发表机构
Lahore University of Management Sciences（拉合尔管理科学大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出量子效用路由（QUR）框架，将有限尺寸密钥速率直接纳入路由搜索，平衡瓶颈SKR与资源使用，在CV-QKD网络中实现密码性能与资源消耗的可配置权衡。
AI 中文摘要
连续变量量子密钥分发（CV-QKD）网络需要同时考虑密钥生成、中继可信度和有限网络资源的路由策略。我们提出了量子效用路由（QUR），一种用于结合可信点对点链路与不可信CV贝尔测量中继网络的加权路由框架。过渡级可组合有限尺寸密钥速率（SKRs）被直接纳入路由搜索，使QUR能够在瓶颈SKR与路由几何和资源使用之间取得平衡。我们考虑了图优化QUR（QUR$_{\mathrm{go}}$）和固定权重实现（QUR$_{\mathrm{fw}}$），后者利用机器学习避免重复的图特定权重优化，并将两者与A*和Max--Min路由进行基准比较。在默认网络配置下，QUR$_{\mathrm{go}}$和QUR$_{\mathrm{fw}}$相对于A*提高了平均路径SKR，同时比Max--Min需要更少的网络资源。该框架还通过利用不可信基础设施作为测量中继，在广泛的可信节点可用性范围内保持安全连接。这些结果表明，CV-QKD网络中的路由可以被视为密码性能与网络资源消耗之间的可配置权衡。
英文摘要
Continuous-variable quantum key distribution (CV-QKD) networks require routing strategies that account simultaneously for secret key generation, relay trust and finite network resources. We introduce Quantum Utility Routing (QUR), a weighted routing framework for networks combining trusted point-to-point links with untrusted CV Bell measurement relays. Transition level composable finite size secret key rates (SKRs) are incorporated directly into the route search, allowing QUR to balance bottleneck SKR against route geometry and resource use. We consider graph optimized QUR (QUR$_{\mathrm{go}}$) and a fixed weight implementation (QUR$_{\mathrm{fw}}$) that uses machine learning to avoid repeated graph specific weight optimization, and benchmark both against A* and Max--Min routing. At the default network configuration, QUR$_{\mathrm{go}}$ and QUR$_{\mathrm{fw}}$ improve the mean path SKR relative to A* while requiring substantially fewer network resources than Max--Min. The framework also maintains secure connectivity over a broad range of trusted node availability by exploiting untrusted infrastructure as measurement relays. These results demonstrate that routing in CV-QKD networks can be treated as a configurable trade off between cryptographic performance and network resource consumption.
Comments17 pages and 6 figures in the main file and 4 pages in supplementary
