```
class Generator_Quantum(nn.Module):
def __init__(self, n_qubits, q_depth, q_delta=0.1):
"""
This is the quantum generator as described in https://arxiv.org/pdf/2010.06201.pdf
"""
super().__init__()
self.q_params = nn.ParameterList([nn.Parameter(q_delta * torch.randn(q_depth * n_qubits)) for i in range(8)])
self.n_qubits = n_qubits
self.q_depth = q_depth
# Spread of the random parameters for the paramaterised quantum gates
self.q_delta = q_delta
device = qml.device('lightning.qubit', wires=self.n_qubits)
# This is just a class with a simple circuit function - obtained by quantum_sim.circuit()
self.quantum_sim = QuantumSim(n_qubits, q_depth)
self.qnodes = qml.QNodeCollection(
[qml.QNode(self.quantum_sim.circuit, device, interface="torch") for i in range(8)]
)
def forward(self, noise):
q_out = torch.Tensor(0, 8* (2**self.n_qubits))
q_out = q_out.to(device)
# Apply the quantum circuit to each element of the batch and append to q_out
for elem in noise:
patched_array = np.empty((0, 2**self.n_qubits))
for p, qnode in zip(self.q_params, self.qnodes):
q_out_elem = qnode(elem, p).float().detach().cpu().numpy()
patched_array = np.append(patched_array, q_out_elem)
patched_tensor = torch.Tensor(patched_array).to(device).reshape(1, 8* (2**self.n_qubits))
q_out = torch.cat((q_out, patched_tensor))
return q_out
```

Let me explain this line

`q_out = torch.Tensor(0, 8* (2**self.n_qubits))`

The 2 ** self.n_qubits comes from the circuit output being qml.probs() on all qubits. The 8 is because I am trying to have 8 lightning.qubits run at the same time for a concatenated output.

On each forward pass, a single vector ‘noise’ of size n_qubits, is passed to each 8 nodes.

```
`for elem in noise:`
```

This for loops over the batch

I hope I made it clear… if not I can provide more code or explanations if I forgot to define anything.

Thanks for your help!