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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
Preface
Author biographies
1 Introduction
2 Quantum information processing
3 Information encoding
4 Quantum computing for inference
5 Quantum variational optimization
6 Variational classifiers and neural networks
7 Quantum generative models
8 Theory, expressivity, and learning bounds