Hi, I have been tring to make hybrid quantum-classical model.

I have two questions about that.

- classical layer between quantum layers

: First, I attempted hybrid layer codes as follows.

dev = qml.device(‘default.qubit’, wires = 8)

@qml.qnode(dev)

def QCNN (…):

embedding.data_embedding(X, embedding_type=embedding_type) ## embedding 256 classical data to amplitude of 8 qubits

QCNN_structure1(unitary.U_SU4, params, U_params)

torch.nn.Linear(16, 16) ## classical layer 1

QCNN_structure2(unitary.U_SU4, params, U_params)

torch.nn.Linear(4, 4) ## classical layer 2

QCNN_structure3(unitary.U_SU4, params, U_params)

result = qml.expval(qml.PauliZ(4))

return result

I checked my hybrid model including above codes runs well without any errors.

However, I don’t have confidence whether this code can run with my goal.

(i.e., the output of QCNN structure_1 pass to classical layer 1 and the output from classical layer 1 to QCNN_structure 2)

How can I check this? And is it possible to write code for my goal?

- quamtum layer between classical layers

: Like the first question I mentioned above, I considered hybrid model with quantum layer between classical layers. In this case, how can I use quantum layer without measurement between classical layers?

For example, (this is pseudo code)

classical_layer1 (torch.nn.Linear(256, 256))

quantum_layer1 (amplitude embedding with 8 qubits and without measurement)

classical_layer2 (torch.nn.Linear(64, 64)

Is it possible to use intermediate data from quantum circuit without measurement step as a input data for classical layer?

Thank you in advance!