Hello! If applicable, put your complete code example down below. Make sure that your code:

- is 100% self-contained — someone can copy-paste exactly what is here and run it to

reproduce the behaviour you are observing - includes comments

Greetings,

I have been trying to visualize the circuit in Quantum Transfer Learning example at:

Quantum transfer learning

Would highly appreciate any help on how to visualize this circuit (or) pointers to where this visualization may already be available.

Thanks

```
# Put code here
def H_layer(nqubits):
"""Layer of single-qubit Hadamard gates.
"""
for idx in range(nqubits):
qml.Hadamard(wires=idx)
def RY_layer(w):
"""Layer of parametrized qubit rotations around the y axis.
"""
for idx, element in enumerate(w):
qml.RY(element, wires=idx)
def entangling_layer(nqubits):
"""Layer of CNOTs followed by another shifted layer of CNOT.
"""
# In other words it should apply something like :
# CNOT CNOT CNOT CNOT... CNOT
# CNOT CNOT CNOT... CNOT
for i in range(0, nqubits - 1, 2): # Loop over even indices: i=0,2,...N-2
qml.CNOT(wires=[i, i + 1])
for i in range(1, nqubits - 1, 2): # Loop over odd indices: i=1,3,...N-3
qml.CNOT(wires=[i, i + 1])
@qml.qnode(dev, interface="torch")
def quantum_net(q_input_features, q_weights_flat):
"""
The variational quantum circuit.
"""
# Reshape weights
q_weights = q_weights_flat.reshape(q_depth, n_qubits)
# Start from state |+> , unbiased w.r.t. |0> and |1>
H_layer(n_qubits)
# Embed features in the quantum node
RY_layer(q_input_features)
# Sequence of trainable variational layers
for k in range(q_depth):
entangling_layer(n_qubits)
RY_layer(q_weights[k])
# Expectation values in the Z basis
exp_vals = [qml.expval(qml.PauliZ(position)) for position in range(n_qubits)]
return tuple(exp_vals)
```

If you want help with diagnosing an error, please put the full error message below:

```
# Put full error message here
```

And, finally, make sure to include the versions of your packages. Specifically, show us the output of `qml.about()`

.

Name: PennyLane

Version: 0.30.0

Summary: PennyLane is a Python quantum machine learning library by Xanadu Inc.

Home-page: GitHub - PennyLaneAI/pennylane: PennyLane is a cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network.

Author:

Author-email:

License: Apache License 2.0

Location: /usr/local/lib/python3.10/dist-packages

Requires: appdirs, autograd, autoray, cachetools, networkx, numpy, pennylane-lightning, requests, rustworkx, scipy, semantic-version, toml

Required-by: PennyLane-Lightning

Platform info: Linux-5.15.107±x86_64-with-glibc2.31

Python version: 3.10.12

Numpy version: 1.22.4

Scipy version: 1.10.1

Installed devices: