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A hybrid classical-quantum approach to speed-up Q-learning
We introduce a classical-quantum hybrid approach to computation, allowing for a quadratic performance improvement in the decision process of a learning agent. Using the paradigm of quantum accelerators, we introduce a routine that runs on a quantum computer, which allows for the encoding of probabil...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9995512/ https://www.ncbi.nlm.nih.gov/pubmed/36890198 http://dx.doi.org/10.1038/s41598-023-30990-5 |
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author | Sannia, A. Giordano, A. Gullo, N. Lo Mastroianni, C. Plastina, F. |
author_facet | Sannia, A. Giordano, A. Gullo, N. Lo Mastroianni, C. Plastina, F. |
author_sort | Sannia, A. |
collection | PubMed |
description | We introduce a classical-quantum hybrid approach to computation, allowing for a quadratic performance improvement in the decision process of a learning agent. Using the paradigm of quantum accelerators, we introduce a routine that runs on a quantum computer, which allows for the encoding of probability distributions. This quantum routine is then employed, in a reinforcement learning set-up, to encode the distributions that drive action choices. Our routine is well-suited in the case of a large, although finite, number of actions and can be employed in any scenario where a probability distribution with a large support is needed. We describe the routine and assess its performance in terms of computational complexity, needed quantum resource, and accuracy. Finally, we design an algorithm showing how to exploit it in the context of Q-learning. |
format | Online Article Text |
id | pubmed-9995512 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-99955122023-03-10 A hybrid classical-quantum approach to speed-up Q-learning Sannia, A. Giordano, A. Gullo, N. Lo Mastroianni, C. Plastina, F. Sci Rep Article We introduce a classical-quantum hybrid approach to computation, allowing for a quadratic performance improvement in the decision process of a learning agent. Using the paradigm of quantum accelerators, we introduce a routine that runs on a quantum computer, which allows for the encoding of probability distributions. This quantum routine is then employed, in a reinforcement learning set-up, to encode the distributions that drive action choices. Our routine is well-suited in the case of a large, although finite, number of actions and can be employed in any scenario where a probability distribution with a large support is needed. We describe the routine and assess its performance in terms of computational complexity, needed quantum resource, and accuracy. Finally, we design an algorithm showing how to exploit it in the context of Q-learning. Nature Publishing Group UK 2023-03-08 /pmc/articles/PMC9995512/ /pubmed/36890198 http://dx.doi.org/10.1038/s41598-023-30990-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Sannia, A. Giordano, A. Gullo, N. Lo Mastroianni, C. Plastina, F. A hybrid classical-quantum approach to speed-up Q-learning |
title | A hybrid classical-quantum approach to speed-up Q-learning |
title_full | A hybrid classical-quantum approach to speed-up Q-learning |
title_fullStr | A hybrid classical-quantum approach to speed-up Q-learning |
title_full_unstemmed | A hybrid classical-quantum approach to speed-up Q-learning |
title_short | A hybrid classical-quantum approach to speed-up Q-learning |
title_sort | hybrid classical-quantum approach to speed-up q-learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9995512/ https://www.ncbi.nlm.nih.gov/pubmed/36890198 http://dx.doi.org/10.1038/s41598-023-30990-5 |
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