Abstract
Quantum routing deals with identifying a set of quantum repeaters to use to create entanglement between distant endpoints. Previous approaches proposed shortest-path and linear programming methods to find a solution to this problem. While the shortest path approach results in suboptimal performance, linear programming takes too long to find a solution as the network size and constraints increase. In this paper, we apply Deep Q-Reinforcement Learning (DQRL) to optimize routing in quantum networks both in terms of execution time and performance. The proposed Quantum Routing Algorithm (QuRA) first chooses which request to schedule among all requests. It then determines which route to take for the selected request. Since the number of all possible routes can be very high, we developed a hop-by-hop decision making model to lower the complexity while still attaining high performance. Experiments show that the proposed QuRA outperforms existing solutions by up to 90% in success rate and up to 79% in execution time. These results highlight QuRA as a scalable and effective solution for intelligent routing in quantum networks.
Recommended Citation
T. Islam et al., "QuRA: Reinforcement Learning Based Routing for Quantum Networks," Proceedings IEEE Consumer Communications and Networking Conference Ccnc, Institute of Electrical and Electronics Engineers, Jan 2026.
The definitive version is available at https://doi.org/10.1109/CCNC65079.2026.11366585
Department(s)
Computer Science
Keywords and Phrases
Deep reinforcement learning; End-to-end entanglement; Entanglement routing; Fidelity-aware routing; Quantum Network
International Standard Serial Number (ISSN)
2331-9860
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2026 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2026
Comments
National Science Foundation, Grant 2427408