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 test_get_parameters():
c = Circuit(3)
c.add(gates.RX(0, theta=0.123))
c.add(gates.RY(1, theta=0.456))
c.add(gates.CZ(1, 2))
c.add(gates.fSim(0, 2, theta=0.789, phi=0.987))
c.add(gates.H(2))
unitary = np.array([[0.123, 0.123], [0.123, 0.123]])
c.add(gates.Unitary(unitary, 1))
params = [0.123, 0.456, (0.789, 0.987), unitary]
assert params == c.get_parameters()
params = [0.123, 0.456, (0.789, 0.987), unitary]
assert params == c.get_parameters()
params = {
c.queue[0]: 0.123,
c.queue[1]: 0.456,
c.queue[3]: (0.789, 0.987),
c.queue[5]: unitary
}
assert params == c.get_parameters("dict")
params = [0.123, 0.456, 0.789, 0.987]
params.extend(unitary.ravel())
assert params == c.get_parameters("flatlist")
with pytest.raises(ValueError):
c.get_parameters("test")
def test_get_parameters(trainable, include_not_trainable):
c = Circuit(3)
c.add(gates.RX(0, theta=0.123))
c.add(gates.RY(1, theta=0.456, trainable=trainable))
c.add(gates.CZ(1, 2))
c.add(gates.fSim(0, 2, theta=0.789, phi=0.987, trainable=trainable))
c.add(gates.H(2))
unitary = np.array([[0.123, 0.123], [0.123, 0.123]])
c.add(gates.Unitary(unitary, 1))
if trainable or include_not_trainable:
params = {
"list": [0.123, 0.456, (0.789, 0.987), unitary],
"dict": {
c.queue[0]: 0.123,
c.queue[1]: 0.456,
c.queue[3]: (0.789, 0.987),
c.queue[5]: unitary
},
"flatlist": [0.123, 0.456, 0.789, 0.987]
}
else:
params = {
"list": [0.123, unitary],
"dict": {
c.queue[0]: 0.123,
c.queue[5]: unitary
},
"flatlist": [0.123]
}
params["flatlist"].extend(unitary.ravel())
for fmt, prm in params.items():
assert c.get_parameters(fmt, include_not_trainable) == prm
with pytest.raises(ValueError):
c.get_parameters("test")
