Quantum algorithms for topological and geometric analysis of data

Persistent homology allows identification of topological features in data sets, allowing the efficient extraction of useful information. Here, the authors propose a quantum machine learning algorithm that provides an exponential speed up over known algorithms for topological data analysis.

Guardado en:

Detalles Bibliográficos
Autores principales:	Seth Lloyd, Silvano Garnerone, Paolo Zanardi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2016
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/70f1e67b437a4e34a6db16ebd4c0eb3d
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:70f1e67b437a4e34a6db16ebd4c0eb3d
record_format	dspace
spelling	oai:doaj.org-article:70f1e67b437a4e34a6db16ebd4c0eb3d2021-12-02T14:39:29ZQuantum algorithms for topological and geometric analysis of data10.1038/ncomms101382041-1723https://doaj.org/article/70f1e67b437a4e34a6db16ebd4c0eb3d2016-01-01T00:00:00Zhttps://doi.org/10.1038/ncomms10138https://doaj.org/toc/2041-1723Persistent homology allows identification of topological features in data sets, allowing the efficient extraction of useful information. Here, the authors propose a quantum machine learning algorithm that provides an exponential speed up over known algorithms for topological data analysis.Seth LloydSilvano GarneronePaolo ZanardiNature PortfolioarticleScienceQENNature Communications, Vol 7, Iss 1, Pp 1-7 (2016)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Seth Lloyd Silvano Garnerone Paolo Zanardi Quantum algorithms for topological and geometric analysis of data
description	Persistent homology allows identification of topological features in data sets, allowing the efficient extraction of useful information. Here, the authors propose a quantum machine learning algorithm that provides an exponential speed up over known algorithms for topological data analysis.
format	article
author	Seth Lloyd Silvano Garnerone Paolo Zanardi
author_facet	Seth Lloyd Silvano Garnerone Paolo Zanardi
author_sort	Seth Lloyd
title	Quantum algorithms for topological and geometric analysis of data
title_short	Quantum algorithms for topological and geometric analysis of data
title_full	Quantum algorithms for topological and geometric analysis of data
title_fullStr	Quantum algorithms for topological and geometric analysis of data
title_full_unstemmed	Quantum algorithms for topological and geometric analysis of data
title_sort	quantum algorithms for topological and geometric analysis of data
publisher	Nature Portfolio
publishDate	2016
url	https://doaj.org/article/70f1e67b437a4e34a6db16ebd4c0eb3d
work_keys_str_mv	AT sethlloyd quantumalgorithmsfortopologicalandgeometricanalysisofdata AT silvanogarnerone quantumalgorithmsfortopologicalandgeometricanalysisofdata AT paolozanardi quantumalgorithmsfortopologicalandgeometricanalysisofdata
_version_	1718390598757515264

Quantum algorithms for topological and geometric analysis of data

Ejemplares similares