Two-Stage Deep Q Learning Routing in Entanglement Networks

Efficient routing in entanglement distribution networks is a challenging task that requires the coordinated allocation of quantum memories, repeaters, and quantum operations such as entanglement swapping and distillation across dynamic network conditions. In this paper, we present a Deep Q-Learning (DQN) Two-Stage Routing strategy designed to improve both
path and quantum operations selection. Our approach enables the agent to make decisions over a diverse action space, including request scheduling, multipath routing, and the strategic application of quantum operations to maximize entanglement
success and resource efficiency. We further extend DQN to integrate Double DQN, Dueling DQN, and Prioritized Experience
Replay (PER) architectures. Simulation results indicate that our approach significantly outperforms baseline methods regarding
request success rate, especially in large-scale networks under strict fidelity requirements.