Quantum Computing for Large-scale Network Optimization: Opportunities and Challenges
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915486862671872 |
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| author | Macaluso, Sebastian Geraci, Giovanni Combarro, Elías F. Abadal, Sergi Arapakis, Ioannis Vallecorsa, Sofia Alarcón, Eduard |
| author_facet | Macaluso, Sebastian Geraci, Giovanni Combarro, Elías F. Abadal, Sergi Arapakis, Ioannis Vallecorsa, Sofia Alarcón, Eduard |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_07773 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum Computing for Large-scale Network Optimization: Opportunities and Challenges Macaluso, Sebastian Geraci, Giovanni Combarro, Elías F. Abadal, Sergi Arapakis, Ioannis Vallecorsa, Sofia Alarcón, Eduard Networking and Internet Architecture Information Theory Machine Learning Signal Processing Quantum Physics 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. |
| title | Quantum Computing for Large-scale Network Optimization: Opportunities and Challenges |
| topic | Networking and Internet Architecture Information Theory Machine Learning Signal Processing Quantum Physics |
| url | https://arxiv.org/abs/2509.07773 |