Scalable quantum networks must support concurrent entanglement requests, yet existing routing protocols fail when users compete for shared repeater resources, wasting fragile quantum states. This paper presents RADAR-Q, a resource-aware decentralized routing protocol embedding real-time resource contention into path selection. Unlike prior designs requiring global coordination or central anchors, RADAR-Q makes intelligent local decisions balancing path length and fidelity, instantaneous quantum memory availability, and intermediate Bell-State Measurement (BSM) operations. By identifying the Nearest Common Ancestor (NCA) within a DODAG hierarchy, RADAR-Q localizes entanglement swapping close to communicating users - avoiding unnecessary central detours and reducing BSM chain length and decoherence exposure. We evaluate RADAR-Q on grid and random topologies against synchronous and root-centric asynchronous baselines. Results show RADAR-Q achieves aggregate throughputs 2.5x and 7.6x higher than synchronized and root-centric designs, respectively. While baselines suffer catastrophic fidelity collapse below the 0.5 threshold under high load, RADAR-Q consistently maintains end-to-end fidelity above 0.76, ensuring pairs remain usable. Furthermore, RADAR-Q exhibits near-perfect fairness (Jain's Fairness Index 96-98%) and retains over 50% of its ideal throughput under stringent 1.0 ms coherence times. These findings establish contention-aware decentralized routing as a scalable foundation for multi-tenant quantum networks.

翻译：可扩展量子网络必须支持并发纠缠请求，但现有路由协议在用户竞争共享中继资源时失效，造成脆弱量子态的浪费。本文提出RADAR-Q——一种将实时资源竞争嵌入路径选择的资源感知型去中心化路由协议。与需要全局协调或中心锚点的先前设计不同，RADAR-Q可在本地做出智能决策，权衡路径长度与保真度、瞬时量子存储器可用性以及中间贝尔态测量操作。通过在有向无环图层次结构中识别最近公共祖先，RADAR-Q将纠缠交换定位在通信用户附近——避免不必要的中心迂回，并缩短贝尔态测量链长度与退相干暴露时间。我们在网格与随机拓扑上针对同步和根中心异步基线方法评估RADAR-Q。结果表明，相较于同步和根中心设计，RADAR-Q分别实现了2.5倍和7.6倍的总吞吐量提升。当基线方法在高负载下保真度灾难性崩溃至0.5阈值以下时，RADAR-Q始终将端到端保真度维持在0.76以上，确保纠缠对保持可用。此外，RADAR-Q展现出近乎完美的公平性（Jain公平指数为96-98%），且在严格的1.0毫秒相干时间约束下仍能保留其理想吞吐量的50%以上。这些发现确立了基于竞争感知的去中心化路由作为多租户量子网络的可扩展基础架构。