Abstract

Entanglement generation in long-distance quantum networks is challenging because resources are limited and entanglement swapping is probabilistic. To maximize the rate of successful requests, existing quantum routing algorithms often rely on computationally expensive methods such as Integer Linear Programming (ILP) to determine which links to entangle and use for end-To-end entanglement generation. However, these approaches fail to meet the latency requirements of real-world quantum networks. In this study, we propose a Reinforcement Learning (RL)-based model that determines which links to entangle in each time slot, replacing the slow ILP-based link-selection phase used in prior algorithms. The proposed Deep Q-learning model is up to 19.2x faster than linear programming in link-selection while maintaining comparable routing performance. The RL link-selection model alone matches ILP in request success rate; combining RL link selection with entanglement caching and proactive swapping raises throughput by up to 52.55% over ILP. Overall, our approach achieves success rates exceeding state-of-The-Art solutions while reducing execution time by more than an order of magnitude. Experiments on a synthetic 50-node Waxman topology and the real-world 54-node SURFnet core topology confirm that these gains generalize across network structures, providing a practical path toward scalable quantum routing.

Department(s)

Computer Science

Publication Status

Open Access

Keywords and Phrases

entanglement generation; quantum networks; quantum routing; reinforcement learning

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2026 The Authors, All rights reserved.

Creative Commons Licensing

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Publication Date

03 Sep 2026

Download

Full Text Link

Share

 
COinS