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.
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Peter Wittek received his PhD in Computer Science from the National University of Singapore, and he also holds an MSc in Mathematics. He is interested in interdisciplinary synergies, such as scalable learning algorithms on supercomputers, computational methods in quantum simulations, and quantum machine learning. He collaborated on these topics during research stints to various institutions, including the Indian Institute of Science, Barcelona Supercomputing Center, Bangor University, Tsinghua University, the Centre for Quantum Technologies, and the Institute of Photonic Sciences. He has been involved in major EU research projects, and obtained several academic and industry grants.Review:
"...represents a nice compact overview over the emerging eld of quantum machine learning for the interested reader." --Zentralblatt MATH
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