Quantum Machine Learning: Concepts and possibilities

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Kathleen E. Hamilton (Oak Ridge National Laboratory (United States)) Andrea Delgado (Oak Ridge National Laboratory (United States))

Quantum Machine Learning

Concepts and possibilities

Kathleen E. Hamilton (Oak Ridge National Laboratory (United States)), Andrea Delgado (Oak Ridge National Laboratory (United States))

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English

Institute of Physics Publishing
15 December 2025

Quantum physics (quantum mechanics & quantum field theory); Artificial intelligence; Machine learning

Series: IOP ebooks

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

By: Kathleen E. Hamilton (Oak Ridge National Laboratory (United States)), Andrea Delgado (Oak Ridge National Laboratory (United States))
Imprint: Institute of Physics Publishing
Country of Publication: United Kingdom
Dimensions: Height: 254mm, Width: 178mm, Spine: 10mm
Weight: 459g
ISBN: 9780750349505
ISBN 10: 0750349506
Series: IOP ebooks
Pages: 136
Publication Date: 15 December 2025
Audience: Professional and scholarly , Undergraduate
Format: Hardback
Publisher's Status: Active

Dr Andrea Delgado is a Research Scientist in the Physics Division and the Quantum Information Science Group at Oak Ridge National Laboratory. Her research focus is on quantum computing applications to high-energy physics. This work combines a scientific interest in extending our knowledge of the fundamental blocks of the universe and how they interact with each other and building better tools to analyze the data from large-scale particle physics experiments such as the LHC. Andrea’s research interests include developing data analysis tools for high-energy physics experiments, including machine learning and quantum computing. She received her Ph.D. from Texas A&M University. Dr Kathleen Hamilton is a Research Scientist in the Quantum Information Science Group at Oak Ridge National Laboratory. Her research covers many different aspects of NISQ-era quantum machine learning including designing new models for sequence prediction, quantum reservoir computing, using machine learning workflows to benchmark near-term quantum devices, and incorporating error mitigation into variational circuit training. She has been a member of the Program Committee for the International Conference on Neuromorphic Systems (ICONS) from 2019-2021, and the Algorithms Track for IEEE's 2020 Quantum Week. She received her Ph.D. from the University of California at Riverside, her M.S. from the University of New Hampshire, and her B.S. from Mary Washington College. She is a member of the American Physical Society and the Society of Industrial and Applied Mathematics.