Quantum communication networks require transmission of high-fidelity, uncoded qubits for applications such as entanglement distribution and quantum key distribution. However, current implementations are constrained by limited buffer capacity and qubit decoherence, which degrades qubit quality while waiting in the buffer. A key challenge arises from the stochastic nature of qubit generation, there exists a random delay (D) between the initiation of a generation request and the availability of the qubit. This induces a fundamental trade off early initiation increases buffer waiting time and hence decoherence, whereas delayed initiation leads to server idling and reduced throughput. We model this system as an admission control problem in a finite buffer queue, where the reward associated with each job is a decreasing function of its sojourn time. We derive analytical conditions under which a simple "no lag" policy where a new qubit is generated immediately upon the availability of buffer space is optimal. To address scenarios with unknown system parameters, we further develop a Bayesian learning framework that adaptively optimizes the admission policy. In addition to quantum communication systems, the proposed model is applicable to delay sensitive IoT sensing and service systems.

翻译：量子通信网络需要传输高保真度、无编码的量子比特，以用于纠缠分发和量子密钥分发等应用。然而，当前实现受限于有限的缓冲容量和量子比特退相干，这会在量子比特等待缓冲时降低其质量。一个关键挑战源于量子比特生成的随机性：从发起生成请求到量子比特可用之间存在随机延迟（D）。这引发了一个基本权衡：过早发起会增加缓冲等待时间，从而加剧退相干；而过晚发起则会导致服务器空闲，降低吞吐量。我们将此系统建模为有限缓冲队列中的准入控制问题，其中每个作业的奖励是其逗留时间的递减函数。我们推导出分析性条件，在此条件下，一种简单的“无滞后”策略（即一旦缓冲空间可用就立即生成新量子比特）是最优的。为应对系统参数未知的场景，我们进一步开发了一个贝叶斯学习框架，以自适应地优化准入策略。除量子通信系统外，所提出的模型也适用于对延迟敏感的物联网感知与服务系统。