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Quantum machine learning

Record Type:	Electronic resources : Monograph/item
Title/Author:	Quantum machine learningedited by Siddhartha Bhattacharyya ... [et al.]
other author:	Bhattacharyya, Siddhartha,
Published:	Berlin ;De Gruyter,c2020.
Description:	1 online resource (xiii, 118 p.) :ill.
Subject:	Machine learning.
Online resource:	https://www.degruyter.com/isbn/9783110670707
ISBN:	9783110670707$q(electronic bk.)

Quantum machine learning
Quantum machine learning[electronic resource] /edited by Siddhartha Bhattacharyya ... [et al.] - 1st ed. - Berlin ;De Gruyter,c2020. - 1 online resource (xiii, 118 p.) :ill. - De Gruyter frontiers in computational intelligence,62512-8868 ;. - De Gruyter frontiers in computational intelligence ;v. 12..

Includes bibliographical references and index.

Frontmatter --

Quantum-enhanced machine learning refers to quantum algorithms that solve tasks in machine learning, thereby improving a classical machine learning method. Such algorithms typically require one to encode the given classical dataset into a quantum computer, so as to make it accessible for quantum information processing. After this, quantum information processing routines can be applied and the result of the quantum computation is read out by measuring the quantum system. While many proposals of quantum machine learning algorithms are still purely theoretical and require a full-scale universal quantum computer to be tested, others have been implemented on small-scale or special purpose quantum devices.

Mode of access: Internet via World Wide Web.

In English.

ISBN: 9783110670707$q(electronic bk.)

Standard No.: 10.1515/9783110670707doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .Q36 2020eb

Dewey Class. No.: 006.31

Quantum machine learning
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245 0 0 $a Quantum machine learning $h [electronic resource] / $c edited by Siddhartha Bhattacharyya ... [et al.]
250 $a 1st ed.
260 $a Berlin ; $a Boston : $b De Gruyter, $c c2020.
300 $a 1 online resource (xiii, 118 p.) : $b ill.
490 1 $a De Gruyter frontiers in computational intelligence, $x 2512-8868 ; $v 6
504 $a Includes bibliographical references and index.
505 0 0 $t Frontmatter -- $t Contents -- $t List of Contributors -- $t Preface -- $t 1. Introduction to quantum machine learning -- $t 2. Topographic representation for quantum machine learning -- $t 3. Quantum optimization for machine learning -- $t 4. From classical to quantum machine learning -- $t 5. Quantum inspired automatic clustering algorithms: A comparative study of Genetic algorithm and Bat algorithm -- $t 6. Conclusion -- $t Index.
520 $a Quantum-enhanced machine learning refers to quantum algorithms that solve tasks in machine learning, thereby improving a classical machine learning method. Such algorithms typically require one to encode the given classical dataset into a quantum computer, so as to make it accessible for quantum information processing. After this, quantum information processing routines can be applied and the result of the quantum computation is read out by measuring the quantum system. While many proposals of quantum machine learning algorithms are still purely theoretical and require a full-scale universal quantum computer to be tested, others have been implemented on small-scale or special purpose quantum devices.
530 $a Issued also in print.
538 $a Mode of access: Internet via World Wide Web.
546 $a In English.
588 $a Description based on online resource; title from PDF title page (publisher's Web site, viewed 28. Feb 2023)
650 0 $a Machine learning. $3 188639
650 0 $a Quantum theory. $3 199020
700 1 $a Bhattacharyya, Siddhartha, $d 1975- $e editor. $3 717982
830 0 $a De Gruyter frontiers in computational intelligence ; $v v. 12. $3 960152
856 4 0 $u https://www.degruyter.com/isbn/9783110670707