In the noisy intermediate-scale quantum era, scientists are trying to improve the entanglement swapping success rate by researching anti-noise technology on the physical level, thereby obtaining a higher generation rate of long-distance entanglement. However, we may improve the generation rate from another perspective, which is studying an efficient entanglement swapping strategy. This paper analyzes the challenges faced by existing entanglement swapping strategies, including the node allocation principle, time synchronization, and processing of entanglement swapping failure. We present Parallel Segment Entanglement Swapping (PSES) to solve these problems. The core idea of PSES is to segment the path and perform parallel entanglement swapping between segments to improve the generation rate of long-distance entanglement. We construct a tree-like model as the carrier of PSES and propose heuristic algorithms called Layer Greedy and Segment Greedy to transform the path into a tree-like model. Moreover, we realize the time synchronization and design the on-demand retransmission mechanism to process entanglement swapping failure. The experiments show that PSES performs superiorly to other entanglement swapping strategies, and the on-demand retransmission mechanism can reduce the average entanglement swapping time by 80% and the average entanglement consumption by 80%.

翻译：在嘈杂中等规模量子时代，科学家们正试图通过在物理层面研究抗噪声技术来提高纠缠交换的成功率，从而获得更高的长距离纠缠生成率。然而，我们可以从另一个角度提高生成率，即研究高效的纠缠交换策略。本文分析了现有纠缠交换策略面临的挑战，包括节点分配原则、时间同步以及纠缠交换失败的处理。我们提出了并行分段纠缠交换（PSES）来解决这些问题。PSES的核心思想是将路径分段，并在段间并行执行纠缠交换，以提高长距离纠缠的生成率。我们构建了一个树状模型作为PSES的载体，并提出了称为层贪婪和段贪婪的启发式算法，将路径转化为树状模型。此外，我们实现了时间同步，并设计了按需重传机制来处理纠缠交换失败。实验表明，PSES的性能优于其他纠缠交换策略，且按需重传机制能将平均纠缠交换时间降低80%，平均纠缠消耗降低80%。