Quantum networks are expected to become a key enabler for interconnecting quantum devices. In contrast to classical communication networks, however, information transfer in quantum networks is usually restricted to short distances due to physical constraints of entanglement distribution. Satellites can extend entanglement distribution over long distances, but routing in such networks is challenging because satellite motion and stochastic link generation create a highly dynamic quantum topology. Existing routing methods often rely on global topology information that quickly becomes outdated due to delays in the classical control plane, while decentralized methods typically act on incomplete local information. We propose SatQNet, a reinforcement learning approach for entanglement routing in satellite-assisted quantum networks that can be decentralized at runtime. Its key innovation is an edge-centric directed line graph neural network that performs local message passing on directed edge embeddings, enabling it to better capture link properties in high-degree and time-varying topologies. By exchanging messages with neighboring repeaters, SatQNet learns a local graph representation at runtime that supports agents in establishing high-fidelity end-to-end entanglements. Trained on random graphs, SatQNet outperforms heuristic and learning-based approaches across diverse settings, including a real-world European backbone topology, and generalizes to unseen topologies without retraining.

翻译：量子网络有望成为互联量子设备的关键技术。然而，与经典通信网络不同，量子网络中的信息传输通常受限于纠缠分配的物理约束而只能覆盖短距离。卫星可以扩展纠缠分配的覆盖范围，但此类网络中的路由极具挑战性，因为卫星运动与随机链路生成会导致高度动态的量子拓扑结构。现有路由方法常依赖全局拓扑信息，但由于经典控制平面的延迟，这些信息会迅速过时；而分散式方法通常基于不完整的局部信息进行决策。我们提出SatQNet，一种面向卫星辅助量子网络中纠缠路由的强化学习方法，可在运行时实现分散化。其核心创新在于一种基于边的有向线图神经网络，该网络对定向边嵌入进行局部消息传递，从而能更好地捕捉高度数与时变拓扑中的链路特性。通过与相邻中继器交换消息，SatQNet在运行时学习局部图表示，支持智能体建立高保真端到端纠缠。在随机图上训练后，SatQNet在包括现实欧洲骨干网拓扑在内的多种场景下均优于启发式方法与基于学习方法，且无需重新训练即可泛化至未见拓扑。