Quantum Computing for Large-scale Network Optimization: Opportunities and Challenges
This addresses optimization challenges for future mobile networks, but it is incremental as it builds on existing quantum computing ideas without new empirical validation.
The paper tackles the intractable multi-objective optimization in large-scale 6G-and-beyond networks by proposing quantum computing as a solution, outlining methods like quantum annealing and quantum reinforcement learning without providing concrete numerical results.
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.