{"ID":2873058,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.07773","arxiv_id":"2509.07773","title":"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.","short_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 cla...","url_abs":"https://arxiv.org/abs/2509.07773","url_pdf":"https://arxiv.org/pdf/2509.07773v1","authors":"[\"Sebastian Macaluso\",\"Giovanni Geraci\",\"Elías F. Combarro\",\"Sergi Abadal\",\"Ioannis Arapakis\",\"Sofia Vallecorsa\",\"Eduard Alarcón\"]","published":"2025-09-09T14:06:24Z","proceeding":"cs.NI","tasks":"[\"cs.NI\",\"cs.IT\",\"cs.LG\",\"eess.SP\",\"quant-ph\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}