWeight=1.0) gs_clgst = pygsti.contract(gs_lgst_go, "CPTP") gs_single_exlgst = pygsti.do_exlgst(ds, gs_clgst, self.elgstStrings[0], self.specs, self.gateset, regularizeFactor=1e-3, svdTruncateTo=4, verbosity=0) gs_single_exlgst_verb = self.runSilent(pygsti.do_exlgst, ds, gs_clgst, self.elgstStrings[0], self.specs, self.gateset, regularizeFactor=1e-3, svdTruncateTo=4, verbosity=10) gs_exlgst = pygsti.do_iterative_exlgst(ds, gs_clgst, self.specs, self.elgstStrings, targetGateset=self.gateset, svdTruncateTo=4, verbosity=0) all_minErrs, all_gs_exlgst_tups = pygsti.do_iterative_exlgst( ds, gs_clgst, self.specs, [ [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.specs, self.elgstStrings, targetGateset=self.gateset, svdTruncateTo=4, verbosity=10) gs_exlgst_reg = pygsti.do_iterative_exlgst(ds, gs_clgst, self.specs, 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.specs, 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.specs, 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_go = pygsti.optimize_gauge(gs_exlgst, 'target', targetGateset=gs_exlgst_compare, spamWeight=1.0) gs_exlgst_reg_go = pygsti.optimize_gauge(gs_exlgst_reg, 'target', targetGateset=gs_exlgst_reg_compare, spamWeight=1.0) 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_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.specs, self.gateset, svdTruncateTo=4, verbosity=0) gs_lgst_go = pygsti.optimize_gauge( gs_lgst, "target", targetGateset=my_datagen_gateset, spamWeight=1.0, gateWeight=1.0) 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 runMC2GSTAnalysis(myspecs, mygerms, gsTarget, seed, maxLs=[1, 2, 4, 8], nSamples=1000, useFreqWeightedChiSq=False, minProbClipForWeighting=1e-4, fidPairList=None, comm=None): rhoStrs, EStrs = pygsti.construction.get_spam_strs(myspecs) lgstStrings = pygsti.construction.list_lgst_gatestrings( myspecs, gsTarget.gates.keys()) lsgstStrings = pygsti.construction.make_lsgst_lists( gsTarget.gates.keys(), rhoStrs, EStrs, mygerms, maxLs, fidPairList) print len(myspecs[0]), " rho specifiers" print len(myspecs[1]), " effect specifiers" print len(mygerms), " germs" print len(lgstStrings), " total LGST gate strings" print len(lsgstStrings[-1]), " LSGST strings before thinning" lsgstStringsToUse = lsgstStrings allRequiredStrs = pygsti.remove_duplicates(lgstStrings + lsgstStrings[-1]) gs_dataGen = gsTarget.depolarize(gate_noise=0.1) dsFake = pygsti.construction.generate_fake_data(gs_dataGen, allRequiredStrs, nSamples, sampleError="multinomial", seed=seed) #Run LGST to get starting gate set gs_lgst = pygsti.do_lgst(dsFake, myspecs, gsTarget, svdTruncateTo=gsTarget.dim, verbosity=3) gs_lgst_go = pygsti.optimize_gauge(gs_lgst, "target", targetGateset=gs_dataGen) #Run full iterative LSGST tStart = time.time() all_gs_lsgst = pygsti.do_iterative_mc2gst( dsFake, gs_lgst_go, lsgstStringsToUse, minProbClipForWeighting=minProbClipForWeighting, probClipInterval=(-1e5, 1e5), verbosity=1, memLimit=3 * (1024)**3, returnAll=True, useFreqWeightedChiSq=useFreqWeightedChiSq, comm=comm) tEnd = time.time() print "Time = ", (tEnd - tStart) / 3600.0, "hours ( =", (tEnd - tStart), " secs)" return all_gs_lsgst, gs_dataGen
def runAnalysis(obj, myspecs, mygerms, gsTarget, seed, maxLs = [1,2,4,8], nSamples=1000, useFreqWeightedChiSq=False, minProbClipForWeighting=1e-4, fidPairList=None, comm=None, distributeMethod="gatestrings"): rhoStrs, EStrs = pygsti.construction.get_spam_strs(myspecs) lgstStrings = pygsti.construction.list_lgst_gatestrings( myspecs, gsTarget.gates.keys()) lsgstStrings = pygsti.construction.make_lsgst_lists( gsTarget.gates.keys(), rhoStrs, EStrs, mygerms, maxLs, fidPairList ) print len(myspecs[0]), " rho specifiers" print len(myspecs[1]), " effect specifiers" print len(mygerms), " germs" print len(lgstStrings), " total LGST gate strings" print len(lsgstStrings[-1]), " LSGST strings before thinning" lsgstStringsToUse = lsgstStrings allRequiredStrs = pygsti.remove_duplicates(lgstStrings + lsgstStrings[-1]) gs_dataGen = gsTarget.depolarize(gate_noise=0.1) dsFake = pygsti.construction.generate_fake_data( gs_dataGen, allRequiredStrs, nSamples, sampleError="multinomial", seed=seed) #Run LGST to get starting gate set gs_lgst = pygsti.do_lgst(dsFake, myspecs, gsTarget, svdTruncateTo=gsTarget.dim, verbosity=3) gs_lgst_go = pygsti.optimize_gauge(gs_lgst,"target", targetGateset=gs_dataGen) #Run full iterative LSGST tStart = time.time() if obj == "chi2": all_gs_lsgst = pygsti.do_iterative_mc2gst( dsFake, gs_lgst_go, lsgstStringsToUse, minProbClipForWeighting=minProbClipForWeighting, probClipInterval=(-1e5,1e5), verbosity=1, memLimit=3*(1024)**3, returnAll=True, useFreqWeightedChiSq=useFreqWeightedChiSq, comm=comm, distributeMethod=distributeMethod) elif obj == "logl": all_gs_lsgst = pygsti.do_iterative_mlgst( dsFake, gs_lgst_go, lsgstStringsToUse, minProbClip=minProbClipForWeighting, probClipInterval=(-1e5,1e5), verbosity=1, memLimit=3*(1024)**3, returnAll=True, useFreqWeightedChiSq=useFreqWeightedChiSq, comm=comm, distributeMethod=distributeMethod) tEnd = time.time() print "Time = ",(tEnd-tStart)/3600.0,"hours" return all_gs_lsgst, gs_dataGen
def test_MPI_derivcols(comm): #Individual processors my1ProcResults = runOneQubit("chi2") #Using all processors myManyProcResults = runOneQubit("chi2",comm,"deriv") #compare on root proc if comm.Get_rank() == 0: for gs1,gs2 in zip(my1ProcResults,myManyProcResults): gs2_go = pygsti.optimize_gauge(gs2, "target", targetGateset=gs1, gateWeight=1.0, spamWeight=1.0) print "Frobenius distance = ", gs1.frobeniusdist(gs2_go) assert(gs1.frobeniusdist(gs2_go) < 1e-5) return
def test_LGST_no_sample_error(self): ds = pygsti.construction.generate_fake_data(self.datagen_gateset, self.lgstStrings, nSamples=1000, sampleError='none') gs_lgst = pygsti.do_lgst(ds, self.specs, self.gateset, svdTruncateTo=4, verbosity=0) gs_lgst = pygsti.optimize_gauge(gs_lgst, "target", targetGateset=self.datagen_gateset, gateWeight=1.0, spamWeight=1.0) self.assertAlmostEqual(gs_lgst.frobeniusdist(self.datagen_gateset), 0)
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.specs, self.gateset, svdTruncateTo=4, verbosity=0) gs_lgst_go = pygsti.optimize_gauge(gs_lgst,"target",targetGateset=my_datagen_gateset, spamWeight=1.0, gateWeight=1.0) 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_MPI_derivcols(comm): #Individual processors my1ProcResults = runOneQubit("chi2") #Using all processors myManyProcResults = runOneQubit("chi2", comm, "deriv") #compare on root proc if comm.Get_rank() == 0: for gs1, gs2 in zip(my1ProcResults, myManyProcResults): gs2_go = pygsti.optimize_gauge(gs2, "target", targetGateset=gs1, gateWeight=1.0, spamWeight=1.0) print "Frobenius distance = ", gs1.frobeniusdist(gs2_go) assert (gs1.frobeniusdist(gs2_go) < 1e-5) return
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.specs, self.gateset, svdTruncateTo=4, verbosity=0) gs_lgst6 = pygsti.do_lgst(ds, self.specs, self.gateset, svdTruncateTo=6, verbosity=0) sys.stdout.flush() self.runSilent(pygsti.do_lgst, ds, self.specs, self.gateset, svdTruncateTo=6, verbosity=4) # test verbose prints chiSq4 = pygsti.chi2(ds, gs_lgst4, self.lgstStrings, minProbClipForWeighting=1e-4) chiSq6 = pygsti.chi2(ds, gs_lgst6, 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.optimize_gauge(gs_lsgst, 'target', targetGateset=gs_lsgst_compare, spamWeight=1.0) self.assertAlmostEqual( gs_lsgst_go.frobeniusdist(gs_lsgst_compare), 0, places=5)
def test_MPI_derivcols(comm): #Create dataset for serial and parallel runs ds,lsgstStrings = create_fake_dataset(comm) #Individual processors my1ProcResults = runOneQubit("chi2",ds,lsgstStrings) #Using all processors myManyProcResults = runOneQubit("chi2",ds,lsgstStrings,comm,"deriv") for i,(gs1,gs2) in enumerate(zip(my1ProcResults,myManyProcResults)): assertGatesetsInSync(gs1, comm) assertGatesetsInSync(gs2, comm) gs2_go = pygsti.optimize_gauge(gs2, "target", targetGateset=gs1, gateWeight=1.0, spamWeight=1.0) print("Frobenius distance %d (rank %d) = " % (i,comm.Get_rank()), gs1.frobeniusdist(gs2_go)) if gs1.frobeniusdist(gs2_go) >= 1e-5: print("DIFF (%d) = " % comm.Get_rank(), gs1.strdiff(gs2_go)) assert(gs1.frobeniusdist(gs2_go) < 1e-5) return
def setUp(self): #Set GateSet objects to "strict" mode for testing pygsti.objects.GateSet._strict = True self.targetGateset = std.gs_target datagen_gateset = self.targetGateset.depolarize(gate_noise=0.05, spam_noise=0.1) self.fiducials = std.fiducials self.germs = std.germs self.specs = pygsti.construction.build_spam_specs(self.fiducials, effect_labels=['E0']) #only use the first EVec self.gateLabels = self.targetGateset.gates.keys() # also == std.gates self.lgstStrings = pygsti.construction.list_lgst_gatestrings(self.specs, self.gateLabels) self.maxLengthList = [0,1,2,4,8] self.lsgstStrings = pygsti.construction.make_lsgst_lists( self.gateLabels, self.fiducials, self.fiducials, self.germs, self.maxLengthList) self.ds = pygsti.objects.DataSet(fileToLoadFrom="cmp_chk_files/reportgen.dataset") # RUN BELOW LINES TO GENERATE ANALYSIS DATASET #ds = pygsti.construction.generate_fake_data(datagen_gateset, lsgstStrings[-1], nSamples=1000, # sampleError='binomial', seed=100) #ds.save("cmp_chk_files/reportgen.dataset") gs_lgst = pygsti.do_lgst(self.ds, self.specs, self.targetGateset, svdTruncateTo=4, verbosity=0) gs_lgst_go = pygsti.optimize_gauge(gs_lgst,"target",targetGateset=self.targetGateset) self.gs_clgst = pygsti.contract(gs_lgst_go, "CPTP") self.gs_clgst_tp = pygsti.contract(self.gs_clgst, "vSPAM") self.gs_clgst_tp.set_all_parameterizations("TP") try: import pptx self.have_python_pptx = True except ImportError: warnings.warn("**** IMPORT: Cannot import pptx (python-pptx), and so" + " Powerpoint slide generation tests have been disabled.") self.have_python_pptx = False
def runAnalysis(obj, ds, myspecs, gsTarget, lsgstStringsToUse, useFreqWeightedChiSq=False, minProbClipForWeighting=1e-4, fidPairList=None, comm=None, distributeMethod="gatestrings"): #Run LGST to get starting gate set assertGatesetsInSync(gsTarget, comm) gs_lgst = pygsti.do_lgst(ds, myspecs, gsTarget, svdTruncateTo=gsTarget.dim, verbosity=3) assertGatesetsInSync(gs_lgst, comm) gs_lgst_go = pygsti.optimize_gauge(gs_lgst,"target", targetGateset=gsTarget) assertGatesetsInSync(gs_lgst_go, comm) #Run full iterative LSGST tStart = time.time() if obj == "chi2": all_gs_lsgst = pygsti.do_iterative_mc2gst( ds, gs_lgst_go, lsgstStringsToUse, minProbClipForWeighting=minProbClipForWeighting, probClipInterval=(-1e5,1e5), verbosity=1, memLimit=3*(1024)**3, returnAll=True, useFreqWeightedChiSq=useFreqWeightedChiSq, comm=comm, distributeMethod=distributeMethod) elif obj == "logl": all_gs_lsgst = pygsti.do_iterative_mlgst( ds, gs_lgst_go, lsgstStringsToUse, minProbClip=minProbClipForWeighting, probClipInterval=(-1e5,1e5), verbosity=1, memLimit=3*(1024)**3, returnAll=True, useFreqWeightedChiSq=useFreqWeightedChiSq, comm=comm, distributeMethod=distributeMethod) tEnd = time.time() print("Time = ",(tEnd-tStart)/3600.0,"hours") return all_gs_lsgst
def test_LGST(self): ds = self.ds #pygsti.construction.generate_fake_data(self.datagen_gateset, self.lgstStrings, nSamples=1000, # sampleError='binomial', seed=None) gs_lgst = pygsti.do_lgst(ds, self.specs, self.gateset, svdTruncateTo=4, verbosity=0) gs_lgst_verb = self.runSilent(pygsti.do_lgst, ds, self.specs, self.gateset, svdTruncateTo=4, verbosity=10) self.assertAlmostEqual(gs_lgst.frobeniusdist(gs_lgst_verb),0) gs_lgst_go = pygsti.optimize_gauge(gs_lgst,"target",targetGateset=self.gateset, spamWeight=1.0, gateWeight=1.0) 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) self.assertAlmostEqual( gs_lgst_go.frobeniusdist(gs_lgst_go_compare), 0) self.assertAlmostEqual( gs_clgst.frobeniusdist(gs_clgst_compare), 0) #Check for error conditions with self.assertRaises(ValueError): gs_lgst = pygsti.do_lgst(ds, self.specs, None, svdTruncateTo=4, verbosity=0) #no gate labels with self.assertRaises(ValueError): gs_lgst = pygsti.do_lgst(ds, self.specs, None, gateLabels=list(self.gateset.gates.keys()), svdTruncateTo=4, verbosity=0) #no spam dict with self.assertRaises(ValueError): gs_lgst = pygsti.do_lgst(ds, self.specs, 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_specs = pygsti.construction.build_spam_specs( pygsti.construction.gatestring_list([('Gx',),('Gx',),('Gx',),('Gx',)]), effect_labels=['E0']) gs_lgst = pygsti.do_lgst(ds, bad_specs, self.gateset, svdTruncateTo=4, verbosity=0) # bad specs (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.specs, 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.specs, self.gateset, svdTruncateTo=4, verbosity=0)
def test_gaugeopt_and_contract(self): ds = self.ds_lgst #pygsti.construction.generate_fake_data(self.datagen_gateset, self.lgstStrings, # nSamples=10000,sampleError='binomial', seed=100) gs_lgst = pygsti.do_lgst(ds, self.specs, self.gateset, svdTruncateTo=4, verbosity=0) #Gauge Opt to Target gs_lgst_target = self.runSilent(pygsti.optimize_gauge, gs_lgst,"target",targetGateset=self.gateset,verbosity=10) #Gauge Opt to Target using non-frobenius metrics gs_lgst_targetAlt = self.runSilent(pygsti.optimize_gauge, gs_lgst_target,"target",targetGateset=self.gateset, targetGatesMetric='fidelity', verbosity=10) gs_lgst_targetAlt = self.runSilent(pygsti.optimize_gauge, gs_lgst_target,"target",targetGateset=self.gateset, targetGatesMetric='tracedist', verbosity=10) gs_lgst_targetAlt = self.runSilent(pygsti.optimize_gauge, gs_lgst_target,"target",targetGateset=self.gateset, targetSpamMetric='fidelity', verbosity=10) gs_lgst_targetAlt = self.runSilent(pygsti.optimize_gauge, gs_lgst_target,"target",targetGateset=self.gateset, targetSpamMetric='tracedist', verbosity=10) with self.assertRaises(ValueError): self.runSilent(pygsti.optimize_gauge, gs_lgst_target,"target",targetGateset=self.gateset, targetGatesMetric='foobar', verbosity=10) #bad targetGatesMetric with self.assertRaises(ValueError): self.runSilent(pygsti.optimize_gauge, gs_lgst_target,"target",targetGateset=self.gateset, targetSpamMetric='foobar', verbosity=10) #bad targetSpamMetric with self.assertRaises(ValueError): self.runSilent(pygsti.optimize_gauge, gs_lgst_target,"foobar",targetGateset=self.gateset, targetSpamMetric='target', verbosity=10) #bad toGetTo #Contractions gs_clgst_tp = self.runSilent(pygsti.contract, gs_lgst_target, "TP",verbosity=10, tol=10.0) gs_clgst_cp = self.runSilent(pygsti.contract, gs_lgst_target, "CP",verbosity=10, tol=10.0) gs_clgst_cptp = self.runSilent(pygsti.contract, gs_lgst_target, "CPTP",verbosity=10, tol=10.0) gs_clgst_cptp2 = self.runSilent(pygsti.contract, gs_lgst_target, "CPTP",verbosity=10, useDirectCP=False) gs_clgst_cptp3 = self.runSilent(pygsti.contract, gs_lgst_target, "CPTP",verbosity=10, tol=10.0, maxiter=0) gs_clgst_xp = self.runSilent(pygsti.contract, gs_lgst_target, "XP", ds,verbosity=10, tol=10.0) gs_clgst_xptp = self.runSilent(pygsti.contract, gs_lgst_target, "XPTP", ds,verbosity=10, tol=10.0) gs_clgst_vsp = self.runSilent(pygsti.contract, gs_lgst_target, "vSPAM",verbosity=10, tol=10.0) gs_clgst_none = self.runSilent(pygsti.contract, gs_lgst_target, "nothing",verbosity=10, tol=10.0) #test bad effect vector cases gs_bad_effect = gs_lgst_target.copy() gs_bad_effect.effects['E0'] = [100.0,0,0,0] # E eigvals all > 1.0 self.runSilent(pygsti.contract, gs_bad_effect, "vSPAM",verbosity=10, tol=10.0) gs_bad_effect.effects['E0'] = [-100.0,0,0,0] # E eigvals all < 0 self.runSilent(pygsti.contract, gs_bad_effect, "vSPAM",verbosity=10, tol=10.0) with self.assertRaises(ValueError): self.runSilent(pygsti.contract, gs_lgst_target, "foobar",verbosity=10, tol=10.0) #bad toWhat #More gauge optimizations gs_lgst_target_cp = self.runSilent(pygsti.optimize_gauge, gs_clgst_cptp,"target",targetGateset=self.gateset, constrainToCP=True,constrainToTP=True,constrainToValidSpam=True,verbosity=10) gs_lgst_cptp = self.runSilent(pygsti.optimize_gauge, gs_lgst,"CPTP",verbosity=10) gs_lgst_cptp_tp = self.runSilent(pygsti.optimize_gauge, gs_lgst,"CPTP",verbosity=10, constrainToTP=True) gs_lgst_tp = self.runSilent(pygsti.optimize_gauge, gs_lgst,"TP",verbosity=10) gs_lgst_tptarget = self.runSilent(pygsti.optimize_gauge, gs_lgst,"TP and target",targetGateset=self.gateset,verbosity=10) gs_lgst_cptptarget = self.runSilent(pygsti.optimize_gauge, gs_lgst,"CPTP and target",targetGateset=self.gateset,verbosity=10) gs_lgst_cptptarget2= self.runSilent(pygsti.optimize_gauge, gs_lgst,"CPTP and target",targetGateset=self.gateset, verbosity=10, constrainToTP=True) gs_lgst_cd = self.runSilent(pygsti.optimize_gauge, gs_lgst,"Completely Depolarized",targetGateset=self.gateset,verbosity=10) #TODO: check output lies in space desired # big kick that should land it outside XP, TP, etc, so contraction # routines are more tested gs_bigkick = gs_lgst_target.kick(absmag=1.0) gs_badspam = gs_bigkick.copy() gs_badspam.effects['E0'] = np.array( [[2],[0],[0],[4]], 'd') #set a bad evec so vSPAM has to work... gs_clgst_tp = self.runSilent(pygsti.contract,gs_bigkick, "TP", verbosity=10, tol=10.0) gs_clgst_cp = self.runSilent(pygsti.contract,gs_bigkick, "CP", verbosity=10, tol=10.0) gs_clgst_cptp = self.runSilent(pygsti.contract,gs_bigkick, "CPTP", verbosity=10, tol=10.0) gs_clgst_xp = self.runSilent(pygsti.contract,gs_bigkick, "XP", ds, verbosity=10, tol=10.0) gs_clgst_xptp = self.runSilent(pygsti.contract,gs_bigkick, "XPTP", ds, verbosity=10, tol=10.0) gs_clgst_vsp = self.runSilent(pygsti.contract,gs_badspam, "vSPAM", verbosity=10, tol=10.0) gs_clgst_none = self.runSilent(pygsti.contract,gs_bigkick, "nothing", verbosity=10, tol=10.0) #TODO: check output lies in space desired #Check Errors with self.assertRaises(ValueError): pygsti.optimize_gauge(gs_lgst,"FooBar",verbosity=0) # bad toGetTo argument with self.assertRaises(ValueError): pygsti.contract(gs_lgst_target, "FooBar",verbosity=0) # bad toWhat argument
def test_eLGST(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.specs, self.gateset, svdTruncateTo=4, verbosity=0) gs_lgst_go = pygsti.optimize_gauge(gs_lgst, "target", targetGateset=self.gateset, spamWeight=1.0, gateWeight=1.0) gs_clgst = pygsti.contract(gs_lgst_go, "CPTP") gs_single_exlgst = pygsti.do_exlgst(ds, gs_clgst, self.elgstStrings[0], self.specs, self.gateset, regularizeFactor=1e-3, svdTruncateTo=4, verbosity=0) gs_single_exlgst_verb = self.runSilent(pygsti.do_exlgst, ds, gs_clgst, self.elgstStrings[0], self.specs, self.gateset, regularizeFactor=1e-3, svdTruncateTo=4, verbosity=10) gs_exlgst = pygsti.do_iterative_exlgst(ds, gs_clgst, self.specs, self.elgstStrings, targetGateset=self.gateset, svdTruncateTo=4, verbosity=0) all_minErrs, all_gs_exlgst_tups = pygsti.do_iterative_exlgst( ds, gs_clgst, self.specs, [[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.specs, self.elgstStrings, targetGateset=self.gateset, svdTruncateTo=4, verbosity=10) gs_exlgst_reg = pygsti.do_iterative_exlgst(ds, gs_clgst, self.specs, 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.specs, 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.specs, 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_go = pygsti.optimize_gauge(gs_exlgst, 'target', targetGateset=gs_exlgst_compare, spamWeight=1.0) gs_exlgst_reg_go = pygsti.optimize_gauge( gs_exlgst_reg, 'target', targetGateset=gs_exlgst_reg_compare, spamWeight=1.0) 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.specs, self.gateset, svdTruncateTo=4, verbosity=0) gs_lgst_go = pygsti.optimize_gauge(gs_lgst, "target", targetGateset=self.gateset, spamWeight=1.0, gateWeight=1.0) gs_clgst = pygsti.contract(gs_lgst_go, "CPTP") gs_single_lsgst = pygsti.do_mc2gst(ds, gs_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-6, probClipInterval=(-1e6, 1e6), regularizeFactor=1e-3, verbosity=0) gs_lsgst = pygsti.do_iterative_mc2gst(ds, gs_clgst, self.lsgstStrings, verbosity=0, minProbClipForWeighting=1e-6, probClipInterval=(-1e6, 1e6), memLimit=1000 * 1024**2) 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=10 * 1024**2) 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=100 * 1024**2) 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=100 * 1024**2) #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}) #Check with small but ok memlimit self.runSilent(pygsti.do_mc2gst, ds, gs_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-6, probClipInterval=(-1e6, 1e6), regularizeFactor=1e-3, verbosity=10, memLimit=300000) #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(NotImplementedError): 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) #cptp pentalty not implemented yet # 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.optimize_gauge(gs_lsgst, 'target', targetGateset=gs_lsgst_compare, spamWeight=1.0) gs_lsgst_reg_go = pygsti.optimize_gauge( gs_lsgst_reg, 'target', targetGateset=gs_lsgst_reg_compare, spamWeight=1.0) self.assertAlmostEqual(gs_lsgst_go.frobeniusdist(gs_lsgst_compare), 0, places=5) self.assertAlmostEqual( gs_lsgst_reg_go.frobeniusdist(gs_lsgst_reg_compare), 0, places=5)
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.specs, self.gateset, svdTruncateTo=4, verbosity=0) gs_lgst_go = pygsti.optimize_gauge(gs_lgst,"target",targetGateset=self.gateset, spamWeight=1.0, gateWeight=1.0) gs_clgst = pygsti.contract(gs_lgst_go, "CPTP") gs_single_mlgst = pygsti.do_mlgst(ds, gs_clgst, self.lsgstStrings[0], minProbClip=1e-6, probClipInterval=(-1e2,1e2), verbosity=0) gs_mlegst = pygsti.do_iterative_mlgst(ds, gs_clgst, self.lsgstStrings, verbosity=0, minProbClip=1e-6, probClipInterval=(-1e2,1e2), memLimit=1000*1024**2) 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-6, 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-6, probClipInterval=(-1e2,1e2), memLimit=10*1024**2) self.assertAlmostEqual(gs_mlegst.frobeniusdist(gs_mlegst_verb),0) self.assertAlmostEqual(gs_mlegst.frobeniusdist(all_gs_mlegst_tups[-1]),0) #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-6, 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-6, probClipInterval=(-1e2,1e2), gateStringSetLabels=["Set1","Set2"], useFreqWeightedChiSq=True ) aliased_list = [ pygsti.obj.GateString( [ (x if x != "Gx" else "GA1") for x in gs]) for gs in self.lsgstStrings[0] ] gs_withA1 = gs_clgst.copy(); gs_withA1.gates["GA1"] = gs_clgst.gates["Gx"] gs_mlegst_chk_opts2 = pygsti.do_mlgst(ds, gs_withA1, aliased_list, minProbClip=1e-6, probClipInterval=(-1e2,1e2), verbosity=0, gateLabelAliases={ 'GA1': ('Gx',) }) #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 } ) self.runSilent(pygsti.do_mlgst, ds, gs_clgst, self.lsgstStrings[0], minProbClip=1e-6, probClipInterval=(-1e2,1e2), verbosity=4, memLimit=300000) #invoke memory control pygsti.do_mlgst(ds, gs_clgst, self.lsgstStrings[0], minProbClip=1e-6, probClipInterval=(-1e2,1e2), verbosity=0, poissonPicture=False) #non-Poisson picture - should use (-1,-1) gateset for consistency? #Check errors: with self.assertRaises(MemoryError): pygsti.do_mlgst(ds, gs_clgst, self.lsgstStrings[0], minProbClip=1e-6, 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.optimize_gauge(gs_mlegst, 'target', targetGateset=gs_mle_compare, spamWeight=1.0) self.assertAlmostEqual( gs_mlegst_go.frobeniusdist(gs_mle_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.specs, self.gateset, svdTruncateTo=4, verbosity=0) gs_lgst_go = pygsti.optimize_gauge(gs_lgst,"target",targetGateset=self.gateset, spamWeight=1.0, gateWeight=1.0) gs_clgst = pygsti.contract(gs_lgst_go, "CPTP") gs_single_lsgst = pygsti.do_mc2gst(ds, gs_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6), regularizeFactor=1e-3, verbosity=0) gs_lsgst = pygsti.do_iterative_mc2gst(ds, gs_clgst, self.lsgstStrings, verbosity=0, minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6), memLimit=1000*1024**2) 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=10*1024**2) 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=100*1024**2) 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=100*1024**2) #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 } ) #Check with small but ok memlimit self.runSilent(pygsti.do_mc2gst,ds, gs_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6), regularizeFactor=1e-3, verbosity=10, memLimit=300000) #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(NotImplementedError): 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) #cptp pentalty not implemented yet # 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.optimize_gauge(gs_lsgst, 'target', targetGateset=gs_lsgst_compare, spamWeight=1.0) gs_lsgst_reg_go = pygsti.optimize_gauge(gs_lsgst_reg, 'target', targetGateset=gs_lsgst_reg_compare, spamWeight=1.0) self.assertAlmostEqual( gs_lsgst_go.frobeniusdist(gs_lsgst_compare), 0, places=5) self.assertAlmostEqual( gs_lsgst_reg_go.frobeniusdist(gs_lsgst_reg_compare), 0, places=5)
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.specs, self.gateset, svdTruncateTo=4, verbosity=0) gs_lgst_go = pygsti.optimize_gauge(gs_lgst, "target", targetGateset=self.gateset, spamWeight=1.0, gateWeight=1.0) gs_clgst = pygsti.contract(gs_lgst_go, "CPTP") gs_single_mlgst = pygsti.do_mlgst(ds, gs_clgst, self.lsgstStrings[0], minProbClip=1e-6, probClipInterval=(-1e2, 1e2), verbosity=0) gs_mlegst = pygsti.do_iterative_mlgst(ds, gs_clgst, self.lsgstStrings, verbosity=0, minProbClip=1e-6, probClipInterval=(-1e2, 1e2), memLimit=1000 * 1024**2) 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-6, 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-6, probClipInterval=(-1e2, 1e2), memLimit=10 * 1024**2) self.assertAlmostEqual(gs_mlegst.frobeniusdist(gs_mlegst_verb), 0) self.assertAlmostEqual(gs_mlegst.frobeniusdist(all_gs_mlegst_tups[-1]), 0) #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-6, 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-6, probClipInterval=(-1e2, 1e2), gateStringSetLabels=["Set1", "Set2"], useFreqWeightedChiSq=True) aliased_list = [ pygsti.obj.GateString([(x if x != "Gx" else "GA1") for x in gs]) for gs in self.lsgstStrings[0] ] gs_withA1 = gs_clgst.copy() gs_withA1.gates["GA1"] = gs_clgst.gates["Gx"] gs_mlegst_chk_opts2 = pygsti.do_mlgst( ds, gs_withA1, aliased_list, minProbClip=1e-6, probClipInterval=(-1e2, 1e2), verbosity=0, gateLabelAliases={'GA1': ('Gx', )}) #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}) self.runSilent(pygsti.do_mlgst, ds, gs_clgst, self.lsgstStrings[0], minProbClip=1e-6, probClipInterval=(-1e2, 1e2), verbosity=4, memLimit=300000) #invoke memory control pygsti.do_mlgst(ds, gs_clgst, self.lsgstStrings[0], minProbClip=1e-6, probClipInterval=(-1e2, 1e2), verbosity=0, poissonPicture=False) #non-Poisson picture - should use (-1,-1) gateset for consistency? #Check errors: with self.assertRaises(MemoryError): pygsti.do_mlgst(ds, gs_clgst, self.lsgstStrings[0], minProbClip=1e-6, 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.optimize_gauge(gs_mlegst, 'target', targetGateset=gs_mle_compare, spamWeight=1.0) self.assertAlmostEqual(gs_mlegst_go.frobeniusdist(gs_mle_compare), 0, places=5)
def test_LGST_no_sample_error(self): ds = pygsti.construction.generate_fake_data(self.datagen_gateset, self.lgstStrings, nSamples=1000, sampleError='none') gs_lgst = pygsti.do_lgst(ds, self.specs, self.gateset, svdTruncateTo=4, verbosity=0) gs_lgst = pygsti.optimize_gauge(gs_lgst, "target", targetGateset=self.datagen_gateset, gateWeight=1.0, spamWeight=1.0) self.assertAlmostEqual( gs_lgst.frobeniusdist(self.datagen_gateset), 0)
def test_LGST(self): ds = self.ds #pygsti.construction.generate_fake_data(self.datagen_gateset, self.lgstStrings, nSamples=1000, # sampleError='binomial', seed=None) gs_lgst = pygsti.do_lgst(ds, self.specs, self.gateset, svdTruncateTo=4, verbosity=0) gs_lgst_verb = self.runSilent(pygsti.do_lgst, ds, self.specs, self.gateset, svdTruncateTo=4, verbosity=10) self.assertAlmostEqual(gs_lgst.frobeniusdist(gs_lgst_verb), 0) gs_lgst_go = pygsti.optimize_gauge(gs_lgst, "target", targetGateset=self.gateset, spamWeight=1.0, gateWeight=1.0) 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) self.assertAlmostEqual(gs_lgst_go.frobeniusdist(gs_lgst_go_compare), 0) self.assertAlmostEqual(gs_clgst.frobeniusdist(gs_clgst_compare), 0) #Check for error conditions with self.assertRaises(ValueError): gs_lgst = pygsti.do_lgst(ds, self.specs, None, svdTruncateTo=4, verbosity=0) #no gate labels with self.assertRaises(ValueError): gs_lgst = pygsti.do_lgst(ds, self.specs, None, gateLabels=list( self.gateset.gates.keys()), svdTruncateTo=4, verbosity=0) #no spam dict with self.assertRaises(ValueError): gs_lgst = pygsti.do_lgst( ds, self.specs, 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_specs = pygsti.construction.build_spam_specs( pygsti.construction.gatestring_list([('Gx', ), ('Gx', ), ('Gx', ), ('Gx', )]), effect_labels=['E0']) gs_lgst = pygsti.do_lgst(ds, bad_specs, self.gateset, svdTruncateTo=4, verbosity=0) # bad specs (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.specs, 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.specs, self.gateset, svdTruncateTo=4, verbosity=0)
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.specs, self.gateset, svdTruncateTo=4, verbosity=0) gs_lgst6 = pygsti.do_lgst(ds, self.specs, self.gateset, svdTruncateTo=6, verbosity=0) sys.stdout.flush() self.runSilent(pygsti.do_lgst, ds, self.specs, self.gateset, svdTruncateTo=6, verbosity=4) # test verbose prints chiSq4 = pygsti.chi2(ds, gs_lgst4, self.lgstStrings, minProbClipForWeighting=1e-4) chiSq6 = pygsti.chi2(ds, gs_lgst6, 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.optimize_gauge(gs_lsgst, 'target', targetGateset=gs_lsgst_compare, spamWeight=1.0) self.assertAlmostEqual(gs_lsgst_go.frobeniusdist(gs_lsgst_compare), 0, places=5)
def setUp(self): super(ReportBaseCase, self).setUp() self.targetGateset = std.gs_target datagen_gateset = self.targetGateset.depolarize(gate_noise=0.05, spam_noise=0.1) self.fiducials = std.fiducials self.germs = std.germs self.specs = pygsti.construction.build_spam_specs( self.fiducials, effect_labels=['E0']) #only use the first EVec self.gateLabels = list( self.targetGateset.gates.keys()) # also == std.gates self.lgstStrings = pygsti.construction.list_lgst_gatestrings( self.specs, self.gateLabels) self.maxLengthList = [0, 1, 2, 4, 8] self.lsgstStrings = pygsti.construction.make_lsgst_lists( self.gateLabels, self.fiducials, self.fiducials, self.germs, self.maxLengthList) self.ds = pygsti.objects.DataSet(fileToLoadFrom=compare_files + "/reportgen.dataset") # RUN BELOW LINES TO GENERATE ANALYSIS DATASET #ds = pygsti.construction.generate_fake_data(datagen_gateset, lsgstStrings[-1], nSamples=1000, # sampleError='binomial', seed=100) #ds.save(compare_files + "/reportgen.dataset") gs_lgst = pygsti.do_lgst(self.ds, self.specs, self.targetGateset, svdTruncateTo=4, verbosity=0) gs_lgst_go = pygsti.optimize_gauge(gs_lgst, "target", targetGateset=self.targetGateset, gateWeight=1.0, spamWeight=0.0) self.gs_clgst = pygsti.contract(gs_lgst_go, "CPTP") self.gs_clgst_tp = pygsti.contract(self.gs_clgst, "vSPAM") self.gs_clgst_tp.set_all_parameterizations("TP") try: import pptx self.have_python_pptx = True except ImportError: warnings.warn( "**** IMPORT: Cannot import pptx (python-pptx), and so" + " Powerpoint slide generation tests have been disabled.") self.have_python_pptx = False #Compute results for MC2GST lsgst_gatesets_prego = pygsti.do_iterative_mc2gst( self.ds, self.gs_clgst, self.lsgstStrings, verbosity=0, minProbClipForWeighting=1e-6, probClipInterval=(-1e6, 1e6), returnAll=True) lsgst_gatesets = [ pygsti.optimize_gauge(gs, "target", targetGateset=self.targetGateset, gateWeight=1, spamWeight=0.001) for gs in lsgst_gatesets_prego ] self.results = pygsti.report.Results() self.results.init_Ls_and_germs( "chi2", self.targetGateset, self.ds, self.gs_clgst, self.maxLengthList, self.germs, lsgst_gatesets, self.lsgstStrings, self.fiducials, self.fiducials, pygsti.construction.repeat_with_max_length, False, None, lsgst_gatesets_prego) self.results.parameters.update({ 'minProbClip': 1e-6, 'minProbClipForWeighting': 1e-4, 'probClipInterval': (-1e6, 1e6), 'radius': 1e-4, 'weights': None, 'defaultDirectory': temp_files + "", 'defaultBasename': "MyDefaultReportName" }) self.results.options.precision = 4 self.results.options.polar_precision = 3 #Compute results for MLGST with TP constraint lsgst_gatesets_TP = pygsti.do_iterative_mlgst(self.ds, self.gs_clgst_tp, self.lsgstStrings, verbosity=0, minProbClip=1e-4, probClipInterval=(-1e6, 1e6), returnAll=True) lsgst_gatesets_TP = [ pygsti.optimize_gauge(gs, "target", targetGateset=self.targetGateset, constrainToTP=True, gateWeight=1, spamWeight=0.001) for gs in lsgst_gatesets_TP ] self.results_logL = pygsti.report.Results() self.results_logL.init_Ls_and_germs( "logl", self.targetGateset, self.ds, self.gs_clgst_tp, self.maxLengthList, self.germs, lsgst_gatesets_TP, self.lsgstStrings, self.fiducials, self.fiducials, pygsti.construction.repeat_with_max_length, True) self.results_logL.options.precision = 4 self.results_logL.options.polar_precision = 3 try: basestring #Only defined in Python 2 self.versionsuffix = "" #Python 2 except NameError: self.versionsuffix = "v3" #Python 3
def test_reports_chi2(self): lsgst_gatesets_prego = pygsti.do_iterative_mc2gst(self.ds, self.gs_clgst, self.lsgstStrings, verbosity=0, minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6), returnAll=True) lsgst_gatesets = [ pygsti.optimize_gauge(gs, "target", targetGateset=self.targetGateset, gateWeight=1,spamWeight=0.001) for gs in lsgst_gatesets_prego] self.results = pygsti.report.Results() self.results.init_Ls_and_germs("chi2", self.targetGateset, self.ds, self.gs_clgst, self.maxLengthList, self.germs, lsgst_gatesets, self.lsgstStrings, self.fiducials, self.fiducials, pygsti.construction.repeat_with_max_length, False, None, lsgst_gatesets_prego) self.results.parameters.update({'minProbClip': 1e-6, 'minProbClipForWeighting': 1e-4, 'probClipInterval': (-1e6,1e6), 'radius': 1e-4, 'weights': None, 'defaultDirectory': "temp_test_files", 'defaultBasename': "MyDefaultReportName" } ) #db = lsgst_gatesets[-1] #firstElIdentityVec = np.zeros( (db.dim,1) ) #firstElIdentityVec[0] = db.dim**0.25 #db.povm_identity = firstElIdentityVec # #print "Target Identity = ", np.asarray(self.targetGateset.povm_identity) #print "Identity = ", np.asarray(db.povm_identity) #print "rho0 = ", np.asarray(db.preps['rho0']) #for plbl in db.get_prep_labels(): # for elbl in db.get_effect_labels(): # print "DB: dot(%s,%s) = " % (plbl,elbl), np.dot(np.transpose(db.effects[elbl]),db.preps[plbl]) #return self.results.create_full_report_pdf(filename="temp_test_files/full_reportA.pdf", confidenceLevel=None, debugAidsAppendix=False, gaugeOptAppendix=False, pixelPlotAppendix=False, whackamoleAppendix=False) self.results.create_brief_report_pdf(filename="temp_test_files/brief_reportA.pdf", confidenceLevel=None) self.results.create_presentation_pdf(filename="temp_test_files/slidesA.pdf", confidenceLevel=None, debugAidsAppendix=False, pixelPlotAppendix=False, whackamoleAppendix=False) if self.have_python_pptx: self.results.create_presentation_ppt(filename="temp_test_files/slidesA.ppt", confidenceLevel=None, debugAidsAppendix=False, pixelPlotAppendix=False, whackamoleAppendix=False) #Run again using default filenames self.results.create_full_report_pdf(filename="auto", confidenceLevel=None, debugAidsAppendix=False, gaugeOptAppendix=False, pixelPlotAppendix=False, whackamoleAppendix=False) self.results.create_brief_report_pdf(filename="auto", confidenceLevel=None) self.results.create_presentation_pdf(filename="auto", confidenceLevel=None, debugAidsAppendix=False, pixelPlotAppendix=False, whackamoleAppendix=False) if self.have_python_pptx: self.results.create_presentation_ppt(filename="auto", confidenceLevel=None, debugAidsAppendix=False, pixelPlotAppendix=False, whackamoleAppendix=False) #Compare the text files, assume if these match the PDFs are equivalent self.checkFile("full_reportA.tex") self.checkFile("brief_reportA.tex") self.checkFile("slidesA.tex") self.results.create_full_report_pdf(filename="temp_test_files/full_reportB.pdf", confidenceLevel=95, debugAidsAppendix=True, gaugeOptAppendix=True, pixelPlotAppendix=True, whackamoleAppendix=True, verbosity=2) self.results.create_full_report_pdf(filename="temp_test_files/full_reportB-noGOpt.pdf", confidenceLevel=95, debugAidsAppendix=True, gaugeOptAppendix=False, pixelPlotAppendix=True, whackamoleAppendix=True) # to test blank GOpt tables self.results.create_brief_report_pdf(filename="temp_test_files/brief_reportB.pdf", confidenceLevel=95, verbosity=2) self.results.create_presentation_pdf(filename="temp_test_files/slidesB.pdf", confidenceLevel=95, debugAidsAppendix=True, pixelPlotAppendix=True, whackamoleAppendix=True, verbosity=2) if self.have_python_pptx: self.results.create_presentation_ppt(filename="temp_test_files/slidesB.ppt", confidenceLevel=95, debugAidsAppendix=True, pixelPlotAppendix=True, whackamoleAppendix=True, verbosity=2) #Compare the text files, assume if these match the PDFs are equivalent self.checkFile("full_reportB.tex") self.checkFile("full_reportB_appendices.tex") self.checkFile("brief_reportB.tex") self.checkFile("slidesB.tex") #Non-markovian error bars (negative confidenceLevel) & tooltips self.results.create_full_report_pdf(filename="temp_test_files/full_reportE.pdf", confidenceLevel=-95, debugAidsAppendix=True, gaugeOptAppendix=True, pixelPlotAppendix=True, whackamoleAppendix=True, verbosity=2, tips=True) self.results.create_brief_report_pdf(filename="temp_test_files/brief_reportE.pdf", confidenceLevel=-95, verbosity=2, tips=True) #Compare the text files, assume if these match the PDFs are equivalent self.checkFile("full_reportE.tex") self.checkFile("full_reportE_appendices.tex")
def test_reports_logL_TP(self): lsgst_gatesets_TP = pygsti.do_iterative_mlgst(self.ds, self.gs_clgst_tp, self.lsgstStrings, verbosity=0, minProbClip=1e-4, probClipInterval=(-1e6,1e6), returnAll=True) lsgst_gatesets_TP = [ pygsti.optimize_gauge(gs, "target", targetGateset=self.targetGateset, constrainToTP=True, gateWeight=1,spamWeight=0.001) for gs in lsgst_gatesets_TP] self.results_logL = pygsti.report.Results() self.results_logL.init_Ls_and_germs("logl", self.targetGateset, self.ds, self.gs_clgst_tp, self.maxLengthList, self.germs, lsgst_gatesets_TP, self.lsgstStrings, self.fiducials, self.fiducials, pygsti.construction.repeat_with_max_length, True) #db = lsgst_gatesets_TP[-1] #firstElIdentityVec = np.zeros( (db.dim,1) ) #firstElIdentityVec[0] = db.dim**0.25 #db.povm_identity = firstElIdentityVec # #print "Target Identity = ", np.asarray(self.targetGateset.povm_identity) #print "Identity = ", np.asarray(db.povm_identity) #print "rho0 = ", np.asarray(db.preps['rho0']) #for plbl in db.get_prep_labels(): # for elbl in db.get_effect_labels(): # print "DB: dot(%s,%s) = " % (plbl,elbl), np.dot(np.transpose(db.effects[elbl]),db.preps[plbl]) #return #Run a few tests to generate tables & figures we don't use in reports self.results_logL.tables["chi2ProgressTable"] self.results_logL.tables["logLProgressTable"] self.results_logL.figures["bestEstimateSummedColorBoxPlot"] self.results_logL.figures["blankBoxPlot"] self.results_logL.figures["blankSummedBoxPlot"] self.results_logL.figures["directLGSTColorBoxPlot"] self.results_logL.figures["directLGSTDeviationColorBoxPlot"] with self.assertRaises(KeyError): self.results_logL.figures["FooBar"] with self.assertRaises(KeyError): self.results_logL._specials['FooBar'] #Run tests to generate tables we don't use in reports self.results_logL.tables["bestGatesetVsTargetAnglesTable"] self.results_logL.create_full_report_pdf(filename="temp_test_files/full_reportC.pdf", confidenceLevel=None, debugAidsAppendix=False, gaugeOptAppendix=False, pixelPlotAppendix=False, whackamoleAppendix=False, verbosity=2) self.results_logL.create_brief_report_pdf(filename="temp_test_files/brief_reportC.pdf", confidenceLevel=None, verbosity=2) self.results_logL.create_presentation_pdf(filename="temp_test_files/slidesC.pdf", confidenceLevel=None, debugAidsAppendix=False, pixelPlotAppendix=False, whackamoleAppendix=False, verbosity=2) if self.have_python_pptx: self.results_logL.create_presentation_ppt(filename="temp_test_files/slidesC.ppt", confidenceLevel=None, debugAidsAppendix=False, pixelPlotAppendix=False, whackamoleAppendix=False, verbosity=2) ##Compare the text files, assume if these match the PDFs are equivalent self.checkFile("full_reportC.tex") self.checkFile("brief_reportC.tex") self.checkFile("slidesC.tex") self.results_logL.create_full_report_pdf(filename="temp_test_files/full_reportD.pdf", confidenceLevel=95, debugAidsAppendix=True, gaugeOptAppendix=True, pixelPlotAppendix=True, whackamoleAppendix=True, verbosity=2) self.results_logL.create_brief_report_pdf(filename="temp_test_files/brief_reportD.pdf", confidenceLevel=95, verbosity=2) self.results_logL.create_presentation_pdf(filename="temp_test_files/slidesD.pdf", confidenceLevel=95, debugAidsAppendix=True, pixelPlotAppendix=True, whackamoleAppendix=True, verbosity=2) if self.have_python_pptx: self.results_logL.create_presentation_ppt(filename="temp_test_files/slidesD.ppt", confidenceLevel=95, debugAidsAppendix=True, pixelPlotAppendix=True, whackamoleAppendix=True, verbosity=2) ##Compare the text files, assume if these match the PDFs are equivalent self.checkFile("full_reportD.tex") self.checkFile("full_reportD_appendices.tex") self.checkFile("brief_reportD.tex") self.checkFile("slidesD.tex")
def setUp(self): #Set GateSet objects to "strict" mode for testing pygsti.objects.GateSet._strict = True self.targetGateset = std.gs_target datagen_gateset = self.targetGateset.depolarize(gate_noise=0.05, spam_noise=0.1) self.fiducials = std.fiducials self.germs = std.germs self.specs = pygsti.construction.build_spam_specs(self.fiducials, effect_labels=['E0']) #only use the first EVec self.gateLabels = self.targetGateset.gates.keys() # also == std.gates self.lgstStrings = pygsti.construction.list_lgst_gatestrings(self.specs, self.gateLabels) self.maxLengthList = [0,1,2,4,8] self.lsgstStrings = pygsti.construction.make_lsgst_lists( self.gateLabels, self.fiducials, self.fiducials, self.germs, self.maxLengthList) self.ds = pygsti.objects.DataSet(fileToLoadFrom="cmp_chk_files/reportgen.dataset") # RUN BELOW LINES TO GENERATE ANALYSIS DATASET #ds = pygsti.construction.generate_fake_data(datagen_gateset, lsgstStrings[-1], nSamples=1000, # sampleError='binomial', seed=100) #ds.save("cmp_chk_files/reportgen.dataset") gs_lgst = pygsti.do_lgst(self.ds, self.specs, self.targetGateset, svdTruncateTo=4, verbosity=0) gs_lgst_go = pygsti.optimize_gauge(gs_lgst,"target",targetGateset=self.targetGateset) self.gs_clgst = pygsti.contract(gs_lgst_go, "CPTP") self.gs_clgst_tp = pygsti.contract(self.gs_clgst, "vSPAM") self.gs_clgst_tp.set_all_parameterizations("TP") try: import pptx self.have_python_pptx = True except ImportError: warnings.warn("**** IMPORT: Cannot import pptx (python-pptx), and so" + " Powerpoint slide generation tests have been disabled.") self.have_python_pptx = False #Compute results for MC2GST lsgst_gatesets_prego = pygsti.do_iterative_mc2gst(self.ds, self.gs_clgst, self.lsgstStrings, verbosity=0, minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6), returnAll=True) lsgst_gatesets = [ pygsti.optimize_gauge(gs, "target", targetGateset=self.targetGateset, gateWeight=1,spamWeight=0.001) for gs in lsgst_gatesets_prego] self.results = pygsti.report.Results() self.results.init_Ls_and_germs("chi2", self.targetGateset, self.ds, self.gs_clgst, self.maxLengthList, self.germs, lsgst_gatesets, self.lsgstStrings, self.fiducials, self.fiducials, pygsti.construction.repeat_with_max_length, False, None, lsgst_gatesets_prego) self.results.parameters.update({'minProbClip': 1e-6, 'minProbClipForWeighting': 1e-4, 'probClipInterval': (-1e6,1e6), 'radius': 1e-4, 'weights': None, 'defaultDirectory': "temp_test_files", 'defaultBasename': "MyDefaultReportName" } ) #Compute results for MLGST with TP constraint lsgst_gatesets_TP = pygsti.do_iterative_mlgst(self.ds, self.gs_clgst_tp, self.lsgstStrings, verbosity=0, minProbClip=1e-4, probClipInterval=(-1e6,1e6), returnAll=True) lsgst_gatesets_TP = [ pygsti.optimize_gauge(gs, "target", targetGateset=self.targetGateset, constrainToTP=True, gateWeight=1,spamWeight=0.001) for gs in lsgst_gatesets_TP] self.results_logL = pygsti.report.Results() self.results_logL.init_Ls_and_germs("logl", self.targetGateset, self.ds, self.gs_clgst_tp, self.maxLengthList, self.germs, lsgst_gatesets_TP, self.lsgstStrings, self.fiducials, self.fiducials, pygsti.construction.repeat_with_max_length, True)
def test_gaugeopt_and_contract(self): ds = self.ds_lgst #pygsti.construction.generate_fake_data(self.datagen_gateset, self.lgstStrings, # nSamples=10000,sampleError='binomial', seed=100) gs_lgst = pygsti.do_lgst(ds, self.specs, self.gateset, svdTruncateTo=4, verbosity=0) #Gauge Opt to Target gs_lgst_target = self.runSilent(pygsti.optimize_gauge, gs_lgst, "target", targetGateset=self.gateset, verbosity=10) #Gauge Opt to Target using non-frobenius metrics gs_lgst_targetAlt = self.runSilent(pygsti.optimize_gauge, gs_lgst_target, "target", targetGateset=self.gateset, targetGatesMetric='fidelity', verbosity=10) gs_lgst_targetAlt = self.runSilent(pygsti.optimize_gauge, gs_lgst_target, "target", targetGateset=self.gateset, targetGatesMetric='tracedist', verbosity=10) gs_lgst_targetAlt = self.runSilent(pygsti.optimize_gauge, gs_lgst_target, "target", targetGateset=self.gateset, targetSpamMetric='fidelity', verbosity=10) gs_lgst_targetAlt = self.runSilent(pygsti.optimize_gauge, gs_lgst_target, "target", targetGateset=self.gateset, targetSpamMetric='tracedist', verbosity=10) with self.assertRaises(ValueError): self.runSilent(pygsti.optimize_gauge, gs_lgst_target, "target", targetGateset=self.gateset, targetGatesMetric='foobar', verbosity=10) #bad targetGatesMetric with self.assertRaises(ValueError): self.runSilent(pygsti.optimize_gauge, gs_lgst_target, "target", targetGateset=self.gateset, targetSpamMetric='foobar', verbosity=10) #bad targetSpamMetric with self.assertRaises(ValueError): self.runSilent(pygsti.optimize_gauge, gs_lgst_target, "foobar", targetGateset=self.gateset, targetSpamMetric='target', verbosity=10) #bad toGetTo #Contractions gs_clgst_tp = self.runSilent(pygsti.contract, gs_lgst_target, "TP", verbosity=10, tol=10.0) gs_clgst_cp = self.runSilent(pygsti.contract, gs_lgst_target, "CP", verbosity=10, tol=10.0) gs_clgst_cptp = self.runSilent(pygsti.contract, gs_lgst_target, "CPTP", verbosity=10, tol=10.0) gs_clgst_cptp2 = self.runSilent(pygsti.contract, gs_lgst_target, "CPTP", verbosity=10, useDirectCP=False) gs_clgst_cptp3 = self.runSilent(pygsti.contract, gs_lgst_target, "CPTP", verbosity=10, tol=10.0, maxiter=0) gs_clgst_xp = self.runSilent(pygsti.contract, gs_lgst_target, "XP", ds, verbosity=10, tol=10.0) gs_clgst_xptp = self.runSilent(pygsti.contract, gs_lgst_target, "XPTP", ds, verbosity=10, tol=10.0) gs_clgst_vsp = self.runSilent(pygsti.contract, gs_lgst_target, "vSPAM", verbosity=10, tol=10.0) gs_clgst_none = self.runSilent(pygsti.contract, gs_lgst_target, "nothing", verbosity=10, tol=10.0) #test bad effect vector cases gs_bad_effect = gs_lgst_target.copy() gs_bad_effect.effects['E0'] = [100.0, 0, 0, 0] # E eigvals all > 1.0 self.runSilent(pygsti.contract, gs_bad_effect, "vSPAM", verbosity=10, tol=10.0) gs_bad_effect.effects['E0'] = [-100.0, 0, 0, 0] # E eigvals all < 0 self.runSilent(pygsti.contract, gs_bad_effect, "vSPAM", verbosity=10, tol=10.0) with self.assertRaises(ValueError): self.runSilent(pygsti.contract, gs_lgst_target, "foobar", verbosity=10, tol=10.0) #bad toWhat #More gauge optimizations gs_lgst_target_cp = self.runSilent(pygsti.optimize_gauge, gs_clgst_cptp, "target", targetGateset=self.gateset, constrainToCP=True, constrainToTP=True, constrainToValidSpam=True, verbosity=10) gs_lgst_cptp = self.runSilent(pygsti.optimize_gauge, gs_lgst, "CPTP", verbosity=10) gs_lgst_cptp_tp = self.runSilent(pygsti.optimize_gauge, gs_lgst, "CPTP", verbosity=10, constrainToTP=True) gs_lgst_tp = self.runSilent(pygsti.optimize_gauge, gs_lgst, "TP", verbosity=10) gs_lgst_tptarget = self.runSilent(pygsti.optimize_gauge, gs_lgst, "TP and target", targetGateset=self.gateset, verbosity=10) gs_lgst_cptptarget = self.runSilent(pygsti.optimize_gauge, gs_lgst, "CPTP and target", targetGateset=self.gateset, verbosity=10) gs_lgst_cptptarget2 = self.runSilent(pygsti.optimize_gauge, gs_lgst, "CPTP and target", targetGateset=self.gateset, verbosity=10, constrainToTP=True) gs_lgst_cd = self.runSilent(pygsti.optimize_gauge, gs_lgst, "Completely Depolarized", targetGateset=self.gateset, verbosity=10) #TODO: check output lies in space desired # big kick that should land it outside XP, TP, etc, so contraction # routines are more tested gs_bigkick = gs_lgst_target.kick(absmag=1.0) gs_badspam = gs_bigkick.copy() gs_badspam.effects['E0'] = np.array( [[2], [0], [0], [4]], 'd') #set a bad evec so vSPAM has to work... gs_clgst_tp = self.runSilent(pygsti.contract, gs_bigkick, "TP", verbosity=10, tol=10.0) gs_clgst_cp = self.runSilent(pygsti.contract, gs_bigkick, "CP", verbosity=10, tol=10.0) gs_clgst_cptp = self.runSilent(pygsti.contract, gs_bigkick, "CPTP", verbosity=10, tol=10.0) gs_clgst_xp = self.runSilent(pygsti.contract, gs_bigkick, "XP", ds, verbosity=10, tol=10.0) gs_clgst_xptp = self.runSilent(pygsti.contract, gs_bigkick, "XPTP", ds, verbosity=10, tol=10.0) gs_clgst_vsp = self.runSilent(pygsti.contract, gs_badspam, "vSPAM", verbosity=10, tol=10.0) gs_clgst_none = self.runSilent(pygsti.contract, gs_bigkick, "nothing", verbosity=10, tol=10.0) #TODO: check output lies in space desired #Check Errors with self.assertRaises(ValueError): pygsti.optimize_gauge(gs_lgst, "FooBar", verbosity=0) # bad toGetTo argument with self.assertRaises(ValueError): pygsti.contract(gs_lgst_target, "FooBar", verbosity=0) # bad toWhat argument