Basic protocols in quantum reinforcement learning with superconducting circuits

Abstract Superconducting circuit technologies have recently achieved quantum protocols involving closed feedback loops. Quantum artificial intelligence and quantum machine learning are emerging fields inside quantum technologies which may enable quantum devices to acquire information from the outer...

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Autor principal:	Lucas Lamata
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0f9615527a5f4c62889f5fac8ed08319
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spelling	oai:doaj.org-article:0f9615527a5f4c62889f5fac8ed083192021-12-02T12:32:55ZBasic protocols in quantum reinforcement learning with superconducting circuits10.1038/s41598-017-01711-62045-2322https://doaj.org/article/0f9615527a5f4c62889f5fac8ed083192017-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-01711-6https://doaj.org/toc/2045-2322Abstract Superconducting circuit technologies have recently achieved quantum protocols involving closed feedback loops. Quantum artificial intelligence and quantum machine learning are emerging fields inside quantum technologies which may enable quantum devices to acquire information from the outer world and improve themselves via a learning process. Here we propose the implementation of basic protocols in quantum reinforcement learning, with superconducting circuits employing feedback- loop control. We introduce diverse scenarios for proof-of-principle experiments with state-of-the-art superconducting circuit technologies and analyze their feasibility in presence of imperfections. The field of quantum artificial intelligence implemented with superconducting circuits paves the way for enhanced quantum control and quantum computation protocols.Lucas LamataNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-10 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Lucas Lamata Basic protocols in quantum reinforcement learning with superconducting circuits
description	Abstract Superconducting circuit technologies have recently achieved quantum protocols involving closed feedback loops. Quantum artificial intelligence and quantum machine learning are emerging fields inside quantum technologies which may enable quantum devices to acquire information from the outer world and improve themselves via a learning process. Here we propose the implementation of basic protocols in quantum reinforcement learning, with superconducting circuits employing feedback- loop control. We introduce diverse scenarios for proof-of-principle experiments with state-of-the-art superconducting circuit technologies and analyze their feasibility in presence of imperfections. The field of quantum artificial intelligence implemented with superconducting circuits paves the way for enhanced quantum control and quantum computation protocols.
format	article
author	Lucas Lamata
author_facet	Lucas Lamata
author_sort	Lucas Lamata
title	Basic protocols in quantum reinforcement learning with superconducting circuits
title_short	Basic protocols in quantum reinforcement learning with superconducting circuits
title_full	Basic protocols in quantum reinforcement learning with superconducting circuits
title_fullStr	Basic protocols in quantum reinforcement learning with superconducting circuits
title_full_unstemmed	Basic protocols in quantum reinforcement learning with superconducting circuits
title_sort	basic protocols in quantum reinforcement learning with superconducting circuits
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/0f9615527a5f4c62889f5fac8ed08319
work_keys_str_mv	AT lucaslamata basicprotocolsinquantumreinforcementlearningwithsuperconductingcircuits
_version_	1718393900811419648

Basic protocols in quantum reinforcement learning with superconducting circuits

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