/
reportBaseCase.py
165 lines (133 loc) · 8.04 KB
/
reportBaseCase.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
import unittest
import warnings
import pickle
import collections
import pygsti
import os
from pygsti.construction import std1Q_XYI as std
from ..testutils import BaseTestCase, compare_files, temp_files
import numpy as np
class ReportBaseCase(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(ReportBaseCase, cls).setUpClass()
orig_cwd = os.getcwd()
os.chdir(os.path.abspath(os.path.dirname(__file__)))
os.chdir('..') # The test_packages directory
targetModel = std.target_model()
datagen_gateset = targetModel.depolarize(op_noise=0.05, spam_noise=0.1)
datagen_gateset2 = targetModel.depolarize(op_noise=0.1, spam_noise=0.05).rotate((0.15,-0.03,0.03))
#cls.specs = pygsti.construction.build_spam_specs(std.fiducials, effect_labels=['E0'])
# #only use the first EVec
opLabels = std.gates
cls.lgstStrings = pygsti.construction.list_lgst_circuits(std.fiducials, std.fiducials, opLabels)
cls.maxLengthList = [1,2,4,8]
cls.lsgstStrings = pygsti.construction.make_lsgst_lists(
opLabels, std.fiducials, std.fiducials, std.germs, cls.maxLengthList)
cls.lsgstStructs = pygsti.construction.make_lsgst_structs(
opLabels, std.fiducials, std.fiducials, std.germs, cls.maxLengthList)
try:
basestring #Only defined in Python 2
cls.versionsuffix = "" #Python 2
except NameError:
cls.versionsuffix = "v3" #Python 3
# RUN BELOW LINES TO GENERATE ANALYSIS DATASET (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(datagen_gateset, cls.lsgstStrings[-1], nSamples=1000,
sampleError='binomial', seed=100)
ds.save(compare_files + "/reportgen.dataset%s" % cls.versionsuffix)
ds2 = pygsti.construction.generate_fake_data(datagen_gateset2, cls.lsgstStrings[-1], nSamples=1000,
sampleError='binomial', seed=100)
ds2.save(compare_files + "/reportgen2.dataset%s" % cls.versionsuffix)
cls.ds = pygsti.objects.DataSet(fileToLoadFrom=compare_files + "/reportgen.dataset%s" % cls.versionsuffix)
cls.ds2 = pygsti.objects.DataSet(fileToLoadFrom=compare_files + "/reportgen2.dataset%s" % cls.versionsuffix)
mdl_lgst = pygsti.do_lgst(cls.ds, std.fiducials, std.fiducials, targetModel, svdTruncateTo=4, verbosity=0)
mdl_lgst_go = pygsti.gaugeopt_to_target(mdl_lgst, targetModel, {'gates': 1.0, 'spam': 0.0})
cls.mdl_clgst = pygsti.contract(mdl_lgst_go, "CPTP")
cls.mdl_clgst_tp = pygsti.contract(cls.mdl_clgst, "vSPAM")
cls.mdl_clgst_tp.set_all_parameterizations("TP")
#Compute results for MC2GST
lsgst_gatesets_prego = pygsti.do_iterative_mc2gst(
cls.ds, cls.mdl_clgst, cls.lsgstStrings, verbosity=0,
minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6),
returnAll=True)
cls.results = pygsti.objects.Results()
cls.results.init_dataset(cls.ds)
cls.results.init_circuits(cls.lsgstStructs)
cls.results.add_estimate(targetModel, cls.mdl_clgst,
lsgst_gatesets_prego,
{'objective': "chi2",
'minProbClipForWeighting': 1e-4,
'probClipInterval': (-1e6,1e6), 'radius': 1e-4,
'weights': None, 'defaultDirectory': temp_files + "",
'defaultBasename': "MyDefaultReportName"})
gaugeOptParams = collections.OrderedDict([
('model', lsgst_gatesets_prego[-1]), #so can gauge-propagate CIs
('targetModel', targetModel), #so can gauge-propagate CIs
('cptp_penalty_factor', 0),
('gatesMetric',"frobenius"),
('spamMetric',"frobenius"),
('itemWeights', {'gates': 1.0, 'spam': 0.001}),
('returnAll', True) ])
_, gaugeEl, go_final_gateset = pygsti.gaugeopt_to_target(**gaugeOptParams)
gaugeOptParams['_gaugeGroupEl'] = gaugeEl #so can gauge-propagate CIs
cls.results.estimates['default'].add_gaugeoptimized(gaugeOptParams, go_final_gateset)
cls.results.estimates['default'].add_gaugeoptimized(gaugeOptParams, go_final_gateset, "go_dup")
#Compute results for MLGST with TP constraint
# Use do_long_sequence_gst with a non-mark dataset to trigger data scaling
tp_target = targetModel.copy(); tp_target.set_all_parameterizations("TP")
cls.ds3 = cls.ds.copy_nonstatic()
cls.ds3.add_counts_from_dataset(cls.ds2)
cls.ds3.done_adding_data()
cls.results_logL = pygsti.do_long_sequence_gst(cls.ds3, tp_target, std.fiducials, std.fiducials,
std.germs, cls.maxLengthList, verbosity=0,
advancedOptions={'tolerance': 1e-6, 'starting point': 'LGST',
'onBadFit': ["robust","Robust","robust+","Robust+"],
'badFitThreshold': -1.0,
'germLengthLimits': {('Gx','Gi','Gi'): 2} })
#OLD
#lsgst_gatesets_TP = pygsti.do_iterative_mlgst(cls.ds, cls.mdl_clgst_tp, cls.lsgstStrings, verbosity=0,
# minProbClip=1e-4, probClipInterval=(-1e6,1e6),
# returnAll=True) #TP initial model => TP output models
#cls.results_logL = pygsti.objects.Results()
#cls.results_logL.init_dataset(cls.ds)
#cls.results_logL.init_circuits(cls.lsgstStructs)
#cls.results_logL.add_estimate(targetModel, cls.mdl_clgst_tp,
# lsgst_gatesets_TP,
# {'objective': "logl",
# 'minProbClip': 1e-4,
# 'probClipInterval': (-1e6,1e6), 'radius': 1e-4,
# 'weights': None, 'defaultDirectory': temp_files + "",
# 'defaultBasename': "MyDefaultReportName"})
#
#tp_target = targetModel.copy(); tp_target.set_all_parameterizations("TP")
#gaugeOptParams = gaugeOptParams.copy() #just to be safe
#gaugeOptParams['model'] = lsgst_gatesets_TP[-1] #so can gauge-propagate CIs
#gaugeOptParams['targetModel'] = tp_target #so can gauge-propagate CIs
#_, gaugeEl, go_final_gateset = pygsti.gaugeopt_to_target(**gaugeOptParams)
#gaugeOptParams['_gaugeGroupEl'] = gaugeEl #so can gauge-propagate CIs
#cls.results_logL.estimates['default'].add_gaugeoptimized(gaugeOptParams, go_final_gateset)
#
##self.results_logL.options.precision = 3
##self.results_logL.options.polar_precision = 2
os.chdir(orig_cwd)
def setUp(self):
super(ReportBaseCase, self).setUp()
cls = self.__class__
self.targetModel = std.target_model()
self.fiducials = std.fiducials[:]
self.germs = std.germs[:]
self.opLabels = std.gates
#self.specs = cls.specs
self.maxLengthList = cls.maxLengthList[:]
self.lgstStrings = cls.lgstStrings
self.ds = cls.ds
self.mdl_clgst = cls.mdl_clgst.copy()
self.mdl_clgst_tp = cls.mdl_clgst_tp.copy()
self.results = cls.results.copy()
self.results_logL = cls.results_logL.copy()