/
test_optools.py
435 lines (363 loc) · 21.2 KB
/
test_optools.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
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
import functools
from unittest import mock
import sys
import numpy as np
import scipy
from pygsti.baseobjs.basis import Basis
from pygsti.baseobjs.errorgenlabel import LocalElementaryErrorgenLabel as LEEL
import pygsti.tools.basistools as bt
import pygsti.tools.lindbladtools as lt
import pygsti.tools.optools as ot
from pygsti.modelmembers.operations.lindbladcoefficients import LindbladCoefficientBlock
from pygsti.modelpacks.legacy import std2Q_XXYYII
from ..util import BaseCase, needs_cvxpy
SKIP_DIAMONDIST_ON_WIN = True
def fake_minimize(fn):
"""Mock scipy.optimize.minimize in the underlying function call to reduce optimization overhead"""
def side_effect(o, mx, **kwargs):
return mock.MagicMock(x=mx)
@functools.wraps(fn)
def wrapper(*args, **kwargs):
with mock.patch.object(scipy.optimize, 'minimize', side_effect=side_effect):
return fn(*args, **kwargs)
return wrapper
class OpToolsTester(BaseCase):
def test_unitary_to_pauligate(self):
theta = np.pi
sigmax = np.array([[0, 1], [1, 0]])
ex = 1j * theta * sigmax / 2
U = scipy.linalg.expm(ex)
# U is 2x2 unitary matrix operating on single qubit in [0,1] basis (X(pi) rotation)
op = ot.unitary_to_pauligate(U)
op_ans = np.array([[ 1., 0., 0., 0.],
[ 0., 1., 0., 0.],
[ 0., 0., -1., 0.],
[ 0., 0., 0., -1.]], 'd')
self.assertArraysAlmostEqual(op, op_ans)
U_2Q = np.identity(4, 'complex')
U_2Q[2:, 2:] = U
# U_2Q is 4x4 unitary matrix operating on isolated two-qubit space (CX(pi) rotation)
op_2Q = ot.unitary_to_pauligate(U_2Q)
op_2Q_inv = ot.process_mx_to_unitary(bt.change_basis(op_2Q, 'pp', 'std'))
self.assertArraysAlmostEqual(U_2Q, op_2Q_inv)
def test_decompose_gate_matrix(self):
# decompose gate mx whose eigenvalues have a real but non-unit pair
oneRealPair = np.array([
[1+1j, 0, 0, 0], # Angle between 0 and 1 should give rotation
[ 0, 1-1j, 0, 0],
[ 0, 0, 2, 0], # should be picked out as fixed point (first real eigenval)
[ 0, 0, 0, 2] # should be picked out as axis of rotation
], 'complex')
decomp = ot.decompose_gate_matrix(oneRealPair)
self.assertEqual(decomp['isValid'], True)
self.assertEqual(decomp['isUnitary'], False)
self.assertArraysAlmostEqual(decomp['fixed point'], [0, 0, 1, 0])
self.assertArraysAlmostEqual(decomp['axis of rotation'], [0, 0, 0, 1])
self.assertArraysAlmostEqual(decomp['rotating axis 1'], [1, 0, 0, 0])
self.assertArraysAlmostEqual(decomp['rotating axis 2'], [0, 1, 0, 0])
self.assertEqual(decomp['decay of diagonal rotation terms'], 1.0 - 2.0)
self.assertEqual(decomp['decay of off diagonal rotation terms'], 1.0 - abs(1+1j))
self.assertEqual(decomp['pi rotations'], np.angle(1+1j)/np.pi)
dblRealPair = np.array([
[ 3, 0, 0, 0],
[ 0, 3, 0, 0],
[ 0, 0, 2, 0], # still taken as fixed point because closest to identity (1.0)
[ 0, 0, 0, 2]
], 'complex')
decomp = ot.decompose_gate_matrix(dblRealPair)
# decompose gate mx whose eigenvalues have two real but non-unit pairs
self.assertEqual(decomp['isValid'], True)
self.assertEqual(decomp['isUnitary'], False)
self.assertArraysAlmostEqual(decomp['fixed point'], [0, 0, 1, 0])
self.assertArraysAlmostEqual(decomp['axis of rotation'], [0, 0, 0, 1])
self.assertArraysAlmostEqual(decomp['rotating axis 1'], [1, 0, 0, 0])
self.assertArraysAlmostEqual(decomp['rotating axis 2'], [0, 1, 0, 0])
self.assertEqual(decomp['decay of diagonal rotation terms'], 1.0 - 2.0)
self.assertEqual(decomp['decay of off diagonal rotation terms'], 1.0 - 3.0)
self.assertEqual(decomp['pi rotations'], np.angle(3.0)/np.pi)
def test_decompose_gate_matrix_invalidates_on_all_complex_eigval(self):
unpairedMx = np.array([
[1+1j, 0, 0, 0],
[ 0, 2-1j, 0, 0],
[ 0, 0, 2+2j, 0],
[ 0, 0, 0, 1.0+3j]
], 'complex')
decomp = ot.decompose_gate_matrix(unpairedMx)
# decompose gate mx which has all complex eigenvalue -> bail out
self.assertFalse(decomp['isValid'])
def test_decompose_gate_matrix_invalidates_on_large_matrix(self):
largeMx = np.identity(16, 'd')
decomp = ot.decompose_gate_matrix(largeMx) # can only handle 1Q mxs
self.assertFalse(decomp['isValid'])
def test_hack_sqrt_m(self):
expected = np.array([
[ 0.55368857+0.46439416j, 0.80696073-0.21242648j],
[ 1.21044109-0.31863972j, 1.76412966+0.14575444j]
])
sqrt = ot._hack_sqrtm(np.array([[1, 2], [3, 4]]))
self.assertArraysAlmostEqual(sqrt, expected)
def test_unitary_to_process_mx(self):
identity = np.identity(2)
processMx = ot.unitary_to_std_process_mx(identity)
self.assertArraysAlmostEqual(processMx, np.identity(4))
class ProjectModelTester(BaseCase):
def setUp(self):
self.projectionTypes = ('H', 'S', 'H+S', 'LND', 'LNDF')
self.target_model = std2Q_XXYYII.target_model()
self.model = self.target_model.depolarize(op_noise=0.01)
@fake_minimize
def test_log_diff_model_projection(self):
self.skipTest("project_model for logG-logT is known to be inconsistent in testing (Gxx,Gxy,Gyx,Gyy gates). Skip tests until it gets fixed.")
basis = self.target_model.basis
gen_type = 'logG-logT'
proj_model, Np_dict = ot.project_model(self.model, self.target_model, self.projectionTypes, gen_type, logG_weight=0)
# Project a second time and ensure models don't change
for pm1, ptype in zip(proj_model, self.projectionTypes):
proj2, _ = ot.project_model(pm1, self.target_model, [ptype], gen_type, logG_weight=0)
pm2 = proj2[0]
for pm1_op, pm2_op in zip(pm1.operations.values(), pm2.operations.values()):
self.assertArraysAlmostEqual(pm1_op, pm2_op)
def test_logTiG_model_projection(self):
gen_type = 'logTiG'
proj_model, Np_dict = ot.project_model(self.model, self.target_model, self.projectionTypes, gen_type)
# Project a second time and ensure models don't change
for pm1, ptype in zip(proj_model, self.projectionTypes):
proj2, _ = ot.project_model(pm1, self.target_model, [ptype], gen_type, logG_weight=0)
pm2 = proj2[0]
for pm1_op, pm2_op in zip(pm1.operations.values(), pm2.operations.values()):
self.assertArraysAlmostEqual(pm1_op, pm2_op)
def test_logGTi_model_projection(self):
gen_type = 'logGTi'
proj_model, Np_dict = ot.project_model(self.model, self.target_model, self.projectionTypes, gen_type)
# Project a second time and ensure models don't change
for pm1, ptype in zip(proj_model, self.projectionTypes):
proj2, _ = ot.project_model(pm1, self.target_model, [ptype], gen_type, logG_weight=0)
pm2 = proj2[0]
for pm1_op, pm2_op in zip(pm1.operations.values(), pm2.operations.values()):
self.assertArraysAlmostEqual(pm1_op, pm2_op)
def test_raises_on_basis_mismatch(self):
with self.assertRaises(ValueError):
mdl_target_gm = std2Q_XXYYII.target_model()
mdl_target_gm.basis = Basis.cast("gm", 16)
ot.project_model(self.model, mdl_target_gm, self.projectionTypes, 'logGti') # basis mismatch
class ErrorGenTester(BaseCase):
def setUp(self):
self.target_model = std2Q_XXYYII.target_model()
self.mdl_datagen = self.target_model.depolarize(op_noise=0.1, spam_noise=0.001)
def test_std_errgens(self):
projectionTypes = ['H', 'S', 'C', 'A']
basisNames = ['gm', 'pp'] # , 'qt'] #dim must == 3 for qt
# Note: bases must have first element == identity
for projectionType in projectionTypes:
#REMOVE ot.std_scale_factor(4, projectionType)
for basisName in basisNames:
#REMOVE ot.std_error_generators(4, projectionType, basisName)
ot.elementary_errorgens_dual(4, projectionType, basisName)
def test_std_errgens_raise_on_bad_projection_type(self):
with self.assertRaises(AssertionError):
#REMOVE ot.std_error_generators(4, "foobar", 'gm')
ot.elementary_errorgens_dual(4, "foobar", 'gm')
def test_lind_errgens(self):
bases = [Basis.cast('gm', 4),
Basis.cast('pp', 4),
Basis.cast('PP', 4)]
for basis in bases:
print(basis)
Hblk = LindbladCoefficientBlock('ham', basis)
Hblk_superops = Hblk.create_lindblad_term_superoperators(mx_basis='std')
for i, mi in enumerate(basis[1:]):
Hi = lt.create_elementary_errorgen('H', mi)
HiB = lt.create_lindbladian_term_errorgen('H', mi)
self.assertArraysAlmostEqual(Hi, HiB)
self.assertArraysAlmostEqual(Hi, Hblk_superops[i])
ODblk = LindbladCoefficientBlock('other_diagonal', basis)
ODblk_superops = ODblk.create_lindblad_term_superoperators(mx_basis='std')
for i, mi in enumerate(basis[1:]):
ODi = lt.create_elementary_errorgen('S', mi)
ODiB = lt.create_lindbladian_term_errorgen('O', mi, mi)
self.assertArraysAlmostEqual(ODi, ODiB)
self.assertArraysAlmostEqual(ODi, ODblk_superops[i])
Oblk = LindbladCoefficientBlock('other', basis)
Oblk_superops = Oblk.create_lindblad_term_superoperators(mx_basis='std')
for i, mi in enumerate(basis[1:]):
for j, mj in enumerate(basis[1:]):
Oij = lt.create_lindbladian_term_errorgen('O', mi, mj)
self.assertArraysAlmostEqual(Oij, Oblk_superops[i][j])
# C_PQ = NH_PQ + NH_QP
# A_PQ = i(NH_PQ - NH_QP)
if i < j:
Cij = lt.create_elementary_errorgen('C', mi, mj)
Aij = lt.create_elementary_errorgen('A', mi, mj)
self.assertArraysAlmostEqual(Oij, (Cij + 1j * Aij) / 2.0)
elif j < i:
Cji = lt.create_elementary_errorgen('C', mj, mi)
Aji = lt.create_elementary_errorgen('A', mj, mi)
self.assertArraysAlmostEqual(Oij, (Cji - 1j * Aji) / 2.0)
else: # i == j
Sii = lt.create_elementary_errorgen('S', mi)
self.assertArraysAlmostEqual(Oij, Sii)
def test_lind_errgen_projects(self):
mx_basis = Basis.cast('pp', 4)
basis = Basis.cast('PP', 4)
X = basis['X']
Y = basis['Y']
Z = basis['Z']
# Build known combination to project back to
errgen = 0.1 * lt.create_elementary_errorgen('H', Z) \
- 0.01 * lt.create_elementary_errorgen('H', X) \
+ 0.2 * lt.create_elementary_errorgen('S', X) \
+ 0.25 * lt.create_elementary_errorgen('S', Y) \
+ 0.05 * lt.create_elementary_errorgen('C', X, Y) \
- 0.01 * lt.create_elementary_errorgen('A', X, Y)
errgen = bt.change_basis(errgen, 'std', mx_basis)
Hblk = LindbladCoefficientBlock('ham', basis)
ODblk = LindbladCoefficientBlock('other_diagonal', basis)
Oblk = LindbladCoefficientBlock('other', basis)
Hblk.set_from_errorgen_projections(errgen, errorgen_basis=mx_basis)
ODblk.set_from_errorgen_projections(errgen, errorgen_basis=mx_basis)
Oblk.set_from_errorgen_projections(errgen, errorgen_basis=mx_basis)
self.assertArraysAlmostEqual(Hblk.block_data, [-0.01, 0, 0.1])
self.assertArraysAlmostEqual(ODblk.block_data, [0.2, 0.25, 0])
self.assertArraysAlmostEqual(Oblk.block_data,
np.array([[0.2, 0.05 + 0.01j, 0],
[0.05 - 0.01j, 0.25, 0],
[0, 0, 0]]))
def dicts_equal(d, f):
f = {LEEL.cast(k): v for k, v in f.items()}
if set(d.keys()) != set(f.keys()): return False
for k in d:
if abs(d[k] - f[k]) > 1e-12: return False
return True
self.assertTrue(dicts_equal(Hblk.elementary_errorgens, {('H','Z'): 0.1, ('H','X'): -0.01, ('H','Y'): 0}))
self.assertTrue(dicts_equal(ODblk.elementary_errorgens, {('S','X'): 0.2, ('S','Y'): 0.25, ('S','Z'): 0}))
self.assertTrue(dicts_equal(Oblk.elementary_errorgens,
{('S', 'X'): 0.2,
('S', 'Y'): 0.25,
('S', 'Z'): 0.0,
('C', 'X', 'Y'): 0.05,
('A', 'X', 'Y'): -0.01,
('C', 'X', 'Z'): 0,
('A', 'X', 'Z'): 0,
('C', 'Y', 'Z'): 0,
('A', 'Y', 'Z'): 0,
}))
#TODO: test with sparse bases??
#TODO: test basis from name (seems unnecessary)?
@fake_minimize
def test_err_gen(self):
projectionTypes = ['hamiltonian', 'stochastic', 'affine']
basisNames = ['std', 'gm', 'pp'] # , 'qt'] #dim must == 3 for qt
for (lbl, gateTarget), gate in zip(self.target_model.operations.items(), self.mdl_datagen.operations.values()):
errgen = ot.error_generator(gate, gateTarget, self.target_model.basis, 'logG-logT')
altErrgen = ot.error_generator(gate, gateTarget, self.target_model.basis, 'logTiG')
altErrgen2 = ot.error_generator(gate, gateTarget, self.target_model.basis, 'logGTi')
with self.assertRaises(ValueError):
ot.error_generator(gate, gateTarget, self.target_model.basis, 'adsf')
#OLD: tested above
#for projectionType in projectionTypes:
# for basisName in basisNames:
# ot.std_errorgen_projections(errgen, projectionType, basisName)
originalGate = ot.operation_from_error_generator(errgen, gateTarget, self.target_model.basis, 'logG-logT')
altOriginalGate = ot.operation_from_error_generator(altErrgen, gateTarget, self.target_model.basis, 'logTiG')
altOriginalGate2 = ot.operation_from_error_generator(altErrgen, gateTarget, self.target_model.basis, 'logGTi')
with self.assertRaises(ValueError):
ot.operation_from_error_generator(errgen, gateTarget, self.target_model.basis, 'adsf')
self.assertArraysAlmostEqual(originalGate, gate) # sometimes need to approximate the log for this one
self.assertArraysAlmostEqual(altOriginalGate, gate)
self.assertArraysAlmostEqual(altOriginalGate2, gate)
@fake_minimize
def test_err_gen_nonunitary(self):
errgen_nonunitary = ot.error_generator(self.mdl_datagen.operations['Gxi'],
self.mdl_datagen.operations['Gxi'],
self.mdl_datagen.basis)
# Perfect match, should get all 0s
self.assertArraysAlmostEqual(np.zeros_like(self.mdl_datagen.operations['Gxi']), errgen_nonunitary)
def test_err_gen_not_near_gate(self):
# Both should warn
with self.assertWarns(UserWarning):
errgen_notsmall = ot.error_generator(self.mdl_datagen.operations['Gxi'], self.target_model.operations['Gix'],
self.target_model.basis, 'logTiG')
with self.assertWarns(UserWarning):
errgen_notsmall = ot.error_generator(self.mdl_datagen.operations['Gxi'], self.target_model.operations['Gix'],
self.target_model.basis, 'logGTi')
def test_err_gen_raises_on_bad_type(self):
with self.assertRaises(ValueError):
ot.error_generator(self.mdl_datagen.operations['Gxi'], self.target_model.operations['Gxi'],
self.target_model.basis, 'foobar')
def test_err_gen_assert_shape_raises_on_ndims_too_high(self):
# Check helper routine _assert_shape
with self.assertRaises(NotImplementedError): # boundary case
ot._assert_shape(np.zeros((2, 2, 2, 2, 2), 'd'), (2, 2, 2, 2, 2), sparse=True) # ndims must be <= 4
class GateOpsTester(BaseCase):
def setUp(self):
self.A = np.array([
[ 0.9, 0, 0.1j, 0],
[ 0, 0, 0, 0],
[-0.1j, 0, 0, 0],
[ 0, 0, 0, 0.1]
], 'complex')
self.B = np.array([
[ 0.5, 0, 0, -0.2j],
[ 0, 0.25, 0, 0],
[ 0, 0, 0.25, 0],
[0.2j, 0, 0, 0.1]
], 'complex')
self.A_TP= np.array([
[1, 0, 0, 0],
[0, -0.74972882, 0.06641116, -0.65840432],
[0, -0.07921032, -0.99680422, -0.01034724],
[0, -0.65698738, 0.04439479, 0.7525933 ]])
self.B_unitary= np.array([
[1, 0, 0, 0 ],
[0, -0.29719065, 0.63991085, -0.70865494],
[0, 0.79014219, -0.2518555 , -0.55878809],
[0, -0.5360532 , -0.72600476, -0.43077146]])
self.A_TP_std= np.array([
[ 0.87629665+0.j, -0.32849369+0.0221974j, -0.32849369-0.0221974j, 0.12370335+0.j],
[-0.32920216+0.00517362j, -0.87326652+0.07281074j, 0.1235377 +0.00639958j, 0.32920216-0.00517362j],
[-0.32920216-0.00517362j, 0.1235377 -0.00639958j, -0.87326652-0.07281074j, 0.32920216+0.00517362j],
[ 0.12370335+0.j, 0.32849369-0.0221974j,0.32849369+0.0221974j , 0.87629665+0.j]])
self.B_unitary_std= np.array([
[ 0.28461427+0.j, -0.2680266 -0.36300238j, -0.2680266 + 0.36300238j, 0.71538573+0.j],
[-0.35432747+0.27939404j, -0.27452307-0.07511567j, -0.02266757-0.71502652j, 0.35432747-0.27939404j],
[-0.35432747-0.27939404j, -0.02266757+0.71502652j, -0.27452307+0.07511567j, 0.35432747+0.27939404j],
[ 0.71538573+0.j, 0.2680266 +0.36300238j, 0.2680266 -0.36300238j, 0.28461427+0.j]])
def test_frobenius_distance(self):
self.assertAlmostEqual(ot.frobeniusdist(self.A, self.A), 0.0)
self.assertAlmostEqual(ot.frobeniusdist(self.A, self.B), (0.430116263352+0j))
self.assertAlmostEqual(ot.frobeniusdist_squared(self.A, self.A), 0.0)
self.assertAlmostEqual(ot.frobeniusdist_squared(self.A, self.B), (0.185+0j))
def test_jtrace_distance(self):
self.assertAlmostEqual(ot.jtracedist(self.A, self.A, mx_basis="std"), 0.0)
self.assertAlmostEqual(ot.jtracedist(self.A, self.B, mx_basis="std"), 0.26430148) # OLD: 0.2601 ?
@needs_cvxpy
def test_diamond_distance(self):
if SKIP_DIAMONDIST_ON_WIN and sys.platform.startswith('win'): return
self.assertAlmostEqual(ot.diamonddist(self.A, self.A, mx_basis="std"), 0.0)
self.assertAlmostEqual(ot.diamonddist(self.A, self.B, mx_basis="std"), 0.614258836298)
def test_frobenius_norm_equiv(self):
from pygsti.tools import matrixtools as mt
self.assertAlmostEqual(ot.frobeniusdist(self.A, self.B), mt.frobeniusnorm(self.A - self.B))
self.assertAlmostEqual(ot.frobeniusdist(self.A, self.B), np.sqrt(mt.frobeniusnorm_squared(self.A - self.B)))
def test_entanglement_fidelity(self):
fidelity = ot.entanglement_fidelity(self.A, self.B)
fidelity_TP_unitary= ot.entanglement_fidelity(self.A_TP, self.B_unitary, is_tp=True, is_unitary=True)
fidelity_TP_unitary_no_flag= ot.entanglement_fidelity(self.A_TP, self.B_unitary)
fidelity_TP_unitary_jam= ot.entanglement_fidelity(self.A_TP, self.B_unitary, is_tp=False, is_unitary=False)
fidelity_TP_unitary_std= ot.entanglement_fidelity(self.A_TP_std, self.B_unitary_std, mx_basis='std')
self.assertAlmostEqual(fidelity, 0.42686642003)
self.assertAlmostEqual(fidelity_TP_unitary, 0.4804724656092404)
self.assertAlmostEqual(fidelity_TP_unitary_no_flag, 0.4804724656092404)
self.assertAlmostEqual(fidelity_TP_unitary, fidelity_TP_unitary_jam)
self.assertAlmostEqual(fidelity_TP_unitary_std, 0.4804724656092404)
def test_fidelity_upper_bound(self):
upperBound = ot.fidelity_upper_bound(self.A)
expected = (
np.array([[ 0.25]]),
np.array([[ 1.00000000e+00, -8.27013523e-16, 8.57305616e-33, 1.95140273e-15],
[ -8.27013523e-16, 1.00000000e+00, 6.28036983e-16, -8.74760501e-31],
[ 5.68444574e-33, -6.28036983e-16, 1.00000000e+00, -2.84689309e-16],
[ 1.95140273e-15, -9.27538795e-31, 2.84689309e-16, 1.00000000e+00]])
)
self.assertArraysAlmostEqual(upperBound[0], expected[0])
self.assertArraysAlmostEqual(upperBound[1], expected[1])