We study the limits of bipartite entanglement distribution using a chain of quantum repeaters that have quantum memories. To generate end-to-end entanglement, each node can attempt the generation of an entangled link with a neighbor, or perform an entanglement swapping measurement. A maximum storage time, known as cutoff, is enforced on the memories to ensure high-quality entanglement. Nodes follow a policy that determines when to perform each operation. Global-knowledge policies take into account all the information about the entanglement already produced. Here, we find global-knowledge policies that minimize the expected time to produce end-to-end entanglement. Our methods are based on Markov decision processes and value and policy iteration. We compare optimal policies to a policy in which nodes only use local information. We find that the advantage in expected delivery time provided by an optimal global-knowledge policy increases with increasing number of nodes and decreasing probability of successful swapping. Our work sheds light on how to distribute entangled pairs in large quantum networks using a chain of intermediate repeaters with cutoffs.

翻译：我们研究利用具有量子存储器的量子中继器链进行二分纠缠分发的极限。为实现端到端纠缠，每个节点可以尝试与邻居生成纠缠链路，或执行纠缠交换测量。为确保高保真度纠缠，存储器会强制执行最大存储时间（称为截止）。节点遵循一种策略，决定何时执行每种操作。全局知识策略考虑已经生成的全部纠缠信息。本文中，我们找到了最小化端到端纠缠预期产生时间的全局知识策略。我们的方法基于马尔可夫决策过程以及价值和策略迭代。我们将最优策略与仅使用本地信息的策略进行比较。研究发现，最优全局知识策略在预期传递时间上的优势随着节点数量增加和成功交换概率降低而增大。我们的工作揭示了如何利用具有截止的中间中继器链在大型量子网络中分发纠缠对。