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Details for: Hamilton K. Quantum Machine Learning. Concepts and possibilities 2025

hamilton k quantum machine learning concepts possibilities 2025

Type:: E-books
Files:: 1
Size:: 7.3 MB
Uploaded On:: Jan. 6, 2026, 1:14 p.m.
Added By:: andryold1
Seeders:: 6
Leechers:: 4
Info Hash:: 74F61F381C0527F9176236DAB04D437CC6EB1396

Textbook in PDF format 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

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Hamilton K. Quantum Machine Learning. Concepts and possibilities 2025.pdf 7.3 MB

Details for: Hamilton K. Quantum Machine Learning. Concepts and possibilities 2025

hamilton k quantum machine learning concepts possibilities 2025

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