用强化学习动态优化量子网络中的纠缠分发路径。
Adaptive Entanglement Routing with Deep Q-Networks in Quantum Networks
- 基于深度Q网络实现自适应纠缠路由决策。
- 提升资源分配效率，满足不同应用的保真度与容量需求。
- 适合量子网络规划与资源管理研究者参考。
量子互联网有望通过量子信息处理原理彻底改变全球通信。尽管量子通信技术取得显著进展，但关键资源（如量子比特）的高效分发仍是持续存在的难题。传统方法难以实现最优资源分配，亟需更有效的解决方案。本文提出一种基于强化学习的自适应纠缠路由框架，旨在根据量子应用的具体需求进行资源分配。引入的QuDQN模型利用强化学习优化量子网络管理，实现高效资源分配与纠缠路由。该模型综合考虑保真度要求、网络拓扑、量子比特容量及请求需求等关键因素。
原文摘要 · Abstract (English)
The quantum internet holds transformative potential for global communication by harnessing the principles of quantum information processing. Despite significant advancements in quantum communication technologies, the efficient distribution of critical resources, such as qubits, remains a persistent and unresolved challenge. Conventional approaches often fall short of achieving optimal resource allocation, underscoring the necessity for more effective solutions. This study proposes a novel reinforcement learning-based adaptive entanglement routing framework designed to enable resource allocation tailored to the specific demands of quantum applications. The introduced QuDQN model utilizes reinforcement learning to optimize the management of quantum networks, allocate resources efficiently, and enhance entanglement routing. The model integrates key considerations, including fidelity requirements, network topology, qubit capacity, and request demands.
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