用量子计算解决6G网络大规模优化难题，探索新路径。
Quantum Computing for Large-scale Network Optimization: Opportunities and Challenges
- 基于图结构统一建模，采用量子退火与强化学习求解
- 针对复杂搜索空间，提升多目标优化效率
- 适合研究量子算法在通信网络中的应用者
6G及更未来网络的复杂性要求对巨大搜索空间进行多目标优化，此类问题通常难以求解。量子计算（QC）为高效的大规模优化提供了有前景的技术。本文提出利用量子计算应对未来移动网络中关键优化问题的愿景。通过分析并识别共性特征，尤其是其图中心化表示，我们提出一种包含量子算法的统一策略。具体而言，阐述了基于量子退火和量子强化学习的优化方法。此外，讨论了量子算法与硬件需克服的主要挑战，以有效优化未来网络。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。