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

TL;DR

This paper introduces a quantum noise-aware framework for blockage prediction in RIS-enabled wireless networks, utilizing variational quantum circuits and signal stabilization to improve accuracy under hardware noise.

Contribution

It proposes a novel hybrid quantum model that explicitly accounts for quantum hardware noise and enhances blockage prediction accuracy in wireless networks.

Findings

Superior accuracy over baseline models

Enhanced robustness against quantum hardware noise

Effective ternary classification of link status

Abstract

This paper presents a noise-aware quantum-assisted framework for blockage prediction in reconfigurable intelligent surface (RIS)-enabled wireless networks. The proposed architecture integrates a Quantum Base Station (QBS), a Quantum RIS (QRIS), and a mobile Quantum User Node (QUN). Visual information captured by an onboard RGB camera is amplitude-encoded into quantum states, while channel state observations are mapped into quantum rotation-encoded features. These hybrid inputs are processed through variational quantum circuits, enabling ternary classification of the link status. To address the inherent imperfections of noisy intermediate-scale quantum (NISQ) hardware, the system explicitly models depolarizing and dephasing channels along direct and QRIS-assisted paths. A fidelity-aware training objective is employed to jointly minimize classification loss and quantum state degradation, with amplitude damping and synthetic noise injection enhancing robustness. Simulation results on a quantum-adapted version of the ViWi dataset demonstrate that the proposed hybrid quantum model achieves superior accuracy and stability under realistic noise conditions, outperforming baseline and single-modality approaches.