Quantum Computing for Large-scale Network Optimization: Opportunities and Challenges

• URL: http://arxiv.org/abs/2509.07773v1
• Date: Tue, 09 Sep 2025 14:06:24 GMT
• Title: Quantum Computing for Large-scale Network Optimization: Opportunities and Challenges
• Authors: Sebastian Macaluso, Giovanni Geraci, Elías F. Combarro, Sergi Abadal, Ioannis Arapakis, Sofia Vallecorsa, Eduard Alarcón,
• Abstract summary: Quantum computing emerges as a promising technology for efficient large-scale optimization.<n>We present our vision of leveraging QC to tackle key classes of problems in future mobile networks.
• Score: 7.632489781851561
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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