Quantum Machine Learning: Theory, Algorithms, and Practical Implementation

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 for graduate students, researchers, and practitioners in computer science, physics, engineering, mathematics, and related disciplines. The book provides a comprehensive introduction to the foundations and modern methods of quantum machine learning.It begins with the principles of quantum information, Hilbert spaces, quantum circuits, and quantum algorithms relevant to learning tasks, and then develops the major paradigms of the field, including quantum data encoding, quantum feature maps and kernels, variational quantum circuits, quantum neural networks, quantum generative models, quantum reinforce