u003cpu003eu003ciu003eQuantum Machine Learningu003c/iu003e 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. u003c/pu003e u003cpu003eu003ciu003eQuantum Machine Learningu003c/iu003e 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.u003c/pu003e u003cbru003eu003cbru003eu003culu003eu003cliu003eBridges the gap between abstract developments in quantum computing with the applied research on machine learningu003c/liu003eu003cliu003eProvides the theoretical minimum of machine learning, quantum mechanics, and quantum computingu003c/liu003eu003cliu003eGives step-by-step guidance to a broader understanding of this emergent interdisciplinary body of researchu003c/liu003eu003c/ulu003e