Speeding up quantum perceptron via shortcuts to adiabaticity

Abstract The quantum perceptron is a fundamental building block for quantum machine learning. This is a multidisciplinary field that incorporates abilities of quantum computing, such as state superposition and entanglement, to classical machine learning schemes. Motivated by the techniques of shortc...

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Autores principales:	Yue Ban, Xi Chen, E. Torrontegui, E. Solano, J. Casanova
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0ecaac1ce61f47d596d8f95e67e6c3da
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spelling	oai:doaj.org-article:0ecaac1ce61f47d596d8f95e67e6c3da2021-12-02T11:35:58ZSpeeding up quantum perceptron via shortcuts to adiabaticity10.1038/s41598-021-85208-32045-2322https://doaj.org/article/0ecaac1ce61f47d596d8f95e67e6c3da2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85208-3https://doaj.org/toc/2045-2322Abstract The quantum perceptron is a fundamental building block for quantum machine learning. This is a multidisciplinary field that incorporates abilities of quantum computing, such as state superposition and entanglement, to classical machine learning schemes. Motivated by the techniques of shortcuts to adiabaticity, we propose a speed-up quantum perceptron where a control field on the perceptron is inversely engineered leading to a rapid nonlinear response with a sigmoid activation function. This results in faster overall perceptron performance compared to quasi-adiabatic protocols, as well as in enhanced robustness against imperfections in the controls.Yue BanXi ChenE. TorronteguiE. SolanoJ. CasanovaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yue Ban Xi Chen E. Torrontegui E. Solano J. Casanova Speeding up quantum perceptron via shortcuts to adiabaticity
description	Abstract The quantum perceptron is a fundamental building block for quantum machine learning. This is a multidisciplinary field that incorporates abilities of quantum computing, such as state superposition and entanglement, to classical machine learning schemes. Motivated by the techniques of shortcuts to adiabaticity, we propose a speed-up quantum perceptron where a control field on the perceptron is inversely engineered leading to a rapid nonlinear response with a sigmoid activation function. This results in faster overall perceptron performance compared to quasi-adiabatic protocols, as well as in enhanced robustness against imperfections in the controls.
format	article
author	Yue Ban Xi Chen E. Torrontegui E. Solano J. Casanova
author_facet	Yue Ban Xi Chen E. Torrontegui E. Solano J. Casanova
author_sort	Yue Ban
title	Speeding up quantum perceptron via shortcuts to adiabaticity
title_short	Speeding up quantum perceptron via shortcuts to adiabaticity
title_full	Speeding up quantum perceptron via shortcuts to adiabaticity
title_fullStr	Speeding up quantum perceptron via shortcuts to adiabaticity
title_full_unstemmed	Speeding up quantum perceptron via shortcuts to adiabaticity
title_sort	speeding up quantum perceptron via shortcuts to adiabaticity
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0ecaac1ce61f47d596d8f95e67e6c3da
work_keys_str_mv	AT yueban speedingupquantumperceptronviashortcutstoadiabaticity AT xichen speedingupquantumperceptronviashortcutstoadiabaticity AT etorrontegui speedingupquantumperceptronviashortcutstoadiabaticity AT esolano speedingupquantumperceptronviashortcutstoadiabaticity AT jcasanova speedingupquantumperceptronviashortcutstoadiabaticity
_version_	1718395803371831296

Speeding up quantum perceptron via shortcuts to adiabaticity

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