Antenna Array Beamforming Based on a Hybrid Quantum Optimization Framework

TL;DR

This paper introduces a hybrid quantum-inspired optimization framework for large-scale antenna array beamforming, combining discrete and continuous variable optimization with novel encoding schemes and parallel exploration techniques.

Contribution

It presents a new hybrid quantum-classical approach with innovative encoding and optimization strategies for efficient large-scale antenna beamforming.

Findings

Achieved nearly double the baseline score in simulations.

Effectively optimized beamforming with constraints on sidelobes and beamwidth.

Enhanced numerical precision and efficiency in coupling matrix construction.

Abstract

This paper proposes a hybrid quantum optimization framework for large-scale antenna-array beamforming with jointly optimized discrete phases and continuous amplitudes. The method combines quantum-inspired search with classical gradient refinement to handle mixed discrete-continuous variables efficiently. For phase optimization, a Gray-code and odd-combination encoding scheme is introduced to improve robustness and avoid the complexity explosion of higher-order Ising models. For amplitude optimization, a geometric spin-combination encoding and a two-stage strategy are developed, using quantum-inspired optimization for coarse search and gradient optimization for fine refinement. To enhance solution diversity and quality, a rainbow quantum-inspired algorithm integrates multiple optimizers for parallel exploration, followed by hierarchical-clustering-based candidate refinement. In addition, a double outer-product method and an augmented version are proposed to construct the coupling matrix and bias vector efficiently, improving numerical precision and implementation efficiency. Under the scoring rules of the 7th National Quantum Computing Hackathon, simulations on a 32-element antenna array show that the proposed method achieves a score of 461.58 under constraints on near-main-lobe sidelobes, wide-angle sidelobes, beamwidth, and optimization time, nearly doubling the baseline score. The proposed framework provides an effective reference for beamforming optimization in future wireless communication systems.