Introduces offline, instance-independent QAOA angles from infinite-size mixed-q Gaussian spin-glass limits for RIS power maximization using bounded-order phase dictionaries.
Quantum Optimization for Electromagnetics: Physics-Informed QAOA for Reconfigurable Intelligent Surfaces
2 Pith papers cite this work. Polarity classification is still indexing.
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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.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Survey classifying 78 joint OFDM-RIS optimization papers into convex relaxation, heuristics, deep learning, and foundation model paradigms, with synthesis showing ML methods achieve near model-based spectral efficiency at much higher speed.
citing papers explorer
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Offline Channel-Independent QAOA Angles for RIS Power Aggregation: Unit-Circle Phase Dictionaries and Infinite-Size Spin-Glass Limits
Introduces offline, instance-independent QAOA angles from infinite-size mixed-q Gaussian spin-glass limits for RIS power maximization using bounded-order phase dictionaries.
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Optimization Algorithms for Joint OFDM Waveform Design and RIS Configuration in 6G Networks: From Convex Relaxation to Foundation Models
Survey classifying 78 joint OFDM-RIS optimization papers into convex relaxation, heuristics, deep learning, and foundation model paradigms, with synthesis showing ML methods achieve near model-based spectral efficiency at much higher speed.