Antenna Array Beamforming Based on a Hybrid Quantum Optimization Framework

2026-03-20

AI Summary

This work addresses the challenge of jointly optimizing discrete phase and continuous amplitude in beamforming for large-scale antenna arrays by proposing a hybrid quantum-inspired optimization framework. The approach integrates quantum-inspired search with classical gradient-based optimization, enhancing phase robustness through Gray code and odd-number combinatorial encoding, while leveraging geometric spin encoding and a two-stage strategy for amplitude optimization. To improve solution diversity and quality, a rainbow quantum-inspired algorithm is introduced. Key technical innovations include hierarchical clustering for candidate selection, construction of a dual outer-product coupling matrix, and an enhanced bias vector. Evaluated on a 32-element array, the method achieves a score of 461.58—nearly twice that of the baseline—and demonstrates significant improvements over existing approaches in sidelobe suppression, beamwidth control, and optimization efficiency.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization
• Machine Learning: Optimization

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• antenna array beamforming
• discrete phase optimization
• continuous amplitude optimization
• mixed discrete-continuous variables
• quantum-inspired optimization

Innovation

• hybrid quantum optimization
• antenna array beamforming
• discrete-continuous optimization
• Gray-code encoding
• rainbow quantum-inspired algorithm