面向混合量子网络的可扩展纠缠分发框架

中文摘要 AI 辅助

可扩展量子网络（QN）的实现依赖于跨异构硬件平台的高效纠缠分发。传统量子网络框架通常局限于单一编码方案，要么完全依赖离散变量（DV），要么完全依赖连续变量（CV），这种分离式架构限制了大规模系统的容量和互操作性。在此，我们提出一种可扩展的混合纠缠分发方案，通过将DV-CV接口处的混合纠缠交换与纠缠浓缩映射到统一的串联和并联图规则集，从而弥合这两个领域。我们证明，与传统的点对点最大纠缠态生成相比，我们的混合方法展现出潜在的性能优势，在串联-并联拓扑中实现了增强的端到端纠缠。这些结果为设计未来量子互联网中资源高效、可解释的架构提供了坚实的理论基础。

英文摘要

The realization of a scalable quantum network (QN) hinges on the efficient distribution of entanglement across heterogeneous hardware platforms. While traditional QN frameworks are often restricted to single-encoding schemes, relying exclusively on either discrete (DV) or continuous (CV) variables, such segregated architectures limit the capacity and interoperability of large-scale systems. Here, we propose a scalable hybrid entanglement distribution scheme that bridges these domains by mapping hybrid entanglement swapping and concentration at DV-CV interfaces onto a unified set of series and parallel graph rules. We demonstrate that our hybrid approach exhibits a potential performance advantage over traditional point-to-point generation of maximally entangled states, achieving enhanced end-to-end entanglement within series-parallel topologies. These results provide a robust theoretical foundation for designing resource-efficient, interpretable architectures for the future quantum internet.

发表机构

• Xi’an University of Technology（西安工业大学）
• Taiyuan University of Technology（太原理工大学）
• Rensselaer Polytechnic Institute（伦斯勒理工学院）
• Okinawa Institute of Science and Technology Graduate University（冲绳科学技术大学院大学）
• Northwestern University（西北大学）
• the University of Chicago（芝加哥大学）

机构由 AI 辅助整理，请以论文原文为准。