Quantum Optimization for Electromagnetics: Physics-Informed QAOA for Reconfigurable Intelligent Surfaces

2026-05-07

AI Summary

This work addresses the performance limitations of reconfigurable intelligent surfaces (RIS) in high-dimensional combinatorial optimization, often caused by neglecting physical constraints such as mutual coupling. The study presents the first systematic evaluation of Ising models with varying degrees of physical fidelity for RIS beamforming, embedding progressively enriched physical information into a QUBO framework. It introduces both sparse and fully coupled Ising Hamiltonians to balance solution accuracy against feasibility on noisy intermediate-scale quantum (NISQ) devices within the quantum approximate optimization algorithm (QAOA). Experimental results demonstrate that while the fully coupled model achieves the highest accuracy, the sparse and distance-penalized variants are better suited for current NISQ hardware, thereby revealing a critical trade-off between physical modeling fidelity and quantum executability.

Abstract

Optimizing Reconfigurable Intelligent Surfaces (RIS) is a high-dimensional combinatorial challenge. Current quantum algorithms often simplify this problem by ignoring physical constraints like mutual coupling, which significantly degrades real-world performance. Rather than targeting a fully realistic RIS description, we embed progressively more physics-informed models of mutual coupling into Quadratic Unconstrained Binary Optimization (QUBO) formulations. We evaluate four Ising interaction models ($J_{ij}$) for the Quantum Approximate Optimization Algorithm (QAOA), ranging from idealized phase-only to fully dense physical models. Analyzing a $5 \times 5$ grid, our results expose a critical trade-off between spatial pointing accuracy and quantum hardware feasibility. While complete global coupling maximizes beamforming precision, dense Hamiltonians introduce prohibitive routing overhead and complicate convergence on near-term processors. Ultimately, we demonstrate that while physics-informed quantum optimization is mathematically viable, sparse, distance-penalized models remain a necessary compromise for execution on current noisy intermediate-scale quantum (NISQ) devices.

Problem

• Reconfigurable Intelligent Surfaces
• Quantum Optimization
• Mutual Coupling
• QAOA
• NISQ

Innovation

• Physics-Informed QAOA
• Reconfigurable Intelligent Surfaces
• Mutual Coupling
• QUBO Formulation
• NISQ Devices