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Annals of Telecommunications

A two-stage Q-learning routing approach for quantum entanglement networks

Diego Medeiros De Abreu, Arthur Pimentel, David Moura, Christian Esteve Rothenberg, Antônio Abelém

Published Apr 22, 2025• 1 citation
Publisher
DOI
10.1007/s12243-025-01090-4

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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 bothpath 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 entanglementsuccess and resource efficiency. We further extend DQN to integrate Double DQN, Dueling DQN, and Prioritized ExperienceReplay (PER) architectures. Simulation results indicate that our approach significantly outperforms baseline methods regardingrequest success rate, especially in large-scale networks under strict fidelity requirements.
Quantum Internet aims to enable quantum communication between any two points, offering applications such as quantum key distribution (QKD), distributed quantum computing, and entanglement networks. However, the current quantum technology presents challenges with low entanglement (EPR pairs) generation rates, limited quantum memory capacity, and decoherence rates that often lead to unusable EPR pairs due to low fidelity. This presents a significant challenge for tasks such as routing. In this paper, we tackle this challenge by introducing qRL, a quantum-aware routing protocol that utilizes reinforcement learning to optimize quantum routing decisions. We show that qRL consistently outperforms traditional methods by maintaining higher fidelity routes and request success rates in different network configuration scenarios.