Quantum Machine Learning (Hardcover)

Hardcover. The scope of the book spans from the fundamental postulates of quantum mechanics and quantum algorithms that underpin QML, to advanced topics including variational quantum algorithms, quantum neural networks, and quantum generative models. It covers both the theoretical formulations, such as expressivity, generalization bounds, and kernel methods, and practical applications, ranging from optimization and pattern recognition to simulation and sensing. The text also explores hybrid quantum-classical workflows, error mitigation strategies, and benchmarks that connect algorithmic development to near-term hardware implementations. By the end of this book, readers gain a holistic view of the current state, promises, and challenges of QML, as well as directions for future research in this rapidly evolving field.Key Features:A chapter on quantum generative models.Accessible reference text useful for both students and researchers.Case studies This book provides a comprehensive, self-contained introduction to quantum machine learning, uniting the principles of quantum computing and modern learning theory to explore how quantum mechanics can enhance data-driven algorithms and their implementation on near-term quantum devices. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. …