跳转至

utils

QuICT.core.gate.utils.GateMatrixGenerator

Generator the Quantum Gates' Matrix.

based_matrix 

based_matrix(gate_type: GateType, precision: complex)

Return the no-parameter Quantum Gates' matrix.

Parameters:

  • gate_type (GateType) –

    The type of Quantum Gate

  • precision (complex) –

    The precision of Quantum Gate

Returns:

  • –

    np.ndarray: The Quantum Gate's matrix

Source code in QuICT/core/gate/utils/gate_matrix.py 
 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
def based_matrix(self, gate_type: GateType, precision: complex):
    """ Return the no-parameter Quantum Gates' matrix.

    Args:
        gate_type (GateType): The type of Quantum Gate
        precision (complex): The precision of Quantum Gate

    Returns:
        np.ndarray: The Quantum Gate's matrix
    """
    if gate_type in [GateType.h, GateType.ch]:
        return np.array([
            [1 / np.sqrt(2), 1 / np.sqrt(2)],
            [1 / np.sqrt(2), -1 / np.sqrt(2)]
        ], dtype=precision)
    elif gate_type == GateType.hy:
        return np.array([
            [1 / np.sqrt(2), -1j / np.sqrt(2)],
            [1j / np.sqrt(2), -1 / np.sqrt(2)]
        ], dtype=precision)
    elif gate_type == GateType.s:
        return np.array([
            [1, 0],
            [0, 1j]
        ], dtype=precision)
    elif gate_type == GateType.sdg:
        return np.array([
            [1, 0],
            [0, -1j]
        ], dtype=precision)
    elif gate_type in [GateType.x, GateType.cx, GateType.ccx]:
        return np.array([
            [0, 1],
            [1, 0]
        ], dtype=precision)
    elif gate_type in [GateType.y, GateType.cy]:
        return np.array([
            [0, -1j],
            [1j, 0]
        ], dtype=precision)
    elif gate_type in [GateType.z, GateType.cz, GateType.ccz]:
        return np.array([
            [1, 0],
            [0, -1]
        ], dtype=precision)
    elif gate_type == GateType.sx:
        return np.array([
            [0.5 + 0.5j, 0.5 - 0.5j],
            [0.5 - 0.5j, 0.5 + 0.5j]
        ], dtype=precision)
    elif gate_type == GateType.sxdg:
        return np.array([
            [0.5 - 0.5j, 0.5 + 0.5j],
            [0.5 + 0.5j, 0.5 - 0.5j]
        ], dtype=precision)
    elif gate_type == GateType.sy:
        return np.array([
            [1 / np.sqrt(2), -1 / np.sqrt(2)],
            [1 / np.sqrt(2), 1 / np.sqrt(2)]
        ], dtype=precision)
    elif gate_type == GateType.sydg:
        return np.array([
            [1 / np.sqrt(2), 1 / np.sqrt(2)],
            [-1 / np.sqrt(2), 1 / np.sqrt(2)]
        ], dtype=precision)
    elif gate_type == GateType.sw:
        return np.array([
            [1 / np.sqrt(2), -np.sqrt(1j / 2)],
            [np.sqrt(-1j / 2), 1 / np.sqrt(2)]
        ], dtype=precision)
    elif gate_type == GateType.id:
        return np.array([
            [1, 0],
            [0, 1]
        ], dtype=precision)
    elif gate_type == GateType.t:
        return np.array([
            [1, 0],
            [0, 1 / np.sqrt(2) + 1j * 1 / np.sqrt(2)]
        ], dtype=precision)
    elif gate_type == GateType.tdg:
        return np.array([
            [1, 0],
            [0, 1 / np.sqrt(2) + 1j * -1 / np.sqrt(2)]
        ], dtype=precision)
    elif gate_type in [GateType.swap, GateType.cswap]:
        return np.array([
            [1, 0, 0, 0],
            [0, 0, 1, 0],
            [0, 1, 0, 0],
            [0, 0, 0, 1]
        ], dtype=precision)
    elif gate_type == GateType.iswap:
        return np.array([
            [1, 0, 0, 0],
            [0, 0, 1j, 0],
            [0, 1j, 0, 0],
            [0, 0, 0, 1]
        ], dtype=precision)
    elif gate_type == GateType.iswapdg:
        return np.array([
            [1, 0, 0, 0],
            [0, 0, -1j, 0],
            [0, -1j, 0, 0],
            [0, 0, 0, 1]
        ], dtype=precision)
    elif gate_type == GateType.sqiswap:
        return np.array([
            [1, 0, 0, 0],
            [0, 1 / np.sqrt(2), 1j / np.sqrt(2), 0],
            [0, 1j / np.sqrt(2), 1 / np.sqrt(2), 0],
            [0, 0, 0, 1]
        ], dtype=precision)
    elif gate_type == GateType.ecr:
        return np.array([
            [0, 0, 1 / np.sqrt(2), 1j / np.sqrt(2)],
            [0, 0, 1j / np.sqrt(2), 1 / np.sqrt(2)],
            [1 / np.sqrt(2), -1j / np.sqrt(2), 0, 0],
            [-1j / np.sqrt(2), 1 / np.sqrt(2), 0, 0]
        ], dtype=precision)
    elif gate_type == GateType.rccx:
        return np.array([
            [1, 0, 0, 0],
            [0, -1, 0, 0],
            [0, 0, 0, 1],
            [0, 0, 1, 0]
        ], dtype=precision)
    else:
        raise TypeError(gate_type)

get_matrix 

get_matrix(gate, precision: str = None, is_get_target: bool = False) -> np.ndarray

Return the given BasicGate's matrix.

Parameters:

  • gate (BasicGate) –

    The Quantum Gate

  • precision (str, default: None ) –

    The precision of Quantum Gate. Defaults to None.

  • is_get_target (bool, default: False ) –

    Whether return the completed BasicGate's matrix, or only return the target

Returns:

  • ndarray –

    np.ndarray: The Quantum Gates' matrix.

Source code in QuICT/core/gate/utils/gate_matrix.py 
 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
def get_matrix(self, gate, precision: str = None, is_get_target: bool = False) -> np.ndarray:
    """ Return the given BasicGate's matrix.

    Args:
        gate (BasicGate): The Quantum Gate
        precision (str, optional): The precision of Quantum Gate. Defaults to None.
        is_get_target (bool, optional): Whether return the completed BasicGate's matrix, or only return the target
        qubits part. Defaults to False.

    Returns:
        np.ndarray: The Quantum Gates' matrix.
    """
    # Step 1: Get based matrix's value
    gate_type = gate.type
    _precision = gate.precision if precision is None else precision
    gate_precision = np.complex128 if _precision == "double" else np.complex64
    gate_params = gate.params
    if gate_params == 0:
        based_matrix = self.based_matrix(gate_type, gate_precision)
    else:
        based_matrix = self.matrix_with_param(
            gate_type,
            gate.pargs,
            gate.symbol_gate,
            gate.symbol_pargs,
            gate_precision,
        )

    if is_get_target:
        return based_matrix

    # Step 2: Depending on controlled_by, generate final matrix
    if gate.controls > 0:
        if gate_type == GateType.rccx:
            control_args = gate.controls - 1
            target_args = gate.targets + 1
        else:
            control_args = gate.controls
            target_args = gate.targets

        controlled_matrix = np.identity(
            1 << (control_args + target_args), dtype=gate_precision
        )
        target_border = 1 << target_args
        controlled_matrix[-target_border:, -target_border:] = based_matrix

        return controlled_matrix

    return based_matrix

grad_for_param 

grad_for_param(gate_type: GateType, gate_pargs: list, symbol_pargs: dict, precision: str)

Return the Parameterized Quantum Gates' gradient matrices.

Parameters:

  • gate_type (GateType) –

    The type of Quantum Gate.

  • gate_pargs (List) –

    The Quantum Gate's parameters.

  • symbol_pargs (dict) –

    The Quantum Gate's symbol parameters.

  • precision (str) –

    The precision of Quantum Gate, one of [double, single]

Returns:

  • list –

    Parameterized Quantum Gates' gradient matrices.

Source code in QuICT/core/gate/utils/gate_matrix.py 
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
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
def grad_for_param(self, gate_type: GateType, gate_pargs: list, symbol_pargs: dict, precision: str):
    """Return the Parameterized Quantum Gates' gradient matrices.

    Args:
        gate_type (GateType): The type of Quantum Gate.
        gate_pargs (List): The Quantum Gate's parameters.
        symbol_pargs (dict): The Quantum Gate's symbol parameters.
        precision (str): The precision of Quantum Gate, one of [double, single]

    Returns:
        list: Parameterized Quantum Gates' gradient matrices.
    """
    pargs = []
    assert len(symbol_pargs) > 0, "The symbol gate must be assigned values before calculation."
    for i in range(len(gate_pargs)):
        pargs.append(symbol_pargs[gate_pargs[i]])

    precision = np.complex128 if precision == "double" else np.complex64

    if gate_type in [GateType.u1, GateType.cu1]:
        return [np.array([
            [1, 0],
            [0, np.exp(1j * pargs[0])]
        ], dtype=precision)]

    elif gate_type == GateType.u2:
        sqrt2 = 1 / np.sqrt(2)
        grad1 = np.array([
            [1 * sqrt2,
             -np.exp(1j * pargs[1]) * sqrt2],
            [1j * np.exp(1j * pargs[0]) * sqrt2,
             1j * np.exp(1j * (pargs[0] + pargs[1])) * sqrt2]
        ], dtype=precision)
        grad2 = np.array([
            [1 * sqrt2,
             -1j * np.exp(1j * pargs[1]) * sqrt2],
            [np.exp(1j * pargs[0]) * sqrt2,
             1j * np.exp(1j * (pargs[0] + pargs[1])) * sqrt2]
        ], dtype=precision)
        return [grad1, grad2]

    elif gate_type in [GateType.u3, GateType.cu3]:
        sin_v = np.sin(pargs[0] / 2)
        cos_v = np.cos(pargs[0] / 2)
        grad1 = np.array([
            [-sin_v / 2,
             -np.exp(1j * pargs[2]) * cos_v / 2],
            [np.exp(1j * pargs[1]) * cos_v / 2,
             -np.exp(1j * (pargs[1] + pargs[2])) * sin_v / 2]
        ], dtype=precision)
        grad2 = np.array([
            [cos_v,
             -np.exp(1j * pargs[2]) * sin_v],
            [1j * np.exp(1j * pargs[1]) * sin_v,
             1j * np.exp(1j * (pargs[1] + pargs[2])) * cos_v]
        ], dtype=precision)
        grad3 = np.array([
            [cos_v,
             -1j * np.exp(1j * pargs[2]) * sin_v],
            [np.exp(1j * pargs[1]) * sin_v,
             1j * np.exp(1j * (pargs[1] + pargs[2])) * cos_v]
        ], dtype=precision)
        return [grad1, grad2, grad3]

    elif gate_type == GateType.rx:
        cos_v = -np.cos(pargs[0] / 2) / 2
        sin_v = -np.sin(pargs[0] / 2) / 2
        return [np.array([
            [sin_v, 1j * cos_v],
            [1j * cos_v, sin_v]
        ], dtype=precision)]

    elif gate_type in [GateType.ry, GateType.cry]:
        cos_v = np.cos(pargs[0] / 2) / 2
        sin_v = np.sin(pargs[0] / 2) / 2
        return [np.array([
            [-sin_v, -cos_v],
            [cos_v, -sin_v]
        ], dtype=precision)]

    elif gate_type in [GateType.rz, GateType.crz, GateType.ccrz]:
        return [np.array([
            [-1j * np.exp(-pargs[0] / 2 * 1j) / 2, 0],
            [0, 1j * np.exp(pargs[0] / 2 * 1j) / 2]
        ], dtype=precision)]

    elif gate_type == GateType.phase:
        return [np.array([
            [1, 0],
            [0, 1j * np.exp(pargs[0] * 1j)]
        ], dtype=precision)]

    elif gate_type == GateType.gphase:
        return [np.array([
            [1j * np.exp(pargs[0] * 1j), 0],
            [0, 1j * np.exp(pargs[0] * 1j)]
        ], dtype=precision)]

    elif gate_type == GateType.fsim:
        costh = np.cos(pargs[0])
        sinth = np.sin(pargs[0])
        phi = pargs[1]
        grad1 = np.array([
            [1, 0, 0, 0],
            [0, -sinth, -1j * costh, 0],
            [0, -1j * costh, -sinth, 0],
            [0, 0, 0, np.exp(-1j * phi)]
        ], dtype=precision)
        grad2 = np.array([
            [1, 0, 0, 0],
            [0, costh, -1j * sinth, 0],
            [0, -1j * sinth, costh, 0],
            [0, 0, 0, -1j * np.exp(-1j * phi)]
        ], dtype=precision)
        return [grad1, grad2]

    elif gate_type == GateType.rxx:
        costh = np.cos(pargs[0] / 2) / 2
        sinth = np.sin(pargs[0] / 2) / 2

        return [np.array([
            [-sinth, 0, 0, -1j * costh],
            [0, -sinth, -1j * costh, 0],
            [0, -1j * costh, -sinth, 0],
            [-1j * costh, 0, 0, -sinth]
        ], dtype=precision)]

    elif gate_type == GateType.ryy:
        costh = np.cos(pargs[0] / 2) / 2
        sinth = np.sin(pargs[0] / 2) / 2

        return [np.array([
            [-sinth, 0, 0, 1j * costh],
            [0, -sinth, -1j * costh, 0],
            [0, -1j * costh, -sinth, 0],
            [1j * costh, 0, 0, -sinth]
        ], dtype=precision)]

    elif gate_type == GateType.rzz:
        expth = 0.5j * np.exp(0.5j * pargs[0])
        sexpth = -0.5j * np.exp(-0.5j * pargs[0])

        return [np.array([
            [sexpth, 0, 0, 0],
            [0, expth, 0, 0],
            [0, 0, expth, 0],
            [0, 0, 0, sexpth]
        ], dtype=precision)]

    elif gate_type == GateType.rzx:
        costh = np.cos(pargs[0] / 2) / 2
        sinth = np.sin(pargs[0] / 2) / 2

        return [np.array([
            [-sinth, -1j * costh, 0, 0],
            [-1j * costh, -sinth, 0, 0],
            [0, 0, -sinth, 1j * costh],
            [0, 0, 1j * costh, -sinth]
        ], dtype=precision)]

    elif gate_type == GateType.rxy:
        costh = np.cos(pargs[1] / 2)
        sinth = np.sin(pargs[1] / 2)

        grad1 = np.array([
            [costh, -np.exp(-1j * pargs[0]) * sinth],
            [np.exp(1j * pargs[0]) * sinth, costh]
        ], dtype=precision)

        grad2 = np.array([
            [-sinth / 2, -1j * np.exp(-1j * pargs[0]) * costh / 2],
            [-1j * np.exp(1j * pargs[0]) * costh / 2, -sinth / 2]
        ], dtype=precision)
        return [grad1, grad2]

    elif gate_type == GateType.xy:
        return [np.array([
            [0, -np.exp(-1j * pargs[0])],
            [np.exp(1j * pargs[0]), 0]
        ], dtype=precision)]

    elif gate_type == GateType.xy2p:
        return [np.array([
            [1 / np.sqrt(2), -1 / np.sqrt(2) * np.exp(-1j * pargs[0])],
            [1 / np.sqrt(2) * np.exp(1j * pargs[0]), 1 / np.sqrt(2)]
        ], dtype=precision)]

    elif gate_type == GateType.xy2m:
        return [np.array([
            [1 / np.sqrt(2), 1 / np.sqrt(2) * np.exp(-1j * pargs[0])],
            [-1 / np.sqrt(2) * np.exp(1j * pargs[0]), 1 / np.sqrt(2)]
        ], dtype=precision)]

    else:
        TypeError(gate_type)

matrix_with_param 

matrix_with_param(gate_type: GateType, gate_pargs: list, symbol_gate: bool, symbol_pargs: dict, precision: complex)

Return the Quantum Gates' matrix, which has parameters.

Parameters:

  • gate_type (GateType) –

    The type of Quantum Gate.

  • gate_pargs (List) –

    The Quantum Gate's parameters.

  • symbol_gate (bool) –

    Whether the Quantum Gate is a symbol gate.

  • symbol_pargs (dict) –

    The Quantum Gate's symbol parameters.

  • precision (complex) –

    The precision of Quantum Gate.

Returns:

  • –

    np.ndarray: The Quantum Gate's matrix.

Source code in QuICT/core/gate/utils/gate_matrix.py 
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
def matrix_with_param(
    self,
    gate_type: GateType,
    gate_pargs: list,
    symbol_gate: bool,
    symbol_pargs: dict,
    precision: complex,
):
    """ Return the Quantum Gates' matrix, which has parameters.

    Args:
        gate_type (GateType): The type of Quantum Gate.
        gate_pargs (List): The Quantum Gate's parameters.
        symbol_gate (bool): Whether the Quantum Gate is a symbol gate.
        symbol_pargs (dict): The Quantum Gate's symbol parameters.
        precision (complex): The precision of Quantum Gate.

    Returns:
        np.ndarray: The Quantum Gate's matrix.
    """
    pargs = []
    if symbol_gate:
        assert len(symbol_pargs) > 0, "The symbol gate must be assigned values before calculation."
        for i in range(len(gate_pargs)):
            pargs.append(symbol_pargs[gate_pargs[i]])
    else:
        pargs = gate_pargs

    if gate_type in [GateType.u1, GateType.cu1]:
        return np.array([
            [1, 0],
            [0, np.exp(1j * pargs[0])]
        ], dtype=precision)

    elif gate_type == GateType.u2:
        sqrt2 = 1 / np.sqrt(2)
        return np.array([
            [1 * sqrt2,
             -np.exp(1j * pargs[1]) * sqrt2],
            [np.exp(1j * pargs[0]) * sqrt2,
             np.exp(1j * (pargs[0] + pargs[1])) * sqrt2]
        ], dtype=precision)

    elif gate_type in [GateType.u3, GateType.cu3]:
        return np.array([
            [np.cos(pargs[0] / 2),
             -np.exp(1j * pargs[2]) * np.sin(pargs[0] / 2)],
            [np.exp(1j * pargs[1]) * np.sin(pargs[0] / 2),
             np.exp(1j * (pargs[1] + pargs[2])) * np.cos(pargs[0] / 2)]
        ], dtype=precision)

    elif gate_type == GateType.rx:
        cos_v = np.cos(pargs[0] / 2)
        sin_v = -np.sin(pargs[0] / 2)
        return np.array([
            [cos_v, 1j * sin_v],
            [1j * sin_v, cos_v]
        ], dtype=precision)

    elif gate_type in [GateType.ry, GateType.cry]:
        cos_v = np.cos(pargs[0] / 2)
        sin_v = np.sin(pargs[0] / 2)
        return np.array([
            [cos_v, -sin_v],
            [sin_v, cos_v]
        ], dtype=precision)

    elif gate_type in [GateType.rz, GateType.crz, GateType.ccrz]:
        return np.array([
            [np.exp(-pargs[0] / 2 * 1j), 0],
            [0, np.exp(pargs[0] / 2 * 1j)]
        ], dtype=precision)

    elif gate_type == GateType.phase:
        return np.array([
            [1, 0],
            [0, np.exp(pargs[0] * 1j)]
        ], dtype=precision)

    elif gate_type == GateType.gphase:
        return np.array([
            [np.exp(pargs[0] * 1j), 0],
            [0, np.exp(pargs[0] * 1j)]
        ], dtype=precision)

    elif gate_type == GateType.fsim:
        costh = np.cos(pargs[0])
        sinth = np.sin(pargs[0])
        phi = pargs[1]
        return np.array([
            [1, 0, 0, 0],
            [0, costh, -1j * sinth, 0],
            [0, -1j * sinth, costh, 0],
            [0, 0, 0, np.exp(-1j * phi)]
        ], dtype=precision)

    elif gate_type == GateType.rxx:
        costh = np.cos(pargs[0] / 2)
        sinth = np.sin(pargs[0] / 2)

        return np.array([
            [costh, 0, 0, -1j * sinth],
            [0, costh, -1j * sinth, 0],
            [0, -1j * sinth, costh, 0],
            [-1j * sinth, 0, 0, costh]
        ], dtype=precision)

    elif gate_type == GateType.ryy:
        costh = np.cos(pargs[0] / 2)
        sinth = np.sin(pargs[0] / 2)

        return np.array([
            [costh, 0, 0, 1j * sinth],
            [0, costh, -1j * sinth, 0],
            [0, -1j * sinth, costh, 0],
            [1j * sinth, 0, 0, costh]
        ], dtype=precision)

    elif gate_type == GateType.rzz:
        expth = np.exp(0.5j * pargs[0])
        sexpth = np.exp(-0.5j * pargs[0])

        return np.array([
            [sexpth, 0, 0, 0],
            [0, expth, 0, 0],
            [0, 0, expth, 0],
            [0, 0, 0, sexpth]
        ], dtype=precision)

    elif gate_type == GateType.rzx:
        costh = np.cos(pargs[0] / 2)
        sinth = np.sin(pargs[0] / 2)

        return np.array([
            [costh, -1j * sinth, 0, 0],
            [-1j * sinth, costh, 0, 0],
            [0, 0, costh, 1j * sinth],
            [0, 0, 1j * sinth, costh]
        ], dtype=precision)

    elif gate_type == GateType.rxy:
        costh = np.cos(pargs[1] / 2)
        sinth = np.sin(pargs[1] / 2)

        return np.array([
            [costh, -1j * np.exp(-1j * pargs[0]) * sinth],
            [-1j * np.exp(1j * pargs[0]) * sinth, costh]
        ], dtype=precision)

    elif gate_type == GateType.xy:
        return np.array([
            [0, -1j * np.exp(-1j * pargs[0])],
            [-1j * np.exp(1j * pargs[0]), 0]
        ], dtype=precision)

    elif gate_type == GateType.xy2p:
        return np.array([
            [1 / np.sqrt(2), -1j / np.sqrt(2) * np.exp(-1j * pargs[0])],
            [-1j / np.sqrt(2) * np.exp(1j * pargs[0]), 1 / np.sqrt(2)]
        ], dtype=precision)

    elif gate_type == GateType.xy2m:
        return np.array([
            [1 / np.sqrt(2), 1j / np.sqrt(2) * np.exp(-1j * pargs[0])],
            [1j / np.sqrt(2) * np.exp(1j * pargs[0]), 1 / np.sqrt(2)]
        ], dtype=precision)

    else:
        TypeError(gate_type)

QuICT.core.gate.utils.ComplexGateBuilder

The class of all build_gate functions for BasicGate.

build_gate classmethod 

build_gate(gate_type, parg, gate_matrix=None)

Gate Decomposition, divided the current gate with a set of small gates

Parameters:

  • gate_type (GateType) –

    The type of Quantum Gate.

  • parg (list) –

    The parameters of Quantum Gate.

  • gate_matrix (_type_, default: None ) –

    The matrix of Quantum Gate, only use for CU3. Defaults to None.

Returns:

  • List –

    List of gate_info(gate_type, qubit_index, parameters)

Source code in QuICT/core/gate/utils/gate_matrix.py 
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
@classmethod
def build_gate(cls, gate_type, parg, gate_matrix=None):
    """ Gate Decomposition, divided the current gate with a set of small gates

    Args:
        gate_type (GateType): The type of Quantum Gate.
        parg (list): The parameters of Quantum Gate.
        gate_matrix (_type_, optional): The matrix of Quantum Gate, only use for CU3. Defaults to None.

    Returns:
        List: List of gate_info(gate_type, qubit_index, parameters)
    """
    if gate_type == GateType.cu3:
        cgate = cls.build_unitary(gate_matrix)
    elif gate_type == GateType.cu1:
        cgate = cls.build_cu1(parg[0])
    elif gate_type == GateType.swap:
        cgate = cls.build_swap()
    elif gate_type == GateType.ccx:
        cgate = cls.build_ccx()
    elif gate_type == GateType.ccz:
        cgate = cls.build_ccz()
    elif gate_type == GateType.ccrz:
        cgate = cls.build_ccrz(parg[0])
    elif gate_type == GateType.cswap:
        cgate = cls.build_cswap()
    elif gate_type == GateType.iswap:
        cgate = cls.build_iswap()
    elif gate_type == GateType.iswapdg:
        cgate = cls.build_iswapdg()
    elif gate_type == GateType.sqiswap:
        cgate = cls.build_sqiswap()
    elif gate_type == GateType.rccx:
        cgate = cls.build_rccx()
    elif gate_type == GateType.rxy:
        cgate = cls.build_rxy(parg)
    elif gate_type in [GateType.xy, GateType.xy2p, GateType.xy2m]:
        cgate = cls.build_xy(gate_type, parg[0])
    else:
        return None

    return cgate

QuICT.core.gate.utils.InverseGate

The class of all Inverse functions for Quantum Gate.

get_inverse_gate classmethod 

get_inverse_gate(gate_type: GateType, pargs: list) -> tuple

Get Inverse Quantum Gate Information.

Parameters:

  • gate_type (GateType) –

    The type of Quantum Gate.

  • pargs (list) –

    The parameters of Quantum Gate.

Returns:

  • tuple ( tuple ) –

    The inverse gate info.

Source code in QuICT/core/gate/utils/gate_matrix.py 
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
@classmethod
def get_inverse_gate(cls, gate_type: GateType, pargs: list) -> tuple:
    """ Get Inverse Quantum Gate Information.

    Args:
        gate_type (GateType): The type of Quantum Gate.
        pargs (list): The parameters of Quantum Gate.

    Returns:
        tuple: The inverse gate info.
    """
    if len(pargs) == 0:
        if gate_type in cls.__GATE_INVERSE_MAP.keys():
            inverse_args = cls.__GATE_INVERSE_MAP[gate_type]

            return inverse_args if isinstance(inverse_args, tuple) else (inverse_args, pargs)
    else:
        inv_params = None
        if gate_type in cls.__INVERSE_GATE_WITH_NEGATIVE_PARAMS:
            inv_params = [p * -1 for p in pargs]
        elif gate_type == GateType.u2:
            inv_params = [np.pi - pargs[1], np.pi - pargs[0]]
        elif gate_type in [GateType.u3, GateType.cu3]:
            inv_params = [pargs[0], np.pi - pargs[2], np.pi - pargs[1]]
        elif gate_type in [GateType.xy, GateType.xy2p, GateType.xy2m]:
            inv_params = [pargs[0] + np.pi]
        elif gate_type == GateType.rxy:
            inv_params = [pargs[0] + np.pi, pargs[1]]

        if inv_params is not None:
            return (gate_type, inv_params)

    return None, pargs