Quantum computing for large-scale network optimization: opportunities and challenges

Abstract

The complexity of large-scale 6G-and-beyond networks demands innovative approaches for multi-objective optimization over vast search spaces, a task often intractable. Quantum computing (QC) emerges as a promising technology for efficient large-scale optimization. We present our vision of leveraging QC to tackle key classes of problems in future mobile networks. By analyzing and identifying common features, particularly their graph-centric representation, we propose a unified strategy involving QC algorithms. Specifically, we outline a methodology for optimization using quantum annealing as well as quantum reinforcement learning. Additionally, we discuss the main challenges that QC algorithms and hardware must overcome to effectively optimize future networks.

The work of Giovanni Geraci was in part supported by the Spanish Research Agency through grants PID2021-123999OB-I00 and CNS2023- 145384 and by the Maria de Maeztu Units of Excellence Programme (CEX2021-001195-M). The work of Elías F. Combarro was partially supported by grant PID2023-146520OB-C22, funded by MICIU/AEI/10.13039/501100011033, by grant IDE/2024/000734 funded by Principado de Asturias, and by the Ministry for Digital Transformation and of Civil Service of the Spanish Government through the QUANTUM ENIA project call – Quantum Spain project, and by the European Union through the Recovery, Transformation and Resilience Plan — NextGenerationEU within the framework of the Digital Spain 2026 Agenda. The work of Eduard Alarcón was partially supported by the ICREA Academia Award 2024.

Peer Reviewed

Postprint (author's final draft)