Quantum-Enhanced Cloud AI:The Next Frontier in Machine Learning and Deep Learning. E-book. Formato EPUB - 9798898813215

Quantum-Enhanced Cloud AI:The Next Frontier in Machine Learning and Deep Learning. E-book. Formato EPUB. Quantum-Enhanced Cloud AI: The Next Frontier in Machine Learning and Deep Learning presents a scholarly and comprehensive examination of the convergence of quantum computing, cloud infrastructure, and advanced artificial intelligence. As classical computing approaches approach their practical and physical limits, this volume positions quantum-enhanced cloud platforms as a transformative foundation for next-generation machine learning and deep learning systems.

The book systematically introduces core principles of quantum computing, cloud-based quantum architectures, and hybrid quantum–classical models, establishing a strong conceptual foundation before advancing to practical implementations. It critically examines quantum algorithms, error correction challenges, coherence limitations, and architectural considerations that shape the feasibility of Quantum AI. Through detailed case studies and comparative analyses, the book demonstrates how quantum machine learning techniques can outperform classical approaches in domains such as healthcare, bioinformatics, finance, cybersecurity, agriculture, smart cities, and Internet of Vehicles systems. Emerging topics including edge intelligence, quantum-enabled IoT, sustainable supply chains, and secure cloud-based healthcare frameworks are also explored.

Written in a rigorous yet accessible style, this reference work serves graduate students, researchers, and professionals seeking to understand both the theoretical underpinnings and real-world implications of quantum-enhanced AI. It offers valuable insights into the technological, industrial, and societal impact of this rapidly evolving field.



Key Features

· Comprehensive coverage of quantum computing principles and cloud-based AI architectures

· In-depth analysis of hybrid quantum–classical machine learning models and algorithms

· Comparative evaluation of quantum and classical AI approaches across multiple application domains

· Practical insights into implementation challenges, scalability, and security

· Forward-looking perspectives on the future of Quantum AI and intelligent computing systems.