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.

翻译：优化可重构智能表面（RIS）是一项高维组合挑战。现有量子算法常通过忽略互耦等物理约束来简化此问题，但这会显著降低实际性能。我们并非追求完全真实的RIS描述，而是将逐步增强的物理信息互耦模型嵌入二次无约束二元优化（QUBO）公式中。针对量子近似优化算法（QAOA），我们评估了四种伊辛相互作用模型（$J_{ij}$），范围从理想化纯相位模型到全密集物理模型。通过对$5 \times 5$网格的分析，我们的结果揭示了空间指向精度与量子硬件可行性之间的关键权衡。虽然完全全局耦合能最大化波束赋形精度，但密集哈密顿量会引入过高的路由开销，并在近期处理器上增加收敛难度。最终，我们证明：尽管物理信息驱动的量子优化在数学上可行，但在当前含噪中等规模量子（NISQ）器件上执行时，稀疏的、基于距离惩罚的模型仍是必要的折中方案。