def test_cvneuralnet_uses_correct_weights(self, gaussian_device_4modes,
weights):
"""Tests that the CVNeuralNetLayers template uses the weigh parameters correctly."""
with qml.utils.OperationRecorder() as rec:
CVNeuralNetLayers(*weights, wires=range(4))
# Test that gates appear in the right order for each layer:
# BS-R-S-BS-R-D-K
for l in range(2):
gates = [
qml.Beamsplitter, qml.Rotation, qml.Squeezing,
qml.Beamsplitter, qml.Rotation, qml.Displacement
]
# count the position of each group of gates in the layer
num_gates_per_type = [0, 6, 4, 4, 6, 4, 4, 4]
s = np.cumsum(num_gates_per_type)
gc = l * sum(num_gates_per_type) + np.array(
list(zip(s[:-1], s[1:])))
# loop through expected gates
for idx, g in enumerate(gates):
# loop through where these gates should be in the queue
for opidx, op in enumerate(rec.queue[gc[idx, 0]:gc[idx, 1]]):
# check that op in queue is correct gate
assert isinstance(op, g)
# test that the parameters are correct
res_params = op.parameters
if idx == 0:
# first BS
exp_params = [
weights[0][l][opidx], weights[1][l][opidx]
]
elif idx == 1:
# first rot
exp_params = [weights[2][l][opidx]]
elif idx == 2:
# squeezing
exp_params = [
weights[3][l][opidx], weights[4][l][opidx]
]
elif idx == 3:
# second BS
exp_params = [
weights[5][l][opidx], weights[6][l][opidx]
]
elif idx == 4:
# second rot
exp_params = [weights[7][l][opidx]]
elif idx == 5:
# displacement
exp_params = [
weights[8][l][opidx], weights[9][l][opidx]
]
assert res_params == exp_params
def circuit(*weights):
CVNeuralNetLayers(*weights, wires=range(4))
return qml.expval(qml.X(0))
def circuit(weights):
CVNeuralNetLayers(*weights, wires=range(N))
return qml.expval.X(wires=0)
def circuit(weights):
CVNeuralNetLayers(*weights, wires=range(n_subsystems))
return qml.expval.Identity(0)
