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.