Offline Channel-Independent QAOA Angles for RIS Power Aggregation: Unit-Circle Phase Dictionaries and Infinite-Size Spin-Glass Limits
Abstract
Reconfigurable intelligent surfaces (RIS) maximize received power by setting per-element phases. Discrete-phase optimization is NP-hard in the worst case, while the quantum approximate optimization algorithm (QAOA) applied to RIS faces limited phase alphabets, either per-problem angle optimization or uncharacterized training cost exposed to barren plateaus, and no scalable performance benchmark. We introduce a $2^{M}$-phase $θ$ dictionary for optimizing power $\|\mathbf{A} \, e^{jθ}\|^{2}$ havin...
Description / Details
Reconfigurable intelligent surfaces (RIS) maximize received power by setting per-element phases. Discrete-phase optimization is NP-hard in the worst case, while the quantum approximate optimization algorithm (QAOA) applied to RIS faces limited phase alphabets, either per-problem angle optimization or uncharacterized training cost exposed to barren plateaus, and no scalable performance benchmark. We introduce a -phase dictionary for optimizing power having channel matrix and QAOA angle offline optimization with instance and size-independent infinite-size limit of the mixed- Gaussian ensemble of Basso et al. Our design bounds the spin-Hamiltonian interaction order to at most quartic for any , and the deployed order-2 reduction lies below the even- regime in which constant-level QAOA limitations are proved. We perform analytical, state-vector, matrix-product-state and Pauli-path-simulation numerical studies for and QAOA depth , verifying offline angle transfer to Rayleigh, Rician/line-of-sight, cascaded double-fading and spatially-correlated RIS channels at . We observe performance reaching a near-optimal multi-start single-flip local-search reference for under order-2 modeling with -phase dictionary while the order-4 model shows a performance ceiling below the classical reference. The approach suggests a route to near-optimal large- performance on future fault-tolerant (FTQ) quantum computers, which enable the higher-depth QAOA circuits.
Source: arXiv:2606.24540v1 - http://arxiv.org/abs/2606.24540v1 PDF: https://arxiv.org/pdf/2606.24540v1 Original Link: http://arxiv.org/abs/2606.24540v1
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Jun 24, 2026
Chemical Engineering
Engineering
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