论文标题
具有截止的均质中继器链中的最佳纠缠分配政策
Optimal entanglement distribution policies in homogeneous repeater chains with cutoffs
论文作者
论文摘要
我们使用具有量子记忆的一系列量子中继器研究了两分纠缠分布的极限。为了产生端到端的纠缠，每个节点都可以尝试与邻居纠缠链接，或执行纠缠交换测量。在记忆中强制执行最大的存储时间，称为截止时间，以确保高质量的纠缠。节点遵循确定何时执行每个操作的策略。全球知识政策考虑了有关已经产生的纠缠的所有信息。在这里，我们发现全球知识政策最大程度地减少了产生端到端纠缠的预期时间。我们的方法基于马尔可夫决策过程以及价值和政策迭代。我们将最佳策略与节点仅使用本地信息的策略进行比较。我们发现，随着节点数量的增加和成功交换的可能性，最佳全球知识政策提供的预期交付时间的优势增加。我们的工作阐明了如何使用带有临界值的中间中继器在大量子网络中分布纠缠的对。
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.
