Channel Estimation for Reconfigurable Intelligent Surface-Aided 6G NOMA Systems: A Quantum Machine Learning Approach

The integration of reconfigurable intelligent surfaces (RISs) and non-orthogonal multiple access (NOMA) is considered a promising technique to enhance spectral efficiency and connectivity in future 6G networks. Accurate channel estimation remains a critical challenge in RIS-NOMA systems due to the increased complexity introduced by the combination of RIS and NOMA technologies. While quantum machine learning (QML) has demonstrated potential in wireless communications, its application in channel estimation remains underexplored. This paper investigates the effectiveness of a hybrid quantum-classical machine learning (ML) model for channel estimation in RIS-NOMA systems. We propose a hybrid architecture that integrates convolutional neural networks (CNNs) with quantum long short-term memory (QLSTM) networks, where CNNs perform spatial feature extraction while QLSTMs capture temporal dependencies in the time-varying channel. Extensive simulations are conducted to evaluate the performance of the model under various network configurations, considering different power allocation factors, the number of RIS elements, and signal-to-noise ratios (SNRs). The performance of the proposed model is benchmarked against both pure quantum and classical ML models, including a quantum neural network (QNN), a CNN, a long short-term memory (LSTM) model, a bidirectional LSTM (BiLSTM) model, and a CNN-LSTM model. The results demonstrate that the proposed CNN-QLSTM model outperforms all baseline methods in terms of root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). These findings highlight the potential of quantum-enhanced ML for channel estimation in next-generation communication networks.