def density_matrix_call(self, state):
state = self.gate_op(state, self.qubits_tensor_dm, 2 * self.nqubits,
*self.target_qubits, get_threads())
matrix = K.conj(matrices.Y)
state = K.op.apply_gate(state, matrix, self.qubits_tensor,
2 * self.nqubits, *self.target_qubits_dm,
get_threads())
return state
def density_matrix_call(self, state):
state = self.gate_op(
state,
self.matrix,
self.qubits_tensor_dm, # pylint: disable=E1121
2 * self.nqubits,
*self.target_qubits,
get_threads())
adjmatrix = K.conj(self.matrix)
state = self.gate_op(state, adjmatrix, self.qubits_tensor,
2 * self.nqubits, *self.target_qubits_dm,
get_threads())
return state
def test_unbalanced_probabilistic_measurement(backend, use_samples):
original_backend = qibo.get_backend()
original_threads = qibo.get_threads()
qibo.set_backend(backend)
# set single-thread to fix the random values generated from the frequency custom op
qibo.set_threads(1)
state = np.array([1, 1, 1, np.sqrt(3)]) / np.sqrt(6)
c = models.Circuit(2)
c.add(gates.Flatten(state))
c.add(gates.M(0, 1))
result = c(nshots=1000)
K.set_seed(1234)
if use_samples:
# calculates sample tensor directly using `tf.random.categorical`
# otherwise it uses the frequency-only calculation
_ = result.samples()
# update reference values based on backend and device
if K.name == "tensorflow":
if K.gpu_devices: # pragma: no cover
# CI does not use GPU
decimal_frequencies = {0: 196, 1: 153, 2: 156, 3: 495}
else:
decimal_frequencies = {0: 168, 1: 188, 2: 154, 3: 490}
elif K.name == "numpy":
decimal_frequencies = {0: 171, 1: 148, 2: 161, 3: 520}
assert sum(result.frequencies().values()) == 1000
assert_result(result, decimal_frequencies=decimal_frequencies)
qibo.set_backend(original_backend)
qibo.set_threads(original_threads)
def main(nqubits,
nlayers,
backend,
varlayer=False,
method="Powell",
maxiter=None,
filename=None):
"""Performs a VQE circuit minimization test."""
qibo.set_backend(backend)
logs = BenchmarkLogger(filename)
logs.append({
"nqubits": nqubits,
"nlayers": nlayers,
"varlayer": varlayer,
"backend": qibo.get_backend(),
"precision": qibo.get_precision(),
"device": qibo.get_device(),
"threads": qibo.get_threads(),
"method": method,
"maxiter": maxiter
})
print("Number of qubits:", nqubits)
print("Number of layers:", nlayers)
print("Backend:", logs[-1]["backend"])
start_time = time.time()
if varlayer:
circuit = varlayer_circuit(nqubits, nlayers)
else:
circuit = standard_circuit(nqubits, nlayers)
hamiltonian = hamiltonians.XXZ(nqubits=nqubits)
vqe = models.VQE(circuit, hamiltonian)
logs[-1]["creation_time"] = time.time() - start_time
target = np.real(np.min(K.to_numpy(hamiltonian.eigenvalues())))
print("\nTarget state =", target)
np.random.seed(0)
nparams = 2 * nqubits * nlayers + nqubits
initial_parameters = np.random.uniform(0, 2 * np.pi, nparams)
start_time = time.time()
options = {'disp': False, 'maxiter': maxiter}
best, params, _ = vqe.minimize(initial_parameters,
method=method,
options=options,
compile=False)
logs[-1]["minimization_time"] = time.time() - start_time
epsilon = np.log10(1 / np.abs(best - target))
print("Found state =", best)
print("Final eps =", epsilon)
logs[-1]["best_energy"] = float(best)
logs[-1]["epsilon_energy"] = float(epsilon)
print("\nCreation time =", logs[-1]["creation_time"])
print("Minimization time =", logs[-1]["minimization_time"])
print("Total time =",
logs[-1]["minimization_time"] + logs[-1]["creation_time"])
logs.dump()
def test_parallel_circuit_evaluation(backend,
skip_parallel): # pragma: no cover
"""Evaluate circuit for multiple input states."""
device = qibo.get_device()
backend_name = qibo.get_backend()
if skip_parallel:
pytest.skip("Skipping parallel test.")
if not is_parallel_supported(backend_name):
pytest.skip("Skipping parallel test due to unsupported configuration.")
original_threads = qibo.get_threads()
qibo.set_threads(1)
nqubits = 10
np.random.seed(0)
c = QFT(nqubits)
states = [np.random.random(2**nqubits) for i in range(5)]
r1 = []
for state in states:
r1.append(c(state))
r2 = parallel_execution(c, states=states, processes=2)
np.testing.assert_allclose(r1, r2)
qibo.set_threads(original_threads)
def test_probabilistic_measurement(backend, accelerators, use_samples):
original_backend = qibo.get_backend()
original_threads = qibo.get_threads()
qibo.set_backend(backend)
# set single-thread to fix the random values generated from the frequency custom op
qibo.set_threads(1)
c = models.Circuit(4, accelerators)
c.add(gates.H(0))
c.add(gates.H(1))
c.add(gates.M(0, 1))
result = c(nshots=1000)
K.set_seed(1234)
if use_samples:
# calculates sample tensor directly using `tf.random.categorical`
# otherwise it uses the frequency-only calculation
_ = result.samples()
# update reference values based on backend and device
if K.name == "tensorflow":
if K.gpu_devices: # pragma: no cover
# CI does not use GPU
decimal_frequencies = {0: 273, 1: 233, 2: 242, 3: 252}
else:
decimal_frequencies = {0: 271, 1: 239, 2: 242, 3: 248}
elif K.name == "numpy":
decimal_frequencies = {0: 249, 1: 231, 2: 253, 3: 267}
assert sum(result.frequencies().values()) == 1000
assert_result(result, decimal_frequencies=decimal_frequencies)
qibo.set_backend(original_backend)
qibo.set_threads(original_threads)
def test_parallel_parametrized_circuit():
"""Evaluate circuit for multiple parameters."""
if 'GPU' in get_device(): # pragma: no cover
pytest.skip("unsupported configuration")
original_threads = get_threads()
set_threads(1)
nqubits = 5
nlayers = 10
c = Circuit(nqubits)
for l in range(nlayers):
c.add((gates.RY(q, theta=0) for q in range(nqubits)))
c.add((gates.CZ(q, q + 1) for q in range(0, nqubits - 1, 2)))
c.add((gates.RY(q, theta=0) for q in range(nqubits)))
c.add((gates.CZ(q, q + 1) for q in range(1, nqubits - 2, 2)))
c.add(gates.CZ(0, nqubits - 1))
c.add((gates.RY(q, theta=0) for q in range(nqubits)))
size = len(c.get_parameters())
np.random.seed(0)
parameters = [np.random.uniform(0, 2 * np.pi, size) for i in range(10)]
state = None
r1 = []
for params in parameters:
c.set_parameters(params)
r1.append(c(state))
r2 = parallel_parametrized_execution(c,
parameters=parameters,
initial_state=state,
processes=2)
np.testing.assert_allclose(r1, r2)
set_threads(original_threads)
def state_vector_call(self, state):
return self.gate_op(
state,
self.matrix,
self.qubits_tensor, # pylint: disable=E1121
self.nqubits,
*self.target_qubits,
get_threads())
def main(nqubits,
nangles,
dense=True,
solver="exp",
method="Powell",
maxiter=None,
filename=None):
"""Performs a QAOA minimization test."""
print("Number of qubits:", nqubits)
print("Number of angles:", nangles)
logs = BenchmarkLogger(filename)
logs.append({
"nqubits": nqubits,
"nangles": nangles,
"dense": dense,
"solver": solver,
"backend": qibo.get_backend(),
"precision": qibo.get_precision(),
"device": qibo.get_device(),
"threads": qibo.get_threads(),
"method": method,
"maxiter": maxiter
})
hamiltonian = hamiltonians.XXZ(nqubits=nqubits, dense=dense)
start_time = time.time()
qaoa = models.QAOA(hamiltonian, solver=solver)
logs[-1]["creation_time"] = time.time() - start_time
target = np.real(np.min(K.to_numpy(hamiltonian.eigenvalues())))
print("\nTarget state =", target)
np.random.seed(0)
initial_parameters = np.random.uniform(0, 0.1, nangles)
start_time = time.time()
options = {'disp': True, 'maxiter': maxiter}
best, params, _ = qaoa.minimize(initial_parameters,
method=method,
options=options)
logs[-1]["minimization_time"] = time.time() - start_time
logs[-1]["best_energy"] = float(best)
logs[-1]["target_energy"] = float(target)
logs[-1]["epsilon"] = np.log10(1 / np.abs(best - target))
print("Found state =", best)
print("Final eps =", logs[-1]["epsilon"])
print("\nCreation time =", logs[-1]["creation_time"])
print("Minimization time =", logs[-1]["minimization_time"])
print("Total time =",
logs[-1]["creation_time"] + logs[-1]["minimization_time"])
logs.dump()
def test_aavqe(backend, method, options, compile, filename, skip_parallel):
"""Performs a AAVQE circuit minimization test."""
original_threads = qibo.get_threads()
if method == 'parallel_L-BFGS-B': # pragma: no cover
if skip_parallel:
pytest.skip("Skipping parallel test.")
from qibo.tests.test_parallel import is_parallel_supported
backend_name = qibo.get_backend()
if not is_parallel_supported(backend_name):
pytest.skip(
"Skipping parallel test due to unsupported configuration.")
qibo.set_threads(1)
nqubits = 6
layers = 4
circuit = models.Circuit(nqubits)
for l in range(layers):
for q in range(nqubits):
circuit.add(gates.RY(q, theta=1.0))
for q in range(0, nqubits - 1, 2):
circuit.add(gates.CZ(q, q + 1))
for q in range(nqubits):
circuit.add(gates.RY(q, theta=1.0))
for q in range(1, nqubits - 2, 2):
circuit.add(gates.CZ(q, q + 1))
circuit.add(gates.CZ(0, nqubits - 1))
for q in range(nqubits):
circuit.add(gates.RY(q, theta=1.0))
easy_hamiltonian = hamiltonians.X(nqubits)
problem_hamiltonian = hamiltonians.XXZ(nqubits)
s = lambda t: t
aavqe = models.AAVQE(circuit,
easy_hamiltonian,
problem_hamiltonian,
s,
nsteps=10,
t_max=1)
np.random.seed(0)
initial_parameters = np.random.uniform(0, 2 * np.pi,
2 * nqubits * layers + nqubits)
best, params = aavqe.minimize(params=initial_parameters,
method=method,
options=options,
compile=compile)
if method == "cma":
# remove `outcmaes` folder
import shutil
shutil.rmtree("outcmaes")
if filename is not None:
assert_regression_fixture(params, filename, rtol=1e-2)
qibo.set_threads(original_threads)
def main(nqubits,
dt,
solver,
backend,
dense=False,
accelerators=None,
filename=None):
"""Performs adiabatic evolution with critical TFIM as the "hard" Hamiltonian."""
qibo.set_backend(backend)
if accelerators is not None:
dense = False
solver = "exp"
logs = BenchmarkLogger(filename)
logs.append({
"nqubits": nqubits,
"dt": dt,
"solver": solver,
"dense": dense,
"backend": qibo.get_backend(),
"precision": qibo.get_precision(),
"device": qibo.get_device(),
"threads": qibo.get_threads(),
"accelerators": accelerators
})
print(f"Using {solver} solver and dt = {dt}.")
print(f"Accelerators: {accelerators}")
print("Backend:", logs[-1]["backend"])
start_time = time.time()
h0 = hamiltonians.X(nqubits, dense=dense)
h1 = hamiltonians.TFIM(nqubits, h=1.0, dense=dense)
logs[-1]["hamiltonian_creation_time"] = time.time() - start_time
print(f"\nnqubits = {nqubits}, solver = {solver}")
print(f"dense = {dense}, accelerators = {accelerators}")
print("Hamiltonians created in:", logs[-1]["hamiltonian_creation_time"])
start_time = time.time()
evolution = models.AdiabaticEvolution(h0,
h1,
lambda t: t,
dt=dt,
solver=solver,
accelerators=accelerators)
logs[-1]["creation_time"] = time.time() - start_time
print("Evolution model created in:", logs[-1]["creation_time"])
start_time = time.time()
final_psi = evolution(final_time=1.0)
logs[-1]["simulation_time"] = time.time() - start_time
print("Simulation time:", logs[-1]["simulation_time"])
logs.dump()
def _check_parallel_configuration(processes): # pragma: no cover
"""Check if configuration is suitable for efficient parallel execution."""
import sys, psutil
from qibo import get_device, get_backend, get_threads
from qibo.config import raise_error, log
device = get_device()
if sys.platform == "win32" or sys.platform == 'darwin': # pragma: no cover
raise_error(RuntimeError,
"Parallel evaluations supported only on linux.")
if get_backend() == "tensorflow": # pragma: no cover
raise_error(
RuntimeError,
f"{get_backend()} backend does not support parallel evaluations.")
if device is not None and "GPU" in device: # pragma: no cover
raise_error(RuntimeError,
"Parallel evaluations cannot be used with GPU.")
if ((processes is not None
and processes * get_threads() > psutil.cpu_count())
or (processes is None and get_threads() != 1)): # pragma: no cover
log.warning(
'Please consider using a lower number of threads per process,'
' or reduce the number of processes for better performance')
def test_vqe(backend, method, options, compile, filename):
"""Performs a VQE circuit minimization test."""
original_backend = qibo.get_backend()
original_threads = qibo.get_threads()
if (method == "sgd" or compile) and backend != "matmuleinsum":
pytest.skip("Skipping SGD test for unsupported backend.")
qibo.set_backend(backend)
if method == 'parallel_L-BFGS-B':
device = qibo.get_device()
if device is not None and "GPU" in device: # pragma: no cover
pytest.skip("unsupported configuration")
import os
if os.name == 'nt': # pragma: no cover
pytest.skip("Parallel L-BFGS-B not supported on Windows.")
qibo.set_threads(1)
nqubits = 6
layers = 4
circuit = models.Circuit(nqubits)
for l in range(layers):
for q in range(nqubits):
circuit.add(gates.RY(q, theta=1.0))
for q in range(0, nqubits - 1, 2):
circuit.add(gates.CZ(q, q + 1))
for q in range(nqubits):
circuit.add(gates.RY(q, theta=1.0))
for q in range(1, nqubits - 2, 2):
circuit.add(gates.CZ(q, q + 1))
circuit.add(gates.CZ(0, nqubits - 1))
for q in range(nqubits):
circuit.add(gates.RY(q, theta=1.0))
hamiltonian = hamiltonians.XXZ(nqubits=nqubits)
np.random.seed(0)
initial_parameters = np.random.uniform(0, 2 * np.pi,
2 * nqubits * layers + nqubits)
v = models.VQE(circuit, hamiltonian)
best, params, _ = v.minimize(initial_parameters,
method=method,
options=options,
compile=compile)
if method == "cma":
# remove `outcmaes` folder
import shutil
shutil.rmtree("outcmaes")
if filename is not None:
assert_regression_fixture(params, filename)
qibo.set_backend(original_backend)
qibo.set_threads(original_threads)
def test_transpose_state(nqubits, ndevices):
for _ in range(10):
# Generate global qubits randomly
all_qubits = np.arange(nqubits)
np.random.shuffle(all_qubits)
qubit_order = list(all_qubits)
state = random_complex((2**nqubits, ))
state_tensor = state.numpy().reshape(nqubits * (2, ))
target_state = np.transpose(state_tensor, qubit_order).ravel()
new_state = K.zeros_like(state)
shape = (ndevices, int(state.shape[0]) // ndevices)
state = K.reshape(state, shape)
pieces = [state[i] for i in range(ndevices)]
if K.gpu_devices: # pragma: no cover
# case not tested by GitHub workflows because it requires GPU
check_unimplemented_error(K.op.transpose_state, pieces, new_state,
nqubits, qubit_order, get_threads())
else:
new_state = K.op.transpose_state(pieces, new_state, nqubits,
qubit_order, get_threads())
np.testing.assert_allclose(target_state, new_state.numpy())
def test_swap_pieces(nqubits):
state = random_complex((2**nqubits, ))
target_state = K.cast(np.copy(state.numpy()), dtype=state.dtype)
shape = (2, int(state.shape[0]) // 2)
for _ in range(10):
global_qubit = np.random.randint(0, nqubits)
local_qubit = np.random.randint(0, nqubits)
while local_qubit == global_qubit:
local_qubit = np.random.randint(0, nqubits)
transpose_order = ([global_qubit] + list(range(global_qubit)) +
list(range(global_qubit + 1, nqubits)))
qubits_t = qubits_tensor(nqubits, [global_qubit, local_qubit])
target_state = K.op.apply_swap(target_state, qubits_t,
nqubits, global_qubit, local_qubit,
get_threads())
target_state = K.reshape(target_state, nqubits * (2, ))
target_state = K.transpose(target_state, transpose_order)
target_state = K.reshape(target_state, shape)
state = K.reshape(state, nqubits * (2, ))
state = K.transpose(state, transpose_order)
state = K.reshape(state, shape)
piece0, piece1 = state[0], state[1]
if K.gpu_devices: # pragma: no cover
# case not tested by GitHub workflows because it requires GPU
check_unimplemented_error(K.op.swap_pieces, piece0, piece1,
local_qubit - 1, nqubits - 1,
get_threads())
else:
K.op.swap_pieces(piece0, piece1,
local_qubit - int(global_qubit < local_qubit),
nqubits - 1, get_threads())
np.testing.assert_allclose(target_state[0], piece0.numpy())
np.testing.assert_allclose(target_state[1], piece1.numpy())
def test_swap_pieces_zero_global(nqubits):
state = random_complex((2**nqubits, ))
target_state = K.cast(np.copy(state.numpy()))
shape = (2, int(state.shape[0]) // 2)
state = K.reshape(state, shape)
for _ in range(10):
local = np.random.randint(1, nqubits)
qubits_t = qubits_tensor(nqubits, [0, local])
target_state = K.op.apply_swap(target_state, qubits_t, nqubits, 0,
local, get_threads())
target_state = K.reshape(target_state, shape)
piece0, piece1 = state[0], state[1]
if K.gpu_devices: # pragma: no cover
# case not tested by GitHub workflows because it requires GPU
check_unimplemented_error(K.op.swap_pieces, piece0, piece1,
local - 1, nqubits - 1, get_threads())
else:
K.op.swap_pieces(piece0, piece1, local - 1, nqubits - 1,
get_threads())
np.testing.assert_allclose(target_state[0], piece0.numpy())
np.testing.assert_allclose(target_state[1], piece1.numpy())
def test_vqe(backend, method, options, compile, filename, skip_parallel):
"""Performs a VQE circuit minimization test."""
original_threads = qibo.get_threads()
if (method == "sgd" or compile) and qibo.get_backend() != "tensorflow":
pytest.skip("Skipping SGD test for unsupported backend.")
if method == 'parallel_L-BFGS-B': # pragma: no cover
if skip_parallel:
pytest.skip("Skipping parallel test.")
from qibo.tests.test_parallel import is_parallel_supported
backend_name = qibo.get_backend()
if not is_parallel_supported(backend_name):
pytest.skip(
"Skipping parallel test due to unsupported configuration.")
qibo.set_threads(1)
nqubits = 6
layers = 4
circuit = models.Circuit(nqubits)
for l in range(layers):
for q in range(nqubits):
circuit.add(gates.RY(q, theta=1.0))
for q in range(0, nqubits - 1, 2):
circuit.add(gates.CZ(q, q + 1))
for q in range(nqubits):
circuit.add(gates.RY(q, theta=1.0))
for q in range(1, nqubits - 2, 2):
circuit.add(gates.CZ(q, q + 1))
circuit.add(gates.CZ(0, nqubits - 1))
for q in range(nqubits):
circuit.add(gates.RY(q, theta=1.0))
hamiltonian = hamiltonians.XXZ(nqubits=nqubits)
np.random.seed(0)
initial_parameters = np.random.uniform(0, 2 * np.pi,
2 * nqubits * layers + nqubits)
v = models.VQE(circuit, hamiltonian)
best, params, _ = v.minimize(initial_parameters,
method=method,
options=options,
compile=compile)
if method == "cma":
# remove `outcmaes` folder
import shutil
shutil.rmtree("outcmaes")
if filename is not None:
assert_regression_fixture(params, filename)
qibo.set_threads(original_threads)
def test_parallel_circuit_evaluation():
"""Evaluate circuit for multiple input states."""
if 'GPU' in get_device(): # pragma: no cover
pytest.skip("unsupported configuration")
original_threads = get_threads()
set_threads(1)
nqubits = 10
np.random.seed(0)
c = QFT(nqubits)
states = [np.random.random(2**nqubits) for i in range(5)]
r1 = []
for state in states:
r1.append(c(state))
r2 = parallel_execution(c, states=states, processes=2)
np.testing.assert_allclose(r1, r2)
set_threads(original_threads)
def test_unbalanced_probabilistic_measurement(backend, use_samples):
original_threads = qibo.get_threads()
# set single-thread to fix the random values generated from the frequency custom op
qibo.set_threads(1)
state = np.array([1, 1, 1, np.sqrt(3)]) / np.sqrt(6)
c = models.Circuit(2)
c.add(gates.Flatten(state))
c.add(gates.M(0, 1))
result = c(nshots=1000)
K.set_seed(1234)
if use_samples:
# calculates sample tensor directly using `tf.random.categorical`
# otherwise it uses the frequency-only calculation
_ = result.samples()
# update reference values based on backend and device
decimal_frequencies = K.test_regressions(
"test_unbalanced_probabilistic_measurement")
assert sum(result.frequencies().values()) == 1000
assert_result(result, decimal_frequencies=decimal_frequencies)
qibo.set_threads(original_threads)
def test_parallel_circuit_evaluation(backend):
"""Evaluate circuit for multiple input states."""
device = qibo.get_device()
backend = qibo.get_backend()
if 'GPU' in qibo.get_device(
) or sys.platform == "win32" or sys.platform == "darwin" or backend == "tensorflow" or backend == "qibojit": # pragma: no cover
pytest.skip("unsupported configuration")
original_threads = qibo.get_threads()
qibo.set_threads(1)
nqubits = 10
np.random.seed(0)
c = QFT(nqubits)
states = [np.random.random(2**nqubits) for i in range(5)]
r1 = []
for state in states:
r1.append(c(state))
r2 = parallel_execution(c, states=states, processes=2)
np.testing.assert_allclose(r1, r2)
qibo.set_threads(original_threads)
def test_apply_pauli_gate(nqubits, target, gate, compile):
"""Check ``apply_x``, ``apply_y`` and ``apply_z`` kernels."""
matrices = {
"x": np.array([[0, 1], [1, 0]], dtype=np.complex128),
"y": np.array([[0, -1j], [1j, 0]], dtype=np.complex128),
"z": np.array([[1, 0], [0, -1]], dtype=np.complex128)
}
state = random_complex((2**nqubits, ))
target_state = K.cast(state, dtype=state.dtype)
qubits = qubits_tensor(nqubits, [target])
target_state = K.op.apply_gate(state, matrices[gate], qubits, nqubits,
target, get_threads())
def apply_operator(state):
qubits = qubits_tensor(nqubits, [target])
return getattr(K.op, "apply_{}".format(gate))(state, qubits, nqubits,
target, get_threads())
if compile:
apply_operator = K.compile(apply_operator)
state = apply_operator(state)
np.testing.assert_allclose(target_state.numpy(), state.numpy())
def test_custom_op_toy_callback(gate, compile):
"""Check calculating ``callbacks`` using intermediate state values."""
import functools
state = random_complex((2**2, ))
mask = random_complex((2**2, ))
matrices = {
"h": np.array([[1, 1], [1, -1]]) / np.sqrt(2),
"x": np.array([[0, 1], [1, 0]]),
"z": np.array([[1, 0], [0, -1]])
}
for k, v in matrices.items():
matrices[k] = np.kron(v, np.eye(2))
matrices["swap"] = np.array([[1, 0, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0],
[0, 0, 0, 1]])
target_state = state.numpy()
target_c1 = mask.numpy().dot(target_state)
target_state = matrices[gate].dot(target_state)
target_c2 = mask.numpy().dot(target_state)
assert target_c1 != target_c2
target_callback = [target_c1, target_c2]
htf = K.cast(np.array([[1, 1], [1, -1]]) / np.sqrt(2), dtype=state.dtype)
qubits_t1 = qubits_tensor(2, [0])
qubits_t2 = qubits_tensor(2, [0, 1])
apply_gate = {
"h":
functools.partial(K.op.apply_gate,
gate=htf,
qubits=qubits_t1,
nqubits=2,
target=0,
omp_num_threads=get_threads()),
"x":
functools.partial(K.op.apply_x,
qubits=qubits_t1,
nqubits=2,
target=0,
omp_num_threads=get_threads()),
"z":
functools.partial(K.op.apply_z,
qubits=qubits_t1,
nqubits=2,
target=0,
omp_num_threads=get_threads()),
"swap":
functools.partial(K.op.apply_swap,
qubits=qubits_t2,
nqubits=2,
target1=0,
target2=1,
omp_num_threads=get_threads())
}
def apply_operator(state):
c1 = K.sum(mask * state)
state0 = apply_gate[gate](state)
c2 = K.sum(mask * state0)
return state0, K.stack([c1, c2])
if compile: # pragma: no cover
# case not tested because it fails
apply_operator = K.compile(apply_operator)
state, callback = apply_operator(state)
np.testing.assert_allclose(target_state, state.numpy())
np.testing.assert_allclose(target_callback, callback.numpy())
def apply_operator(state):
qubits = qubits_tensor(nqubits, targets, controls)
return K.op.apply_swap(state, qubits, nqubits, *targets, get_threads())
def state_vector_call(self, state):
return self.gate_op(state, self.qubits_tensor, self.result_tensor,
self.nqubits, self.normalize, get_threads())
def apply_operator(state):
qubits = qubits_tensor(nqubits, [nqubits - 1], controls)
return K.op.apply_gate(state, xgate, qubits, nqubits, nqubits - 1,
get_threads())
def apply_operator(state, gate):
qubits = qubits_tensor(nqubits, [target])
return K.op.apply_gate(state, gate, qubits, nqubits, target,
get_threads())
def state_vector_call(self, state):
return self.gate_op(state, self.qubits_tensor, self.nqubits,
*self.target_qubits, get_threads())
def density_matrix_call(self, state):
state = self.gate_op(state, self.qubits_tensor_dm, 2 * self.nqubits,
*self.target_qubits, get_threads())
state = self.gate_op(state, self.qubits_tensor, 2 * self.nqubits,
*self.target_qubits_dm, get_threads())
return state
def density_matrix_call(self, state):
state = self.gate_op(state, self.qubits_tensor_dm, self.result_tensor,
2 * self.nqubits, False, get_threads())
state = self.gate_op(state, self.qubits_tensor, self.result_tensor,
2 * self.nqubits, False, get_threads())
return state / K.trace(state)
def apply_operator(dtype):
"""Apply the initial_state operator"""
return K.op.initial_state(nqubits=4,
dtype=dtype,
is_matrix=False,
omp_num_threads=get_threads())
