Introduction to quantum machine learning

Title

Introduction to quantum machine learning

Creator

Dey S.; De S.; Bhattacharyya S.

Description

Quantum Machine Learning (QML) is popularly known to be an integrative approach to learning of the Quantum Physics (QP) and Machine Learning (ML). In this chapter, an outline of the fundamental ideas and features related to quantum machine learning is laid out. The different facets of quantum algorithms are discussed in this chapter. In addition to this, the basic features of quantum reinforcement learning and quantum annealing are also provided in this chapter. Finally, the chapter deliberates about the advancement of quantum neural networks to through light in the direction of QML. 2020 Walter de Gruyter GmbH, Berlin/Boston. All rights reserved.

Source

Quantum Machine Learning, pp. 1-10.

Date

2020-01-01

Publisher

De Gruyter

Subject

Grover's Search Algorithm; Machine learning; Quantum annealing; Quantum neural networks; Reinforcement learning

Coverage

Dey S., Department of Computer Science Sukanta Mahavidyalaya, Sukanta Nagar Dhupguri, Jalpaiguri, West Bengal, India; De S., Department of Computer Science and Engineering Cooch Behar Government Engineering College Cooch Behar, West Bengal, India; Bhattacharyya S., Department of Computer Science and Engineering CHRIST (Deemed to be University), Bangalore, India

Rights

Restricted Access

Relation

ISBN: 978-311067070-7; 978-311067072-1

Format

Online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.1515/9783110670707-001

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