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Quantum Machine Learning: Theory, Algorithms, and Practical Implementation book cover

1st Edition

Quantum Machine Learning Theory, Algorithms, and Practical Implementation

By Hamid D. Ismail Copyright 2027
492 Pages 75 B/W Illustrations
by CRC Press

492 Pages 75 B/W Illustrations
by CRC Press

Quantum machine learning has emerged as a rapidly developing field at the intersection of quantum computing, artificial intelligence, and data science. As quantum hardware and algorithms continue to advance, there is a growing need for a rigorous and accessible text that explains how quantum principles can be used to design, analyze, and implement machine learning models. This book is intended... Read more

Preface. List of Abbreviations. Chapter 1: Introduction to Quantum Machine Learning. Chapter 2: Quantum Information and Mathematical Foundations for QML. Chapter 3: Classical Machine Learning Essentials for Quantum Machine Learning. Chapter 4: Quantum Algorithms Relevant to Machine Learning. Chapter 5: Quantum Data Encoding and Feature Maps. Chapter 6: Quantum Feature Spaces and Kernels. Chapter 7: Optimization Methods for Quantum Machine Learning. Chapter 8: Quantum Kernel Methods. Chapter 9: Variational Quantum Models. Chapter 10: Quantum Generative Models. Chapter 11: Quantum Reinforcement Learning. Chapter 12: Quantum Transfer Learning. Chapter 13: Quantum Linear Algebra for Machine Learning. Chapter 14: Learning Theory of Quantum Models. Chapter 15: Tensor Networks and Quantum-Inspired Machine Learning. Appendix. Bibliography.

Biography

Hamid Ismail, Ph.D., is a faculty member in the Department of Computational Data Science and Engineering at North Carolina A&T State University. His research spans bioinformatics, high-performance computing, quantum computing, and machine learning. He is the author of several textbooks in bioinformatics and statistics.

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