Overview
- Explains relevant concepts and terminology from machine learning and quantum information
- Critically reviews challenges that are a common theme in the literature
- Focuses on the developments in near-term quantum machine learning in this second edition
Part of the book series: Quantum Science and Technology (QST)
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About this book
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.
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Keywords
Table of contents (9 chapters)
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Front Matter
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Back Matter
Authors and Affiliations
About the authors
Maria Schuld works as a researcher for the Toronto-based quantum computing start-up Xanadu. She received her Ph.D. from the University of KwaZulu-Natal in 2017, where she began working on the intersection between quantum computing and machine learning in 2013. Besides her numerous contributions to the field, she is a co-developer for the open-source quantum machine learning software framework PennyLane.
Francesco Petruccione received his Ph.D. (1988) and “Habilitation” (1994) from the University of Freiburg, Germany. Since 2004, he has been a professor of Theoretical Physics at the University of KwaZulu-Natal in Durban, South Africa, where in 2007, he was granted a South African Research Chair for Quantum Information Processing and Communication. He is the co-author of “The Theory of Open Quantum Systems” (Oxford University Press, 2002) and has published more than 250 papers in refereed journals. Francesco Petruccione’s research focuses on open quantum systems and quantum information processing and communication.
Bibliographic Information
Book Title: Machine Learning with Quantum Computers
Authors: Maria Schuld, Francesco Petruccione
Series Title: Quantum Science and Technology
DOI: https://doi.org/10.1007/978-3-030-83098-4
Publisher: Springer Cham
eBook Packages: Physics and Astronomy, Physics and Astronomy (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-83097-7Published: 18 October 2021
Softcover ISBN: 978-3-030-83100-4Published: 19 October 2022
eBook ISBN: 978-3-030-83098-4Published: 17 October 2021
Series ISSN: 2364-9054
Series E-ISSN: 2364-9062
Edition Number: 2
Number of Pages: XIV, 312
Number of Illustrations: 30 b/w illustrations, 74 illustrations in colour
Additional Information: Originally published with the title: Supervised Learning with Quantum Computers
Topics: Quantum Computing, Machine Learning, Mathematics, general