Parallel Segment Entanglement Swapping

TL;DR

This paper introduces Parallel Segment Entanglement Swapping (PSES), a strategy that segments the path and performs parallel swapping to significantly improve long-distance entanglement generation in quantum networks.

Contribution

The paper proposes PSES, a novel parallel entanglement swapping strategy with heuristic algorithms and failure handling mechanisms, enhancing efficiency over existing methods.

Findings

PSES outperforms other strategies in entanglement generation rate.

On-demand retransmission reduces swapping time and consumption by 80%.

Tree-like model and algorithms enable effective path segmentation and synchronization.

Abstract

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%.