  Using a dissipative quantum neural network (DQNN) accompanied by conjugate
layers, we upgrade the performance of the existing quantum auto-encoder (QAE)
network as a quantum denoiser of a noisy m-qubit GHZ state. Our new denoising
architecture requires a much smaller number of learning parameters, which can
decrease the training time, especially when a deep or stacked DQNN is needed to
approach the highest fidelity in the Noisy Intermediate-Scale Quantum (NISQ)
era. In QAE, we reduce the connection between the hidden layer's qubits and the
output's qubits to modify the decoder. The Renyi entropy of the hidden and
output qubits' states is analyzed with respect to other qubits during learning
iterations. During the learning process, if the hidden layer remains connected
to the input layers, the network can almost perfectly denoise unseen noisy data
with a different underlying noise distribution using the learning parameters
acquired from training data.
