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About this product
Product Identifiers
PublisherSpringer
ISBN-109811968993
ISBN-139789811968990
eBay Product ID (ePID)14064169004
Product Key Features
Number of PagesX, 138 Pages
Publication NameConcise Guide to Quantum Machine Learning
LanguageEnglish
Publication Year2023
SubjectProbability & Statistics / General, Intelligence (Ai) & Semantics
TypeTextbook
Subject AreaMathematics, Computers
AuthorDavide Pastorello
SeriesMachine Learning: Foundations, Methodologies, and Applications Ser.
FormatTrade Paperback
Dimensions
Item Weight10.4 Oz
Item Length10 in
Item Width7 in
Additional Product Features
Dewey Edition23
Reviews"The book under review summarises lecture notes presented by the author for the quantum machine learning MSc course at the University of Trento; it is therefore structured in a student-friendly manner, offering support both on the mathematical side (also with the interpretation of quantum mechanics) and on the algorithmic side. ... The book concludes with an extensive, well-curated set of references, which represent an excellent continuation of quantum approaches." (Irina Ioana Mohorianu, zbMATH 1530.68003, 2024)
Number of Volumes1 vol.
IllustratedYes
Dewey Decimal006.31
Table Of ContentChapter 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.
SynopsisThis 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.
LC Classification NumberQ334-342
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