Queuing delay is an essential topic in the design of quantum networks. This paper introduces a tractable model for analyzing the queuing delay of quantum data, referred to as quantum queuing delay (QQD). The model employs a dynamic programming formalism and accounts for practical aspects such as the finite memory size. Using this model, we develop a cognitive-memory-based policy for memory management and show that this policy can decrease the average queuing delay exponentially with respect to memory size. Such a significant reduction can be traced back to the use of entanglement, a peculiar quantum phenomenon that has no classical counterpart. Numerical results validate the theoretical analysis and demonstrate the near-optimal performance of the developed policy.

中文翻译：

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量子排队延迟

排队延迟是量子网络设计中的一个重要课题。本文介绍了一种用于分析量子数据排队延迟的易处理模型，简称量子排队延迟（QQD）。该模型采用动态编程形式，并考虑了实际方面，例如有限的内存大小。使用该模型，我们开发了一种基于认知记忆的内存管理策略，并表明该策略可以相对于内存大小以指数方式降低平均排队延迟。如此显着的减少可以追溯到纠缠的使用，这是一种没有经典对应物的奇特量子现象。数值结果验证了理论分析并证明了所开发策略的近乎最佳性能。