arXiv Machine Learning July 1, 2026 By Gilberto Cunha, Alexandra Ram\^oa, Andr\'e Sequeira, Michael de Oliveira, Lu\'is Barbosa

Quantum Bayesian Networks Can Speed up Reinforcement Learning in Partially Observable Environments

Read the original on arXiv Machine Learning →

arXiv:2507. 18606v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) provides a principled framework for decision-making in partially observable environments, which can be modeled as Markov decision processes and compactly represented through dynamic decision Bayesian networks.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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