On quantum methods for machine learning problems part II: Quantum classification algorithms

dc.contributor.author	Ablayev F.
dc.contributor.author	Ablayev M.
dc.contributor.author	Huang J.Z.
dc.contributor.author	Khadiev K.
dc.contributor.author	Salikhova N.
dc.contributor.author	Wu D.
dc.date.accessioned	2021-02-25T06:41:06Z
dc.date.available	2021-02-25T06:41:06Z
dc.date.issued	2020
dc.identifier.issn	2096-0654
dc.identifier.uri	https://dspace.kpfu.ru/xmlui/handle/net/161004
dc.description.abstract	© 2020 The author(s). This is a review of quantum methods for machine learning problems that consists of two parts. The first part, "quantum tools", presented some of the fundamentals and introduced several quantum tools based on known quantum search algorithms. This second part of the review presents several classification problems in machine learning that can be accelerated with quantum subroutines. We have chosen supervised learning tasks as typical classification problems to illustrate the use of quantum methods for classification.
dc.relation.ispartofseries	Big Data Mining and Analytics
dc.subject	Binary classification
dc.subject	Nearest neighbor algorithm
dc.subject	Quantum classification
dc.title	On quantum methods for machine learning problems part II: Quantum classification algorithms
dc.type	Review
dc.relation.ispartofseries-issue	1
dc.relation.ispartofseries-volume	3
dc.collection	Публикации сотрудников КФУ
dc.relation.startpage	56
dc.source.id	SCOPUS20960654-2020-3-1-SID85094629763

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Публикации сотрудников КФУ Scopus [24551]
Коллекция содержит публикации сотрудников Казанского федерального (до 2010 года Казанского государственного) университета, проиндексированные в БД Scopus, начиная с 1970г.