6893865468

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30.16

/waveforms/qlisp/simulator/__init__.py

import itertools
from functools import partial, reduce

import numpy as np

from waveforms.math.matricies import (CR, CX, CZ, SWAP, H, S, Sdag, SQiSWAP, T,
                                      Tdag, U, fSim, iSWAP, make_immutable,
                                      rfUnitary, sigmaI, sigmaX, sigmaY,
                                      sigmaZ)

try:
    import tensornetwork as tn
except ImportError:
    tn = None

__matrix_of_gates = {}


def regesterGateMatrix(gate, mat, N=None):
    if isinstance(mat, np.ndarray):
        mat = make_immutable(mat)
    if N is None:
        N = int(np.log2(mat.shape[0]))
    __matrix_of_gates[gate] = (mat, N)


def gate2mat(gate):
    if isinstance(gate, str) and gate in __matrix_of_gates:
        return __matrix_of_gates[gate][0]
    elif isinstance(gate, tuple) and gate[0] in __matrix_of_gates:
        if callable(__matrix_of_gates[gate[0]][0]):
            return __matrix_of_gates[gate[0]][0](*gate[1:])
        else:
            raise ValueError(
                f"Could not call {gate[0]}(*{gate[1:]}), `{gate[0]}` is not callable."
            )
    elif isinstance(gate, tuple) and gate[0] == 'C':
        U = gate2mat(gate[1])
        ret = np.eye(2 * U.shape[0], dtype=np.complex)
        ret[U.shape[0]:, U.shape[0]:] = U
        return ret
    else:
        raise ValueError(f'Unexcept gate {gate}')


def splite_at(l, bits):
    """将 l 的二进制位于 bits 所列的位置上断开插上0
    
    如 splite_at(int('1111',2), [0,2,4,6]) == int('10101010', 2)
    bits 必须从小到大排列
    """
    r = l
    for n in bits:
        mask = (1 << n) - 1
        low = r & mask
        high = r - low
        r = (high << 1) + low
    return r


def place_at(l, bits):
    """将 l 的二进制位置于 bits 所列的位置上
    
    如 place_at(int('10111',2), [0,2,4,5,6]) == int('01010101', 2)
    """
    r = 0
    for index, n in enumerate(bits):
        b = (l >> index) & 1
        r += b << n
    return r


def reduceSubspace(targets, N, inputMat, func, args):
    innerDim = 2**len(targets)
    outerDim = 2**(N - len(targets))

    targets = tuple(reversed([N - i - 1 for i in targets]))

    def index(targets, i, j):
        return splite_at(j, sorted(targets)) | place_at(i, targets)

    if len(inputMat.shape) == 1:
        for k in range(outerDim):
            innerIndex = [index(targets, i, k) for i in range(innerDim)]
            inputMat[innerIndex] = func(inputMat[innerIndex], *args)
    else:
        for k, l in itertools.product(range(outerDim), repeat=2):
            innerIndex = np.asarray(
                [[index(targets, i, k),
                  index(targets, j, l)]
                 for i, j in itertools.product(range(innerDim), repeat=2)]).T
            sub = inputMat[innerIndex[0], innerIndex[1]].reshape(
                (innerDim, innerDim))
            inputMat[innerIndex[0], innerIndex[1]] = func(sub, *args).flatten()
    return inputMat


def applySeq(seq, psi0=None):
    if psi0 is None:
        psi = np.array([1, 0], dtype=complex)
        N = 1
    else:
        psi = psi0
        N = int(np.round(np.log2(psi.shape[0])))

    if len(psi.shape) == 1:
        I = np.array([1, 0])
    else:
        I = np.eye(2)

    def func(psi, U):
        return U @ psi

    for gate, qubits in seq:
        if (isinstance(gate, tuple) and gate[0] in ['Measure', 'Delay']) or (
                isinstance(gate, str) and gate in ['Barrier']):
            continue
        if isinstance(qubits, tuple):
            M = max(qubits)
        else:
            M = qubits
            qubits = (qubits, )
        if M >= N:
            psi = reduce(np.kron, itertools.repeat(I, times=M - N + 1), psi)
            N = M + 1

        reduceSubspace(qubits, N, psi, func, (gate2mat(gate), ))

    return psi


def seq2mat(seq):
    return applySeq(seq, np.eye(2, dtype=complex))


def apply_gate(qubit_edges, gate, operating_qubits):
    if tn is None:
        raise ImportError('Please install tensornetwork first.')

    op = tn.Node(gate)
    for i, bit in enumerate(operating_qubits):
        tn.connect(qubit_edges[bit], op[i])
        qubit_edges[bit] = op[i + len(operating_qubits)]


def circuit_network(circ):
    if tn is None:
        raise ImportError('Please install tensornetwork first.')

    N = 0

    all_nodes = []

    with tn.NodeCollection(all_nodes):
        left_edges = []
        right_edges = []

        for gate, qubits in circ:
            if (isinstance(gate, tuple)
                    and gate[0] in ['Measure', 'Delay']) or (isinstance(
                        gate, str) and gate in ['Barrier']):
                continue
            if isinstance(qubits, tuple):
                M = max(qubits)
            else:
                M = qubits
                qubits = (qubits, )
            if M >= N:
                new_nodes = [tn.Node(np.eye(2)) for _ in range(M - N + 1)]
                left_edges.extend([node[0] for node in new_nodes])
                right_edges.extend([node[1] for node in new_nodes])
                N = M + 1

            gate_mat = gate2mat(gate)
            gate_tenser = gate_mat.reshape((2, 2) * len(qubits))
            apply_gate(left_edges, gate_tenser, qubits)

    return all_nodes, left_edges, right_edges, N


def apply_circuit(circ, qubits=None, init_state=None):
    if tn is None:
        raise ImportError('Please install tensornetwork first.')

    all_nodes, left_edges, right_edges, N = circuit_network(circ)
    if qubits is not None:
        assert len(qubits) == N
    else:
        qubits = list(range(N))
    if init_state is None:
        init_state_nodes = [
            tn.Node(np.array([1, 0], dtype=complex)) for i in range(N)
        ]
        init_state_edges = [init_state_nodes[i][0] for i in range(N)]
    else:
        init_state_nodes, init_state_edges = init_state

    all_nodes.extend(init_state_nodes)
    qubit_edges = [left_edges[q] for q in qubits]
    for i, q in enumerate(qubits):
        tn.connect(right_edges[i], init_state_edges[q])
    result = tn.contractors.optimal(all_nodes, output_edge_order=qubit_edges)
    return result.tensor.reshape(-1)


def circuit2mat(circ):
    if tn is None:
        raise ImportError('Please install tensornetwork first.')

    all_nodes, left_edges, right_edges, N = circuit_network(circ)
    result = tn.contractors.optimal(all_nodes,
                                    output_edge_order=left_edges + right_edges)
    return result.tensor.reshape((2**N, 2**N))


regesterGateMatrix('U', U, 1)
regesterGateMatrix('P', lambda p: U(theta=0, phi=0, lambda_=p), 1)
regesterGateMatrix('rfUnitary', rfUnitary, 1)
regesterGateMatrix('Rx', partial(rfUnitary, phi=0), 1)
regesterGateMatrix('Ry', partial(rfUnitary, phi=np.pi / 2), 1)
regesterGateMatrix('Rz', lambda p: U(theta=0, phi=0, lambda_=p), 1)
regesterGateMatrix('fSim', fSim, 2)

# one qubit
regesterGateMatrix('I', sigmaI())
regesterGateMatrix('X', -1j * sigmaX())
regesterGateMatrix('Y', -1j * sigmaY())
regesterGateMatrix('X/2', np.array([[1, -1j], [-1j, 1]]) / np.sqrt(2))
regesterGateMatrix('Y/2', np.array([[1, -1], [1, 1]]) / np.sqrt(2))
regesterGateMatrix('-X/2', np.array([[1, 1j], [1j, 1]]) / np.sqrt(2))
regesterGateMatrix('-Y/2', np.array([[1, 1], [-1, 1]]) / np.sqrt(2))
regesterGateMatrix('Z', sigmaZ())
regesterGateMatrix('S', S)
regesterGateMatrix('-S', Sdag)
regesterGateMatrix('H', H)

# non-clifford
regesterGateMatrix('T', T)
regesterGateMatrix('-T', Tdag)
regesterGateMatrix('W/2', rfUnitary(np.pi / 2, np.pi / 4))
regesterGateMatrix('-W/2', rfUnitary(-np.pi / 2, np.pi / 4))
regesterGateMatrix('V/2', rfUnitary(np.pi / 2, 3 * np.pi / 4))
regesterGateMatrix('-V/2', rfUnitary(-np.pi / 2, 3 * np.pi / 4))

# two qubits
regesterGateMatrix('CZ', CZ)
regesterGateMatrix('Cnot', CX)
regesterGateMatrix('CX', CX)
regesterGateMatrix('iSWAP', iSWAP)
regesterGateMatrix('SWAP', SWAP)
regesterGateMatrix('CR', CR)

# non-clifford
regesterGateMatrix('SQiSWAP', SQiSWAP)

if __name__ == '__main__':
    # Porter-Thomas distribution

    def randomSeq(depth, N):
        seq = []
        for i in range(depth):
            for j in range(N):
                seq.append((np.random.choice(['X/2', 'Y/2', 'W/2']), j))
            for j in range(i % 2, N, 2):
                seq.append(('SQiSWAP', (j, (j + 1) % N)))
        return seq

    p = []
    # run 1000 random circuit on 6 qubits
    for i in range(1000):
        print('    ', i, end='')
        seq = randomSeq(50, 6)
        psi = applySeq(seq)
        p.extend(list(np.abs(psi)**2))
        print('    ', i)
    p = np.asarray(p)

    # plot distribution of probabilities
    N = 2**6
    y, x = np.histogram(N * p, bins=50, density=True)

    import matplotlib.pyplot as plt

    plt.semilogy((x[:-1] + x[1:]) / 2, y)
    plt.show()

1	import itertools	9✔
2	from functools import partial, reduce	9✔
3
4	import numpy as np	9✔
5
6	from waveforms.math.matricies import (CR, CX, CZ, SWAP, H, S, Sdag, SQiSWAP, T,	9✔
7	Tdag, U, fSim, iSWAP, make_immutable,
8	rfUnitary, sigmaI, sigmaX, sigmaY,
9	sigmaZ)
10
11	try:	9✔
12	import tensornetwork as tn	9✔
13	except ImportError:	9✔
14	tn = None	9✔
15
16	__matrix_of_gates = {}	9✔
17
18
19	def regesterGateMatrix(gate, mat, N=None):	9✔
20	if isinstance(mat, np.ndarray):	9✔
21	mat = make_immutable(mat)	9✔
22	if N is None:	9✔
23	N = int(np.log2(mat.shape[0]))	9✔
24	__matrix_of_gates[gate] = (mat, N)	9✔
25
26
27	def gate2mat(gate):	9✔
28	if isinstance(gate, str) and gate in __matrix_of_gates:	×
29	return __matrix_of_gates[gate][0]	×
UNCOV 30	elif isinstance(gate, tuple) and gate[0] in __matrix_of_gates:	×
31	if callable(__matrix_of_gates[gate[0]][0]):	×
UNCOV 32	return __matrix_of_gates[gate[0]][0](*gate[1:])	×
33	else:
34	raise ValueError(	×
35	f"Could not call {gate[0]}(*{gate[1:]}), `{gate[0]}` is not callable."
36	)
37	elif isinstance(gate, tuple) and gate[0] == 'C':	×
38	U = gate2mat(gate[1])	×
UNCOV 39	ret = np.eye(2 * U.shape[0], dtype=np.complex)	×
40	ret[U.shape[0]:, U.shape[0]:] = U	×
UNCOV 41	return ret	×
42	else:
UNCOV 43	raise ValueError(f'Unexcept gate {gate}')	×
44
45
46	def splite_at(l, bits):	9✔
47	"""将 l 的二进制位于 bits 所列的位置上断开插上0
48
49	如 splite_at(int('1111',2), [0,2,4,6]) == int('10101010', 2)
50	bits 必须从小到大排列
51	"""
52	r = l	×
53	for n in bits:	×
54	mask = (1 << n) - 1	×
55	low = r & mask	×
UNCOV 56	high = r - low	×
UNCOV 57	r = (high << 1) + low	×
UNCOV 58	return r	×
59
60
61	def place_at(l, bits):	9✔
62	"""将 l 的二进制位置于 bits 所列的位置上
63
64	如 place_at(int('10111',2), [0,2,4,5,6]) == int('01010101', 2)
65	"""
66	r = 0	×
67	for index, n in enumerate(bits):	×
UNCOV 68	b = (l >> index) & 1	×
UNCOV 69	r += b << n	×
UNCOV 70	return r	×
71
72
73	def reduceSubspace(targets, N, inputMat, func, args):	9✔
74	innerDim = 2**len(targets)	×
UNCOV 75	outerDim = 2**(N - len(targets))	×
76
77	targets = tuple(reversed([N - i - 1 for i in targets]))	×
78
79	def index(targets, i, j):	×
80	return splite_at(j, sorted(targets)) \| place_at(i, targets)	×
81
82	if len(inputMat.shape) == 1:	×
UNCOV 83	for k in range(outerDim):	×
84	innerIndex = [index(targets, i, k) for i in range(innerDim)]	×
85	inputMat[innerIndex] = func(inputMat[innerIndex], *args)	×
86	else:
UNCOV 87	for k, l in itertools.product(range(outerDim), repeat=2):	×
UNCOV 88	innerIndex = np.asarray(	×
89	[[index(targets, i, k),
90	index(targets, j, l)]
91	for i, j in itertools.product(range(innerDim), repeat=2)]).T
92	sub = inputMat[innerIndex[0], innerIndex[1]].reshape(	×
93	(innerDim, innerDim))
UNCOV 94	inputMat[innerIndex[0], innerIndex[1]] = func(sub, *args).flatten()	×
UNCOV 95	return inputMat	×
96
97
98	def applySeq(seq, psi0=None):	9✔
UNCOV 99	if psi0 is None:	×
100	psi = np.array([1, 0], dtype=complex)	×
101	N = 1	×
102	else:
103	psi = psi0	×
104	N = int(np.round(np.log2(psi.shape[0])))	×
105
106	if len(psi.shape) == 1:	×
UNCOV 107	I = np.array([1, 0])	×
108	else:
109	I = np.eye(2)	×
110
111	def func(psi, U):	×
112	return U @ psi	×
113
114	for gate, qubits in seq:	×
115	if (isinstance(gate, tuple) and gate[0] in ['Measure', 'Delay']) or (	×
116	isinstance(gate, str) and gate in ['Barrier']):
UNCOV 117	continue	×
118	if isinstance(qubits, tuple):	×
119	M = max(qubits)	×
120	else:
121	M = qubits	×
122	qubits = (qubits, )	×
UNCOV 123	if M >= N:	×
124	psi = reduce(np.kron, itertools.repeat(I, times=M - N + 1), psi)	×
UNCOV 125	N = M + 1	×
126
UNCOV 127	reduceSubspace(qubits, N, psi, func, (gate2mat(gate), ))	×
128
UNCOV 129	return psi	×
130
131
132	def seq2mat(seq):	9✔
UNCOV 133	return applySeq(seq, np.eye(2, dtype=complex))	×
134
135
136	def apply_gate(qubit_edges, gate, operating_qubits):	9✔
137	if tn is None:	×
138	raise ImportError('Please install tensornetwork first.')	×
139
140	op = tn.Node(gate)	×
UNCOV 141	for i, bit in enumerate(operating_qubits):	×
UNCOV 142	tn.connect(qubit_edges[bit], op[i])	×
UNCOV 143	qubit_edges[bit] = op[i + len(operating_qubits)]	×
144
145
146	def circuit_network(circ):	9✔
147	if tn is None:	×
UNCOV 148	raise ImportError('Please install tensornetwork first.')	×
149
UNCOV 150	N = 0	×
151
152	all_nodes = []	×
153
UNCOV 154	with tn.NodeCollection(all_nodes):	×
155	left_edges = []	×
156	right_edges = []	×
157
UNCOV 158	for gate, qubits in circ:	×
159	if (isinstance(gate, tuple)	×
160	and gate[0] in ['Measure', 'Delay']) or (isinstance(
161	gate, str) and gate in ['Barrier']):
UNCOV 162	continue	×
163	if isinstance(qubits, tuple):	×
164	M = max(qubits)	×
165	else:
166	M = qubits	×
167	qubits = (qubits, )	×
168	if M >= N:	×
169	new_nodes = [tn.Node(np.eye(2)) for _ in range(M - N + 1)]	×
UNCOV 170	left_edges.extend([node[0] for node in new_nodes])	×
171	right_edges.extend([node[1] for node in new_nodes])	×
172	N = M + 1	×
173
UNCOV 174	gate_mat = gate2mat(gate)	×
175	gate_tenser = gate_mat.reshape((2, 2) * len(qubits))	×
UNCOV 176	apply_gate(left_edges, gate_tenser, qubits)	×
177
UNCOV 178	return all_nodes, left_edges, right_edges, N	×
179
180
181	def apply_circuit(circ, qubits=None, init_state=None):	9✔
182	if tn is None:	×
183	raise ImportError('Please install tensornetwork first.')	×
184
UNCOV 185	all_nodes, left_edges, right_edges, N = circuit_network(circ)	×
186	if qubits is not None:	×
187	assert len(qubits) == N	×
188	else:
UNCOV 189	qubits = list(range(N))	×
UNCOV 190	if init_state is None:	×
191	init_state_nodes = [	×
192	tn.Node(np.array([1, 0], dtype=complex)) for i in range(N)
193	]
UNCOV 194	init_state_edges = [init_state_nodes[i][0] for i in range(N)]	×
195	else:
196	init_state_nodes, init_state_edges = init_state	×
197
198	all_nodes.extend(init_state_nodes)	×
199	qubit_edges = [left_edges[q] for q in qubits]	×
200	for i, q in enumerate(qubits):	×
UNCOV 201	tn.connect(right_edges[i], init_state_edges[q])	×
UNCOV 202	result = tn.contractors.optimal(all_nodes, output_edge_order=qubit_edges)	×
UNCOV 203	return result.tensor.reshape(-1)	×
204
205
206	def circuit2mat(circ):	9✔
207	if tn is None:	×
208	raise ImportError('Please install tensornetwork first.')	×
209
210	all_nodes, left_edges, right_edges, N = circuit_network(circ)	×
UNCOV 211	result = tn.contractors.optimal(all_nodes,	×
212	output_edge_order=left_edges + right_edges)
UNCOV 213	return result.tensor.reshape((2N, 2N))	×
214
215
216	regesterGateMatrix('U', U, 1)	9✔
217	regesterGateMatrix('P', lambda p: U(theta=0, phi=0, lambda_=p), 1)	9✔
218	regesterGateMatrix('rfUnitary', rfUnitary, 1)	9✔
219	regesterGateMatrix('Rx', partial(rfUnitary, phi=0), 1)	9✔
220	regesterGateMatrix('Ry', partial(rfUnitary, phi=np.pi / 2), 1)	9✔
221	regesterGateMatrix('Rz', lambda p: U(theta=0, phi=0, lambda_=p), 1)	9✔
222	regesterGateMatrix('fSim', fSim, 2)	9✔
223
224	# one qubit
225	regesterGateMatrix('I', sigmaI())	9✔
226	regesterGateMatrix('X', -1j * sigmaX())	9✔
227	regesterGateMatrix('Y', -1j * sigmaY())	9✔
228	regesterGateMatrix('X/2', np.array([[1, -1j], [-1j, 1]]) / np.sqrt(2))	9✔
229	regesterGateMatrix('Y/2', np.array([[1, -1], [1, 1]]) / np.sqrt(2))	9✔
230	regesterGateMatrix('-X/2', np.array([[1, 1j], [1j, 1]]) / np.sqrt(2))	9✔
231	regesterGateMatrix('-Y/2', np.array([[1, 1], [-1, 1]]) / np.sqrt(2))	9✔
232	regesterGateMatrix('Z', sigmaZ())	9✔
233	regesterGateMatrix('S', S)	9✔
234	regesterGateMatrix('-S', Sdag)	9✔
235	regesterGateMatrix('H', H)	9✔
236
237	# non-clifford
238	regesterGateMatrix('T', T)	9✔
239	regesterGateMatrix('-T', Tdag)	9✔
240	regesterGateMatrix('W/2', rfUnitary(np.pi / 2, np.pi / 4))	9✔
241	regesterGateMatrix('-W/2', rfUnitary(-np.pi / 2, np.pi / 4))	9✔
242	regesterGateMatrix('V/2', rfUnitary(np.pi / 2, 3 * np.pi / 4))	9✔
243	regesterGateMatrix('-V/2', rfUnitary(-np.pi / 2, 3 * np.pi / 4))	9✔
244
245	# two qubits
246	regesterGateMatrix('CZ', CZ)	9✔
247	regesterGateMatrix('Cnot', CX)	9✔
248	regesterGateMatrix('CX', CX)	9✔
249	regesterGateMatrix('iSWAP', iSWAP)	9✔
250	regesterGateMatrix('SWAP', SWAP)	9✔
251	regesterGateMatrix('CR', CR)	9✔
252
253	# non-clifford
254	regesterGateMatrix('SQiSWAP', SQiSWAP)	9✔
255
256	if __name__ == '__main__':	9✔
257	# Porter-Thomas distribution
258
UNCOV 259	def randomSeq(depth, N):	×
UNCOV 260	seq = []	×
UNCOV 261	for i in range(depth):	×
UNCOV 262	for j in range(N):	×
UNCOV 263	seq.append((np.random.choice(['X/2', 'Y/2', 'W/2']), j))	×
UNCOV 264	for j in range(i % 2, N, 2):	×
UNCOV 265	seq.append(('SQiSWAP', (j, (j + 1) % N)))	×
UNCOV 266	return seq	×
267
268	p = []	×
269	# run 1000 random circuit on 6 qubits
270	for i in range(1000):	×
271	print(' ', i, end='')	×
272	seq = randomSeq(50, 6)	×
273	psi = applySeq(seq)	×
274	p.extend(list(np.abs(psi)**2))	×
275	print(' ', i)	×
UNCOV 276	p = np.asarray(p)	×
277
278	# plot distribution of probabilities
279	N = 2**6	×
280	y, x = np.histogram(N * p, bins=50, density=True)	×
281
282	import matplotlib.pyplot as plt	×
283
284	plt.semilogy((x[:-1] + x[1:]) / 2, y)	×
285	plt.show()	×

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