Quantum Noise-Aware RIS-Aided Wireless Networks Using Variational Encoding and Signal Stabilization

Abstract

This paper introduces a noise-aware, quantum-assisted blockage prediction framework for RIS-enabled wireless networks, integrating a QBS, QRIS, and QUN. Visual and channel data are hybrid-encoded into quantum states and processed via variational quantum circuits for ternary link classification. To address NISQ limitations, we model depolarizing and dephasing noise, apply amplitude damping, and use a fidelity-aware loss. Simulations on a quantum-adapted ViWi dataset demonstrate improved accuracy and robustness compared to classical and single-modality models under realistic noise conditions.