Concise Guide to Quantum Machine Learning

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Beschreibung

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.

Über den Autor

Davide Pastorello is an assistant professor in the Department of Information Engineering and Computer Science at the University of Trento.

Zusammenfassung

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

Inhaltsverzeichnis

Chapter 1: Introduction.- Chapter 2: Basics of Quantum Mechanics.- Chapter 3: Basics of Quantum Computing.- Chapter 4: Relevant Quantum Algorithms.- Chapter 5: QML Toolkit.- Chapter 6: Quantum Clustering.- Chapter 7: Quantum Classification.- Chapter 8: Quantum Pattern Recognition.- Chapter 9: Quantum Neural Networks.- Chapter 10: Concluding Remarks.

Details

Erscheinungsjahr:	2022
Genre:	Informatik
Rubrik:	Naturwissenschaften & Technik
Medium:	Buch
Seiten:	148
Reihe:	Machine Learning: Foundations, Methodologies, and Applications
Inhalt:	x 138 S. 7 s/w Illustr. 5 farbige Illustr. 138 p. 12 illus. 5 illus. in color.
ISBN-13:	9789811968969
ISBN-10:	9811968969
Sprache:	Englisch
Ausstattung / Beilage:	HC runder Rücken kaschiert
Einband:	Gebunden
Autor:	Pastorello, Davide
Auflage:	1st ed. 2023
Hersteller:	Springer Singapore Springer Nature Singapore Machine Learning: Foundations, Methodologies, and Applications
Maße:	260 x 183 x 14 mm
Von/Mit:	Davide Pastorello
Erscheinungsdatum:	17.12.2022
Gewicht:	0,489 kg

preigu-id: 123647335