Quantum machine learning :concepts and possibilities /

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"Version: 20251201"--Title page verso.Includes bibliographical references.1. Introduction -- 1.1. Motivation and scope -- 1.2. The promise of quantum machine learning -- 1.3. Quantum computing : a refresher -- 1.4. Machine learning foundations -- 1.5. Types of quantum machine learning2. Quantum information processing -- 2.1. Quantum computing -- 2.2. Dirac notation -- 2.3. Bloch sphere -- 2.4. The Copenhagen interpretation -- 2.5. Quantum gates and circuits -- 2.6. The Deutsch-Jozsa algorithm -- 2.7. Grover's algorithm -- 2.8. Summary3. Information encoding -- 3.1. Bridging two computational realms -- 3.2. Encoding schemes for quantum machine learning -- 3.3. Quantum autoencoders -- 3.4. Quantum data -- 3.5. Practical considerations for quantum encoding -- 3.6. Summary4. Quantum computing for inference -- 4.1. Classical foundations of inference -- 4.2. Quantum feature maps -- 4.3. Quantum kernels and kernel methods -- 4.4. Linear quantum models -- 4.5. Performance and benchmarks -- 4.6. Case studies and applications -- 4.7. Open challenges and future directions -- 4.8. Summary5. Quantum variational optimization -- 5.1. Model description -- 5.2. Case studies and applications -- 5.3. Open challenges and future directions -- 5.4. Summary6. Variational classifiers and neural networks -- 6.1. Model description -- 6.2. Backpropagation and gradient estimation in quantum models -- 6.3. Architectural variants : QCNNs and quantum graph-based models -- 6.4. Open challenges and future directions -- 6.5. Summary7. Quantum generative models -- 7.1. Classical generative models -- 7.2. Quantum generative models -- 7.3. Expressivity and learning power -- 7.4. Training quantum generative models -- 7.5. Case studies and applications -- 7.6. Open challenges and future directions -- 7.7. Summary8. Theory, expressivity, and learning bounds -- 8.1. Definitions and frameworks for expressivity -- 8.2. Learning performance : sample complexity and generalization -- 8.3. Positive results from quantum learning -- 8.4. Limitations, no-go theorems, and dequantization -- 8.5. Open problems and future directions.Full-text restricted to subscribers or individual document purchasers.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. Part of IOP Series in Quantum Technology.Researchers, advanced students from either physics of computer science.Also available in print.Mode of access: World Wide Web.System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.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 PhD 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 PhD from the University of California at Riverside, her MS from the University of New Hampshire, and her BS from Mary Washington College. She is a member of the American Physical Society and the Society of Industrial and Applied Mathematics.Title from PDF title page (viewed on January 8, 2026).

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: .,

Collation

1 online resource (various pagings) :illustrations (some color).

Language

English

ISBN/ISSN

9780750349529

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004.1/1

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-

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Subject(s)

SCIENCE / Physics / Quantum Theory.
Quantum computers.
Machine learning.

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Statement of Responsibility

Andrea Delgado, Kathleen E. Hamilton.

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