As quantum networks evolve from experimental testbeds to fault-tolerant systems, the primary performance metric shifts from physical link fidelity to end-to-end logical error rate. However, current control planes remain ill-equipped for this transition: routing decisions are typically decoupled from Quantum Error Correction (QEC) strategies, relying on topology or scalar fidelity metrics that fail to predict how specific physical noise structures interact with logical codes. Optimizing this coupled route-and-code performance requires precise, real-time visibility into network error biases, yet traditional active tomography is operationally prohibitive due to throughput collapse and service interruption. We present SCOPE (Syndrome-based COntrol PlanE), a network-layer architecture that enables joint routing and coding optimization using purely passive telemetry. Instead of injecting probes, SCOPE harvests error syndromes -- the parity-check outcomes naturally generated by QEC decoders during user service. By aggregating these signals, SCOPE's inference engine reconstructs the network's time-varying error map, capturing complex, context-dependent noise correlations. This visibility drives a decision engine that proactively pushes optimal route-and-code configurations to source nodes. NetSquid and IBM-calibrated simulations show that SCOPE reduces estimation error by more than 60% relative to a standard EM baseline. In large-scale networks, this precision reduces logical error rates by 30-35% (up to 65%) against topology-aware baselines.

翻译：随着量子网络从实验测试平台演进为容错系统，核心性能指标从物理链路保真度转向端到端逻辑错误率。然而，当前的控制平面尚未充分适应这一转变：路由决策通常与量子纠错（QEC）策略解耦，仅依赖拓扑或标量保真度指标，无法预测特定物理噪声结构与逻辑码之间的交互。优化这种路由与编码的耦合性能需要精确、实时的网络误差偏置可见性，但传统主动层析成像因吞吐量崩溃和服务中断在操作上不可行。我们提出SCOPE（基于综合征的控制平面），这是一种网络层架构，可通过纯被动遥测实现路由与编码的联合优化。SCOPE不注入探测包，而是采集错误综合征——用户服务期间QEC解码器自然生成的奇偶校验结果。通过聚合这些信号，SCOPE的推理引擎重建网络时变误差图，捕捉复杂、依赖上下文的噪声相关性。这种可见性驱动决策引擎主动向源节点推送最优路由-编码配置。NetSquid与IBM校准仿真表明，相比标准EM基线，SCOPE将估计误差降低超过60%。在大规模网络中，相比拓扑感知基线，这种精确性使逻辑错误率降低30-35%（最高达65%）。