Layer's outpout is None when using LossChannel

[jonasbrami]

Issue description

I may be missing something, but when trying to add LossChannels to the layer in the quantum_neural_network.py, the output of the layer is None

• Expected behavior: Output shouldn't be None

• Reproduces how often: 100%

• System information:

  
  Copyright 2018-2020 Xanadu Quantum Technologies Inc.

Python version: 3.8.5 Platform info: Linux-5.8.0-44-generic-x86_64-with-glibc2.10 Installation path: /home/jonas/anaconda3/envs/strawberry/lib/python3.8/site-packages/strawberryfields Strawberry Fields version: 0.17.0 Numpy version: 1.19.2 Scipy version: 1.4.1 SymPy version: 1.7.1 NetworkX version: 2.5 The Walrus version: 0.14.0 Blackbird version: 0.3.1-dev TensorFlow version: 2.2.0

#### Source code and tracebacks

def layer(params, q, loss_channel=None, loss_channel_param=None): """CV quantum neural network layer acting on N modes.

Args:
    params (list[float]): list of length ``2*(max(1, N-1) + N**2 + n)`` containing
        the number of parameters for the layer
    q (list[RegRef]): list of Strawberry Fields quantum registers the layer
        is to be applied to
"""
N = len(q)
M = int(N * (N - 1)) + max(1, N - 1)

int1 = params[:M]
s = params[M:M+N]
int2 = params[M+N:2*M+N]
dr = params[2*M+N:2*M+2*N]
dp = params[2*M+2*N:2*M+3*N]
k = params[2*M+3*N:2*M+4*N]

# begin layer
interferometer(int1, q)

for i in range(N):
    ops.Sgate(s[i]) | q[i]

interferometer(int2, q)

for i in range(N):
    ops.Dgate(dr[i], dp[i]) | q[i]
    ops.Kgate(k[i]) | q[i]
    if i == N-1:
        if loss_channel is None:
            pass
        elif loss_channel == 'vacuum_coupling':
            ops.LossChannel(loss_channel_param) | q[i]
        elif loss_channel == 'thermal_coupling':
            ops.ThermalLossChannel(loss_channel_param) | q[i]

[antalszava]

Hi @jonasbrami, the quantum_neural_network.py tutorial is tailored towards using pure states. Adding loss channels to the circuit might (and in most cases does) result in mixed quantum states. Strawberry Fields keeps track of the purity of the state internally and the state.ket method returns None for mixed states. To adjust the tutorial for using loss channels, the code would likely need to be altered to use state.dm (which returns the state in the density matrix formalism).

I'll remove the bug label, but let us know if you have any related questions. :slightly_smiling_face:

[jonasbrami]

Thanks a lot for the explanation :)