缩短近期量子网络中服务时间的工具
Tools for Reducing Service Time in Near-Term Quantum Networks
中文总结 AI 辅助
针对多用户量子网络中纠缠生成尝试失败导致的空闲时间问题，提出基于分析执行模型缩短间隔的方法，可减少网络服务时间，适用于共享硬件的量子网络架构且支持在线使用。
中文摘要 AI 辅助
已有架构被提出用于控制多用户量子网络中的纠缠生成。为给终端节点的本地操作与经典通信留出时间，这些架构在连续批次的纠缠生成尝试间插入固定间隔。当尝试失败时，这会降低网络利用率，使网络在预定间隔期间处于空闲状态。为解决这一局限，我们提出一种新方法，在不违反硬件约束的前提下，通过缩短尝试间的预定间隔来回收该空闲时间。该方法使用分析执行模型优化间隔，以减少应用的总网络服务时间。在Arqon架构内的评估显示，单应用的网络服务时间最多可减少42分钟（7.6%），协同调度时每个应用的服务时间可减少16-29分钟（26-30%）。该方法广泛适用于在纠缠生成与本地操作间共享硬件的量子网络架构，且可被网络调度器在线使用。
英文摘要
Architectures have been proposed to control entanglement generation in multi-user quantum networks. To allow time for local operations and classical communication at end nodes, these architectures insert fixed separations between consecutive batches of entanglement generation attempts. This reduces network utilization when attempts fail, leaving the network idle during the scheduled separation. To address this limitation, we propose a novel method to reclaim this idle time by shortening the scheduled separation between attempts while respecting hardware constraints. The method uses an analytical execution model to optimize the separation and reduce the total network service time of an application. Evaluations within the Arqon architecture show network service time reductions of up to 42 minutes (7.6%) for single applications and 16-29 minutes (26-30%) per application when co-scheduled. The approach applies broadly to quantum network architectures that share hardware between entanglement generation and local operations, and the method can be used online by network schedulers.
