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Quantum Machine Learning
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Table of Contents

Introduction Chapter 1: Machine Learning Chapter 2: Quantum Mechanics Chapter 3: Quantum Computing Chapter 4: Unsupervised Learning Chapter 5: Pattern Recognition and Neural Networks Chapter 6: Supervised Learning and SUpport Vector Machines Chapter 7: Regression Analysis Chapter 8: Boosting Chapter 9: Clustering Structure and Quantum Computing Chapter 10: Quantum Pattern Recognition Chapter 11: Quantum Classification Chapter 12: Quantum Process Tomography Chapter 13: Boosting and Adiabatic Quantum Computing

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Captures a broad array of highly specialized content in an accessible and up-to-date review of the growing academic field of quantum machine learning and its applications in industry

About the Author

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.

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"...represents a nice compact overview over the emerging eld of quantum machine learning for the interested reader." --Zentralblatt MATH

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