在线学习量子网络最佳路径，支持不同反馈机制。
Learning Best Paths in Quantum Networks
- 设计两种算法分别处理链路级与路径级反馈
- 在模拟中高概率准确找到最优传输路径
- 适用于量子密钥分发等实际量子网络场景
量子网络（QNs）在噪声通道中传输脆弱的量子信息。关键应用如量子密钥分发（QKD）和分布式量子计算（DQC）依赖于高效的量子信息传输。学习一对端节点间的最佳路径是提升这些应用性能的关键。本文研究在在线学习设置下如何寻找量子网络中的最佳路径。探讨两类反馈：链路级反馈适用于具备先进量子交换机的网络，可对单条链路进行基准测试；路径级反馈则对应基础量子交换机，仅能对整条路径进行评估。提出两种在线学习算法BeQuP-Link和BeQuP-Path，分别用于链路级和路径级反馈。BeQuP-Link动态评测关键链路，BeQuP-Path通过子程序将路径级观测转化为链路级参数估计，采用批处理方式。分析了算法的量子资源复杂度，证明二者均可高效且以高概率确定最佳路径。最后，基于NetSquid的仿真验证了两种算法在准确性和效率上的表现。
原文摘要 · Abstract (English)
Quantum networks (QNs) transmit delicate quantum information across noisy quantum channels. Crucial applications, like quantum key distribution (QKD) and distributed quantum computation (DQC), rely on efficient quantum information transmission. Learning the best path between a pair of end nodes in a QN is key to enhancing such applications. This paper addresses learning the best path in a QN in the online learning setting. We explore two types of feedback: "link-level" and "path-level". Link-level feedback pertains to QNs with advanced quantum switches that enable link-level benchmarking. Path-level feedback, on the other hand, is associated with basic quantum switches that permit only path-level benchmarking. We introduce two online learning algorithms, BeQuP-Link and BeQuP-Path, to identify the best path using link-level and path-level feedback, respectively. To learn the best path, BeQuP-Link benchmarks the critical links dynamically, while BeQuP-Path relies on a subroutine, transferring path-level observations to estimate link-level parameters in a batch manner. We analyze the quantum resource complexity of these algorithms and demonstrate that both can efficiently and, with high probability, determine the best path. Finally, we perform NetSquid-based simulations and validate that both algorithms accurately and efficiently identify the best path.
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