Quantum machine learning / edited by Siddhartha Bhattacharyya [and five others].

Format:

Book

Contributor:

Bhattacharyya, Siddhartha, 1975- editor.

Series:

De Gruyter frontiers in computational intelligence ; Volume 6.

De Gruyter frontiers in computational intelligence ; Volume 6

Language:

English

Subjects (All):

Machine learning.

Physical Description:

1 online resource (XIII, 118 p.)

Place of Publication:

Berlin ; Boston : De Gruyter, [2020]

Language Note:

In English.

Summary:

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.

Contents:

Frontmatter

Contents

List of Contributors

Preface

1. Introduction to quantum machine learning

2. Topographic representation for quantum machine learning

3. Quantum optimization for machine learning

4. From classical to quantum machine learning

5. Quantum inspired automatic clustering algorithms: A comparative study of Genetic algorithm and Bat algorithm

6. Conclusion

Index

Notes:

Description based on print version record.

Includes index.

ISBN:

3-11-067070-4

OCLC:

1158179576