quantum machine learning concepts (8 Ergebnisse)

Taschenbuch. Zustand: Neu. Neuware - 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.

Zustand: New.

Paperback. Zustand: Brand New. 348 pages. 9.18x6.12x9.21 inches. In Stock.

Zustand: New. In.

Buch. Zustand: Neu. Neuware - 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.

Zustand: New.

Buch. Zustand: Neu. Neuware - In the exploration of new frontiers in data-driven solutions, the potential of quantum-enhanced machine learning has become too important to overlook. Quantum machine learning, though still in its formative stages, holds the promise to tackle some of the most complex problems that lie beyond the reach of classical computing. Quantum Machine Learning: Concepts, Algorithms, and Applications is a guide to understanding such quantum principles as superposition and entanglement and how they can enhance learning algorithms and data-processing capabilities. The book features a carefully structured progression from foundational concepts and core algorithms to application-driven case studies and emerging directions for future exploration.The book provides a broad and in-depth treatment of topics ranging from quantum data encoding and quantum neural networks to hybrid models and optimization frameworks. Emphasis has also been placed on real-world use cases and the practical tools available for implementation, thereby ensuring that this book serves not only as a reference but also as a springboard for experimentation and innovation. Highlights include the following: - Implementing quantum neural networks on near-term quantum hardware - Quantum variational optimization for machine learning - Quantum-accelerated neural imputations with large language models - Emerging trends, addressing hardware limitations, algorithm optimization, and ethical considerations This book serves as both a primer and an advanced guide by providing essential knowledge for understanding and implementing quantum-enhanced AI solutions in various professional contexts. It equips readers to become active participants in the quantum revolution transforming machine learning.

Hardcover. Zustand: Brand New. 348 pages. 9.18x6.12x9.45 inches. In Stock.