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Quantum Machine Learning
This textbook provides a comprehensive and structured introduction to Quantum Machine Learning (QML), combining the essential principles of quantum mechanics with modern machine learning methodology.
Designed for students, researchers, and engineers, the book offers a clear progression from foundational mathematical tools to advanced quantum algorithms and practical implementation techniques.
Beginning with the fundamentals of quantum states, operators, entanglement, and quantum gates, the text systematically develops the computational concepts required for QML. Detailed discussions cover quantum data encoding methods, variational quantum circuits, feature maps, hybrid quantum–classical architectures, and key algorithms such as Grover’s search, Shor’s factorization, the HHL algorithm, and quantum principal component analysis.
Designed for students, researchers, and engineers, the book offers a clear progression from foundational mathematical tools to advanced quantum algorithms and practical implementation techniques.
Beginning with the fundamentals of quantum states, operators, entanglement, and quantum gates, the text systematically develops the computational concepts required for QML. Detailed discussions cover quantum data encoding methods, variational quantum circuits, feature maps, hybrid quantum–classical architectures, and key algorithms such as Grover’s search, Shor’s factorization, the HHL algorithm, and quantum principal component analysis.
55 pages, Kindle Edition
Published December 7, 2025
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