Quantum Graph Neural Networks for Double-Sided Reconfigurable Intelligent Surface Optimization

TL;DR

This paper introduces a quantum graph neural network framework for optimizing double-sided reconfigurable intelligent surfaces in 6G wireless systems, reducing computational complexity and outperforming classical methods.

Contribution

The novel quantum framework (QGCN) enables efficient joint optimization of RIS elements, incorporating discrete phase shifts and inter-element coupling, with demonstrated advantages on quantum hardware.

Findings

QGCN reduces computational complexity and memory usage.

QGCN outperforms classical GNN by +0.38 bps/Hz.

Experimental validation on IBM Quantum processor.

Abstract

As a key enabler for sixth-generation (6G) wireless communications, reconfigurable intelligent surfaces (RISs) provide the flexibility to control signal strength. Nevertheless, optimizing hundreds of elements is computationally expensive. To overcome this challenge, we present a quantum framework (QGCN) to jointly optimize the physical and electromagnetic response of a double-sided RIS design that incorporates discrete phase shifts and inter-element coupling. The core contribution is the adaptive activation or deactivation of elements, allowing a virtual spacing mechanism using PIN diode switches. We then solve a multi-objective problem that maximizes the minimum user data rate subject to constraints on aperture length and mutual coupling between active elements. Experimental results on IBM Quantum's 127-qubit ibm_kyiv superconducting processor demonstrate that the proposed QGCN algorithm reduces both per-iteration computational complexity and memory requirements compared to existing approaches. Also, the QGCN outperforms classical graph neural networks (GNN) on an equivalent graph topology by an additional $+$0.38 bps/Hz. This advantage is increasing with increasing array sizes.