A Novel Lightweight Hybrid MLP and Quantum Neural Network Model for Bit Error Rate Prediction in NOMA-Enabled Visible Light Communication Systems

In this letter, a lightweight hybrid Multi-Layer Perceptron-Quantum Neural Network (MLP-QNN) architecture for Bit Error Rate (BER) prediction in Non-Orthogonal Multiple Access-Visible Light Communication (NOMA-VLC) systems is proposed. The proposed approach integrates classical preprocessing with variational quantum circuits (VQC) using ZFeatureMap encoding and TwoLocal ansatz in a compact 170-parameter structure implemented through Qiskit-PyTorch framework. The quantum circuits serve as enhanced feature extractors to capture complex NOMA-VLC dynamics. Simulation results demonstrate the superiority of the hybrid quantum-classical approach, achieving R2scores of 0.9989 and 0.9985 for far and near users, with 1 – 1.5dBSNR gains over classical MLP, advanced classical and analytical methods at reduced computational requirements. The model achieves the high accuracy and ≈0.8dBSNR gains under noisy simulation, confirming quantum noise robustness.