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arXiv Machine Learning By Leon Rode, Sumeet Khatri, Supartha Podder

Learning and interpreting policies for simultaneous entanglement requests in quantum networks

Read the original on arXiv Machine Learning →

The paper presents a reinforcement‑learning approach to schedule link‑level entanglement in quantum networks, using a Markov Decision Process and double deep Q‑networks with message‑passing neural networks. The resulting policies achieve 100% success rates even when the link activation probability is reduced by up to 71% compared to baseline heuristics, and maintain at least 80% success when task placements are hardware‑restricted. The authors also develop metrics to interpret the learned policy and employ a large language model to generate a heuristic that matches the DQN performance, suggesting a scalable method for extracting interpretable strategies in large quantum networks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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