Meta-Learning with QAOA for Channel Decoding

With the development of 6G networks, the required computational power has been greatly increased, and quantum computing offers promising approaches for this purpose due to its properties such as superposition and entanglement. It has been proposed to combine the Quantum Approximation optimization Algorithm (QAOA) with the Learning-to-Learn (L2L) strategy using classical Recurrent Neural Networks (RNN) to solve the problem of channel decoding on Noisy IntermediateScale Quantum (NISQ) devices. In this paper, we build on this foundation to further investigate the impact of different RNNs on channel decoding. The experimental results show that by training the RNN model of Minimal Gated Unit (MGU) with fewer parameters, the initial parameters of the obtained QAOA have a certain degree of similarity with the Long Short Term Memory Network (LSTM), and the decoding results are comparable. The method of training RNNs improves the performance and saves the cost compared to the random initialization method. Despite the limitation by the current quantum hardware, this quantum computation approach provides the future for channel decoding in the 6G network a promising scheme.