End-to-end entanglement request scheduling in quantum networks via topology-aware decision transformer

Quantum networks represent promising foundations for secure communication, distributed quantum computing, and advanced quantum-enabled services. However, their deployment faces practical challenges including limited quantum resources, short coherence times, and environmental disturbances. Effective end-to-end entanglement request scheduling is critical to addressing these challenges, as it directly affects resource utilization and network reliability. Although end-to-end entanglement service rate is one of the representative performance measures in the quantum networks, the explicit optimization of the rate under the realistic constraints is relatively unexplored in the previous studies. This paper proposes an offline reinforcement learning (RL)-based scheduling framework, employing a decision transformer integrated with graph attention networks, to specifically optimize service rates within practical operational constraints, such as the single-time-slot usage limitation of quantum links. Our approach adaptively leverages network topology and operational dynamics to enhance scheduling decisions. Simulation studies conducted on the NetSquid platform across four quantum network topologies demonstrate that our model consistently outperforms both a conventional rule-based method and a baseline offline RL method in terms of service rate, while preserving fidelity and maintaining delays within acceptable levels. These results confirm the effectiveness of the proposed method for practical quantum network management.