Article
Quantum reinforcement learning in the presence of thermal dissipation
Author/s | Olivera Atencio, María Laura
Lamata Manuel, Lucas Morillo Buzón, Manuel Casado Pascual, Jesús |
Department | Universidad de Sevilla. Departamento de Física Atómica, Molecular y Nuclear |
Publication Date | 2023 |
Deposit Date | 2023-08-18 |
Published in |
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Abstract | A study of the effect of thermal dissipation on quantum reinforcement learning is performed. For this purpose,
a nondissipative quantum reinforcement learning protocol is adapted to the presence of thermal dissipation.
... A study of the effect of thermal dissipation on quantum reinforcement learning is performed. For this purpose, a nondissipative quantum reinforcement learning protocol is adapted to the presence of thermal dissipation. Analytical calculations as well as numerical simulations are carried out, obtaining evidence that dissipation does not significantly degrade the performance of the quantum reinforcement learning protocol for sufficiently low temperatures, in some cases even being beneficial. Quantum reinforcement learning under realistic experimental conditions of thermal dissipation opens an avenue for the realization of quantum agents to be able to interact with a changing environment, as well as adapt to it, with many plausible applications inside quantum technologies and machine learning |
Funding agencies | Junta de Andalucía Universidad de Sevilla Ministerio de Ciencia, Innovación y Universidades (MICINN). España |
Project ID. | P20-00617
US-1380840 PID2019-104002GB-C21 PID2019-104002GB-C22 |
Citation | Olivera Atencio, M.L., Lamata Manuel, L., Morillo Buzón, M. y Casado Pascual, J. (2023). Quantum reinforcement learning in the presence of thermal dissipation. Physical Review E, 108 (1), 014128. https://doi.org/10.1103/PhysRevE.108.014128. |
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PhysRevE.108.014128.pdf | 644.2Kb | [PDF] | View/ | |