Experimental semi-autonomous eigensolver using reinforcement learning

Abstract The characterization of observables, expressed via Hermitian operators, is a crucial task in quantum mechanics. For this reason, an eigensolver is a fundamental algorithm for any quantum technology. In this work, we implement a semi-autonomous algorithm to obtain an approximation of the eig...

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Autores principales:	C.-Y. Pan, M. Hao, N. Barraza, E. Solano, F. Albarrán-Arriagada
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/af1127bdbeb84e349f4405ed5694d843
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id	oai:doaj.org-article:af1127bdbeb84e349f4405ed5694d843
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spelling	oai:doaj.org-article:af1127bdbeb84e349f4405ed5694d8432021-12-02T17:47:03ZExperimental semi-autonomous eigensolver using reinforcement learning10.1038/s41598-021-90534-72045-2322https://doaj.org/article/af1127bdbeb84e349f4405ed5694d8432021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90534-7https://doaj.org/toc/2045-2322Abstract The characterization of observables, expressed via Hermitian operators, is a crucial task in quantum mechanics. For this reason, an eigensolver is a fundamental algorithm for any quantum technology. In this work, we implement a semi-autonomous algorithm to obtain an approximation of the eigenvectors of an arbitrary Hermitian operator using the IBM quantum computer. To this end, we only use single-shot measurements and pseudo-random changes handled by a feedback loop, reducing the number of measures in the system. Due to the classical feedback loop, this algorithm can be cast into the reinforcement learning paradigm. Using this algorithm, for a single-qubit observable, we obtain both eigenvectors with fidelities over 0.97 with around 200 single-shot measurements. For two-qubits observables, we get fidelities over 0.91 with around 1500 single-shot measurements for the four eigenvectors, which is a comparatively low resource demand, suitable for current devices. This work is useful to the development of quantum devices able to decide with partial information, which helps to implement future technologies in quantum artificial intelligence.C.-Y. PanM. HaoN. BarrazaE. SolanoF. Albarrán-ArriagadaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q C.-Y. Pan M. Hao N. Barraza E. Solano F. Albarrán-Arriagada Experimental semi-autonomous eigensolver using reinforcement learning
description	Abstract The characterization of observables, expressed via Hermitian operators, is a crucial task in quantum mechanics. For this reason, an eigensolver is a fundamental algorithm for any quantum technology. In this work, we implement a semi-autonomous algorithm to obtain an approximation of the eigenvectors of an arbitrary Hermitian operator using the IBM quantum computer. To this end, we only use single-shot measurements and pseudo-random changes handled by a feedback loop, reducing the number of measures in the system. Due to the classical feedback loop, this algorithm can be cast into the reinforcement learning paradigm. Using this algorithm, for a single-qubit observable, we obtain both eigenvectors with fidelities over 0.97 with around 200 single-shot measurements. For two-qubits observables, we get fidelities over 0.91 with around 1500 single-shot measurements for the four eigenvectors, which is a comparatively low resource demand, suitable for current devices. This work is useful to the development of quantum devices able to decide with partial information, which helps to implement future technologies in quantum artificial intelligence.
format	article
author	C.-Y. Pan M. Hao N. Barraza E. Solano F. Albarrán-Arriagada
author_facet	C.-Y. Pan M. Hao N. Barraza E. Solano F. Albarrán-Arriagada
author_sort	C.-Y. Pan
title	Experimental semi-autonomous eigensolver using reinforcement learning
title_short	Experimental semi-autonomous eigensolver using reinforcement learning
title_full	Experimental semi-autonomous eigensolver using reinforcement learning
title_fullStr	Experimental semi-autonomous eigensolver using reinforcement learning
title_full_unstemmed	Experimental semi-autonomous eigensolver using reinforcement learning
title_sort	experimental semi-autonomous eigensolver using reinforcement learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/af1127bdbeb84e349f4405ed5694d843
work_keys_str_mv	AT cypan experimentalsemiautonomouseigensolverusingreinforcementlearning AT mhao experimentalsemiautonomouseigensolverusingreinforcementlearning AT nbarraza experimentalsemiautonomouseigensolverusingreinforcementlearning AT esolano experimentalsemiautonomouseigensolverusingreinforcementlearning AT falbarranarriagada experimentalsemiautonomouseigensolverusingreinforcementlearning
_version_	1718379547536130048

Experimental semi-autonomous eigensolver using reinforcement learning

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