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Using a dissipative quantum neural network (DQNN) accompanied by conjugatelayers, 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 denoisingarchitecture requires a much smaller number of learning parameters, which candecrease the training time, especially when a deep or stacked DQNN is needed toapproach the highest fidelity in the Noisy Intermediate-Scale Quantum (NISQ)era. In QAE, we reduce the connection between the hidden layer's qubits and theoutput's qubits to modify the decoder. The Renyi entropy of the hidden andoutput qubits' states is analyzed with respect to other qubits during learningiterations. During the learning process, if the hidden layer remains connectedto the input layers, the network can almost perfectly denoise unseen noisy datawith a different underlying noise distribution using the learning parametersacquired from training data.

NISQ-Compatible Error Correction of Quantum Data Using Modified Dissipative Quantum Neural Networks