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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.
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