Overview
- Offers a brief but effective introduction to quantum machine learning
- Reviews those quantum algorithms most relevant to machine learning
- Does not require a background in quantum computing or machine learning
Part of the book series: Machine Learning: Foundations, Methodologies, and Applications (MLFMA)
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Table of contents (10 chapters)
Keywords
About this book
This book offers a brief but effective introduction to quantum machine learning (QML). QML is not merely a translation of classical machine learning techniques into the language of quantum computing, but rather a new approach to data representation and processing. Accordingly, the content is not divided into a “classical part” that describes standard machine learning schemes and a “quantum part” that addresses their quantum counterparts. Instead, to immerse the reader in the quantum realm from the outset, the book starts from fundamental notions of quantum mechanics and quantum computing. Avoiding unnecessary details, it presents the concepts and mathematical tools that are essential for the required quantum formalism. In turn, it reviews those quantum algorithms most relevant to machine learning. Later chapters highlight the latest advances in this field and discuss the most promising directions for future research.
To gain the most from this book, a basic grasp of statistics and linear algebra is sufficient; no previous experience with quantum computing or machine learning is needed. The book is aimed at researchers and students with no background in quantum physics and is also suitable for physicists looking to enter the field of QML.
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Concise Guide to Quantum Machine Learning
Authors: Davide Pastorello
Series Title: Machine Learning: Foundations, Methodologies, and Applications
DOI: https://doi.org/10.1007/978-981-19-6897-6
Publisher: Springer Singapore
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023
Hardcover ISBN: 978-981-19-6896-9Published: 17 December 2022
Softcover ISBN: 978-981-19-6899-0Published: 17 December 2023
eBook ISBN: 978-981-19-6897-6Published: 16 December 2022
Series ISSN: 2730-9908
Series E-ISSN: 2730-9916
Edition Number: 1
Number of Pages: X, 138
Number of Illustrations: 7 b/w illustrations, 5 illustrations in colour
Topics: Artificial Intelligence, Machine Learning, Quantum Computing