UID:
almahu_9949195322202882
Format:
XIV, 312 p. 104 illus., 74 illus. in color.
,
online resource.
Edition:
2nd ed. 2021.
ISBN:
9783030830984
Series Statement:
Quantum Science and Technology,
Content:
This book offers an introduction into quantum machine learning research, covering approaches that range from "near-term" to fault-tolerant quantum machine learning algorithms, and from theoretical to practical techniques that help us understand how quantum computers can learn from data. Among the topics discussed are parameterized quantum circuits, hybrid optimization, data encoding, quantum feature maps and kernel methods, quantum learning theory, as well as quantum neural networks. The book aims at an audience of computer scientists and physicists at the graduate level onwards. The second edition extends the material beyond supervised learning and puts a special focus on the developments in near-term quantum machine learning seen over the past few years.
Note:
Chapter 1. Introduction -- Chapter 2. Machine Learning -- Chapter 3. Quantum Computing -- Chapter 4. Representing Data on a Quantum Computer -- Chapter 5. Variational Circuits as Machine Learning Models -- Chapter 6. Quantum Models as Kernel Methods -- Chapter 7. Fault-Tolerant Quantum Machine Learning -- Chapter 8. Approaches Based on the Ising Model -- Chapter 9. Potential Quantum Advantages.
In:
Springer Nature eBook
Additional Edition:
Printed edition: ISBN 9783030830977
Additional Edition:
Printed edition: ISBN 9783030830991
Additional Edition:
Printed edition: ISBN 9783030831004
Language:
English
Subjects:
Computer Science
DOI:
10.1007/978-3-030-83098-4
URL:
https://doi.org/10.1007/978-3-030-83098-4
URL:
Volltext
(URL des Erstveröffentlichers)
