Quantum Optimization in Wireless Communication Systems: Principles and Applications

TL;DR

This paper explores how quantum optimization techniques like quantum annealing and gate-based algorithms can enhance wireless communication system design, demonstrated through a case study on reconfigurable intelligent surface beamforming with real quantum hardware.

Contribution

It provides a comprehensive overview of quantum optimization principles and evaluates their application to wireless systems, including experimental validation on quantum hardware.

Findings

Quantum optimization offers promising advantages for wireless system design.

Experimental results validate quantum algorithms for beamforming.

Quantum methods show potential despite current limitations.

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.