Hands On Introduction To Quantum Machine Learning
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Quantum Computing For Dummies
Comprehend the mysteries—and the amazing potential—of quantum computing Quantum computing has the promise to be the next huge thing in technology. How do we know that? Look at how much the big players in tech are investing in the technology. Quantum Computing For Dummies preps you for the amazing changes that are coming with the world of computing built on the phenomena of quantum mechanics. Need to know what is it and how does it work? This easy-to-understand book breaks it down and answers your most pressing questions. Get a better understanding of how quantum computing is revolutionizing networking, data management, cryptography, and artificial intelligence in ways that would have previously been unthinkable. With a Dummies guide by your side, you’ll get a primer on the inner workings and practical applications of quantum computers. Learn the difference binary and quantum computers Discover which industries will be most influenced by quantum computing See how quantum improves encryption and enables business Take a look at how quantum is applied in big data and AI For technologists and IT pros interested in getting on board the quantum train—plus anyone who’s quantum-curious—this Dummies guide is a must-have.
Quantum Machine Learning
Quantum Machine Learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. Paring down the complexity of the disciplines involved, it focuses on providing a synthesis that explains the most important machine learning algorithms in a quantum framework. Theoretical advances in quantum computing are hard to follow for computer scientists, and sometimes even for researchers involved in the field. The lack of a step-by-step guide hampers the broader understanding of this emergent interdisciplinary body of research. Quantum Machine Learning sets the scene for a deeper understanding of the subject for readers of different backgrounds. The author has carefully constructed a clear comparison of classical learning algorithms and their quantum counterparts, thus making differences in computational complexity and learning performance apparent. This book synthesizes of a broad array of research into a manageable and concise presentation, with practical examples and applications. - Bridges the gap between abstract developments in quantum computing with the applied research on machine learning - Provides the theoretical minimum of machine learning, quantum mechanics, and quantum computing - Gives step-by-step guidance to a broader understanding of this emergent interdisciplinary body of research
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.