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:
| ISBN: | 9780750349505 |
| Publication date: | 15th December 2025 |
| Author: | Kathleen E Hamilton, Andrea Delgado |
| Publisher: | IOP Publishing an imprint of Institute of Physics Publishing |
| Format: | Hardback |
| Pagination: | 300 pages |
| Series: | IOP Ebooks |
| Genres: |
Quantum physics (quantum mechanics and quantum field theory) Machine learning Artificial intelligence |
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:
Quantum Machine Learning features in the following genres: Quantum physics (quantum mechanics and quantum field theory), Machine learning, Artificial intelligence
Quantum Machine Learning is available in Hardback
Quantum Machine Learning was written by Kathleen E Hamilton, Andrea Delgado and published by IOP Publishing an imprint of Institute of Physics Publishing
Quantum Machine Learning has 300 pages
Yes it is part of IOP Ebooks series
£108.00