On quantum methods for machine learning problems part I: Quantum tools

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	2021-02-25T06:41:05Z
dc.date.available	2021-02-25T06:41:05Z
dc.date.issued	2020
dc.identifier.issn	2096-0654
dc.identifier.uri	https://dspace.kpfu.ru/xmlui/handle/net/161003
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", presents the fundamentals of qubits, quantum registers, and quantum states, introduces important quantum tools based on known quantum search algorithms and SWAP-test, and discusses the basic quantum procedures used for quantum search methods. The second part, "quantum classification algorithms", introduces several classification problems that can be accelerated by using quantum subroutines and discusses the quantum methods used for classification.
dc.relation.ispartofseries	Big Data Mining and Analytics
dc.subject	Machine learning
dc.subject	Quantum algorithm
dc.subject	Quantum programming
dc.title	On quantum methods for machine learning problems part I: Quantum tools
dc.type	Review
dc.relation.ispartofseries-issue	1
dc.relation.ispartofseries-volume	3
dc.collection	Публикации сотрудников КФУ
dc.relation.startpage	41
dc.source.id	SCOPUS20960654-2020-3-1-SID85093295685

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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.