Experimental quantum speed-up in reinforcement learning agents

As the field of artificial intelligence advances, the demand for algorithms that can learn quickly and efficiently increases. An important paradigm within artificial intelligence is reinforcement learning [1], where decision-making entities called agents interact with environments and learn by updat...

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Detalles Bibliográficos
Autores principales: Saggio, V., Asenbeck, B. E., Hamann, A., Strömberg, T., Schiansky, P., Dunjko, V., Friis, N., Harris, N. C., Hochberg, M., Englund, D., Wölk, S., Briegel, H. J., Walther, P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7612051/
https://www.ncbi.nlm.nih.gov/pubmed/33692560
http://dx.doi.org/10.1038/s41586-021-03242-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7612051/
https://www.ncbi.nlm.nih.gov/pubmed/33692560
http://dx.doi.org/10.1038/s41586-021-03242-7

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