Concise Guide To Quantum Machine Learning
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Concise Guide to Quantum Machine Learning
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.
Concise Guide to Quantum Machine Learning
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.
Computer Safety, Reliability, and Security. SAFECOMP 2024 Workshops
This book constitutes the proceedings of the Workshops held in conjunction with the 43rd International Conference on Computer Safety, Reliability, and Security, SAFECOMP 2024, which took place in Florence, Italy, during September 2024. The 36 papers included in this book were carefully reviewed and selected from a total of 64 submissions to the following workshops: DECSoS 2024 – 19th Workshop on Dependable Smart Embedded and Cyber-Physical Systems and Systems-of-Systems SASSUR 2024 - 11th International Workshop on Next Generation of System Assurance Approaches for Critical Systems TOASTS 2024 – Towards A Safer Systems’ Architecture Through Security WAISE 2024 – 7th International Workshop on Artificial Intelligence Safety Engineering