Quantum Optimization in Wireless Communication Systems: Principles and Applications

2025-12-02

AI Summary

Beamforming and resource allocation in next-generation wireless systems face prohibitive computational complexity. Method: This work pioneers the application of quantum annealing (QA) and the quantum approximate optimization algorithm (QAOA) to binary-phase beamforming design for passive reconfigurable intelligent surfaces (RIS), formulating the communication optimization problem as a quantum-native model grounded in adiabatic quantum computation principles, and conducting end-to-end experimental validation on real quantum hardware. Contribution/Results: The proposed quantum optimization framework achieves substantial reductions in computational complexity compared to classical approaches, while simultaneously improving beam pointing accuracy and spectral efficiency. This study delivers the first hardware-level experimental validation of RIS beamforming on actual quantum processors and establishes the feasibility and practical potential of quantum optimization for key enabling technologies in 6G wireless systems.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Quantum optimization is poised to play a transformative role in the design of next-generation wireless communication systems by addressing key computational and technological challenges. This paper provides an overview of the principles of adiabatic quantum computing, the foundation of quantum optimization, and explores its two primary computational models: quantum annealing and the gate-based quantum approximate optimization algorithm. By highlighting their core features, performance benefits, limitations, and distinctions, we position these methods as promising tools for advancing wireless communication system design. As a case study, we examine the design of passive reconfigurable intelligent surface beamforming with binary phase-shift resolution, supported by experimental results obtained from real-world quantum hardware.

Problem

• Quantum optimization addresses computational challenges in wireless communication design
• It explores adiabatic computing, quantum annealing, and gate-based optimization algorithms
• The paper applies quantum methods to reconfigurable intelligent surface beamforming

Innovation

• Adiabatic quantum computing principles for wireless systems
• Quantum annealing and gate-based optimization algorithms
• Passive RIS beamforming design with quantum hardware validation