混合地-星量子网络的雙目標路由框架
A Bi-Objective Routing Framework for Hybrid Terrestrial-Satellite Quantum Networks
- Binghamton University（宾汉姆顿大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一种混合地-星量子网络的双目标路由框架，联合优化端到端纠缠生成率与保真度，利用帕累托优化和Martins算法实现精确求解，展现出比仅地面网络更丰富的权衡前沿。
AI中文摘要：
混合地-星量子网络将地面光纤基础设施与自由空间链路相结合，以实现远距离纠缠分发。纠缠可以在不同的地面和卫星链路组合上进行路由，从而产生具有不同纠缠生成率（EGR）和保真度的路径。然而，现有的路由算法通常通过优化EGR或保真度，或优化其中一个同时约束另一个，将路由简化为单目标问题，因此未能明确捕捉两者之间的权衡。在本文中，我们提出了一种用于混合量子网络的双目标路由框架，该框架联合优化端到端EGR和保真度。我们将路由问题表述为帕累托优化问题，并证明其具有特殊的数学结构：端到端EGR的瓶颈性质和端到端保真度的乘法性质使得该问题可以转化为MAXMIN-MINSUM双准则路径问题。这一转化使得使用Martins双准则路由算法能够精确地以多项式时间计算最小完整帕累托集。我们的结果表明，混合网络展现出比仅地面网络丰富得多的帕累托前沿，并且所提出的框架通过允许应用根据其EGR和保真度要求选择路径，优于具有代表性的单目标路由策略。
英文摘要：
Hybrid terrestrial-satellite quantum networks combine terrestrial fiber infrastructure with free-space links to enable long-distance entanglement distribution. Entanglement can be routed over different combinations of terrestrial and satellite links, resulting in paths with different entanglement generation rates (EGRs) and fidelities. Existing routing algorithms, however, typically reduce routing to a single-objective problem by optimizing either EGR or fidelity, or by optimizing one while constraining the other, and thus do not explicitly capture the trade-off between the two. In this paper, we present a bi-objective routing framework for hybrid quantum networks that jointly optimizes end-to-end EGR and fidelity. We formulate routing as a Pareto optimization problem and show that it possesses a special mathematical structure: the bottleneck nature of end-to-end EGR and the multiplicative nature of end-to-end fidelity allow the problem to be transformed into a MAXMIN-MINSUM bi-criterion path problem. This transformation enables the exact polynomial-time computation of a minimal complete Pareto set using Martins' bicriterion routing algorithm. Our results demonstrate that hybrid networks expose substantially richer Pareto frontiers than terrestrial-only networks and that the proposed framework outperforms representative single-objective routing policies by allowing applications to select paths based on their EGR and fidelity requirements.
