Machine learning enables completely automatic tuning of a quantum device faster than human experts

To optimize operating conditions of large scale semiconductor quantum devices, a large parameter space has to be explored. Here, the authors report a machine learning algorithm to navigate the entire parameter space of gate-defined quantum dot devices, showing about 180 times faster than a pure rand...

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Autores principales:	H. Moon, D. T. Lennon, J. Kirkpatrick, N. M. van Esbroeck, L. C. Camenzind, Liuqi Yu, F. Vigneau, D. M. Zumbühl, G. A. D. Briggs, M. A. Osborne, D. Sejdinovic, E. A. Laird, N. Ares
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/dd86a9c2a00a4779a6625d51bea8a4a4
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spelling	oai:doaj.org-article:dd86a9c2a00a4779a6625d51bea8a4a42021-12-02T17:08:46ZMachine learning enables completely automatic tuning of a quantum device faster than human experts10.1038/s41467-020-17835-92041-1723https://doaj.org/article/dd86a9c2a00a4779a6625d51bea8a4a42020-08-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-17835-9https://doaj.org/toc/2041-1723To optimize operating conditions of large scale semiconductor quantum devices, a large parameter space has to be explored. Here, the authors report a machine learning algorithm to navigate the entire parameter space of gate-defined quantum dot devices, showing about 180 times faster than a pure random search.H. MoonD. T. LennonJ. KirkpatrickN. M. van EsbroeckL. C. CamenzindLiuqi YuF. VigneauD. M. ZumbühlG. A. D. BriggsM. A. OsborneD. SejdinovicE. A. LairdN. AresNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-10 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q H. Moon D. T. Lennon J. Kirkpatrick N. M. van Esbroeck L. C. Camenzind Liuqi Yu F. Vigneau D. M. Zumbühl G. A. D. Briggs M. A. Osborne D. Sejdinovic E. A. Laird N. Ares Machine learning enables completely automatic tuning of a quantum device faster than human experts
description	To optimize operating conditions of large scale semiconductor quantum devices, a large parameter space has to be explored. Here, the authors report a machine learning algorithm to navigate the entire parameter space of gate-defined quantum dot devices, showing about 180 times faster than a pure random search.
format	article
author	H. Moon D. T. Lennon J. Kirkpatrick N. M. van Esbroeck L. C. Camenzind Liuqi Yu F. Vigneau D. M. Zumbühl G. A. D. Briggs M. A. Osborne D. Sejdinovic E. A. Laird N. Ares
author_facet	H. Moon D. T. Lennon J. Kirkpatrick N. M. van Esbroeck L. C. Camenzind Liuqi Yu F. Vigneau D. M. Zumbühl G. A. D. Briggs M. A. Osborne D. Sejdinovic E. A. Laird N. Ares
author_sort	H. Moon
title	Machine learning enables completely automatic tuning of a quantum device faster than human experts
title_short	Machine learning enables completely automatic tuning of a quantum device faster than human experts
title_full	Machine learning enables completely automatic tuning of a quantum device faster than human experts
title_fullStr	Machine learning enables completely automatic tuning of a quantum device faster than human experts
title_full_unstemmed	Machine learning enables completely automatic tuning of a quantum device faster than human experts
title_sort	machine learning enables completely automatic tuning of a quantum device faster than human experts
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/dd86a9c2a00a4779a6625d51bea8a4a4
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Machine learning enables completely automatic tuning of a quantum device faster than human experts

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