Preface

Quantum-Enhanced Cloud AI: The Next Frontier in Machine Learning and Deep Learning is a comprehensive book that discusses the traditional computing paradigms, while powerful, are nearing their natural limits in addressing the ever-growing demands of advanced machine learning and deep learning systems. Quantum computing, with its fundamentally different approach to processing information, and cloud computing, with its promise of scalable, accessible infrastructure, are together shaping a new horizon for AI — one that is faster, more powerful, and more transformative than anything we have seen before.

The first chapter addresses the most notable cloud-based platforms for quantum computing and their game-changing impact on the acceleration of AI development and implementation that will define the future of intelligent computing. Chapter two gives a comprehensive overview of quantum algorithms, hybrid quantum-classical models, and real-world implementations. Authors also explore how AI-cloud companies will be affected after major cloud providers enter the world of quantum computing-based business. This book also looks at security challenges, fundamental scalability limitations, and future research directions, with insights into how quantum computing will reshape the AI landscape over the next few decades. Chapter three shows that Quantum AI pertains to the practical feasibility, despite errors, corrections, coherence times, and a lack of algorithm development. This study highlights the importance of continuing to invest in Quantum AI to realize its full potential. Chapter four concludes with a forward-looking scope of AI and ML, considering their potential to transform other industries such as innovation and health, financial services, quantum computing, stock markets, transportation, education, logistics, and sustainable supply chains, impact societal structures, and offer solutions to complex worldwide problems. Chapter five’s main objective is to examine the architectural framework of Cloud Quantum Computing, assess its impact on AI-driven applications, and analyze key challenges and innovations shaping its development. Chapter six evaluates the active Quantum Machine Learning (QML) algorithms while demonstrating their improved functionality compared to usual approaches, and it identifies implementation barriers in real-world scenarios. Chapter seven summarizes the nature of Quantum AI for researchers, experts, and anyone who wishes to venture into the world's next big thing in technology. Chapter eight provides a comparative analysis of human protein function prediction through machine learning techniques and is further enhanced by using the QML approach.

Chapter nine discusses difficulties, further study prospects, and prospects for Quantum AI in the domains of Natural Language Processing and Computer Vision. Chapter ten explores the advancements in quantum computing as well as the development of edge intelligence, which are being set to transform industries by creating high levels of efficiency, security, and automation in real-time operations. Chapter eleven discusses the comparative analysis of unsupervised machine learning techniques for agricultural data analysis. Chapter twelve presents an AI/ML-based framework that uses a wearable smart band that will help people in maintaining social distances, tell whom to meet and whom to avoid having symptoms like body temperature above normal, coughing, and sneezing. Chapter thirteen gives an elaborate overview of the relevance of Quantum AI in healthcare, finance, and cybersecurity, which defines the trend of intelligent and secure computing systems. Chapter fourteen designed a protocol to be scalable and cloud-compatible, paving the way for secure integration with quantum-enhanced cloud AI healthcare systems. Chapter fifteen provides valuable insights into the evolving role of Quantum AI in web accessibility evaluation, offering a foundation for future advancements in automated assessment techniques. Chapter sixteen proposed two architectures from a unified framework that addresses both communication efficiency and security in next-generation Internet of Vehicles systems. Chapter seventeen highlights the revolutionary possibility of combining AI and QC in WSNs. This review focuses on the revolutionary potential of integrating AI and QC in WSNs and proposes future research areas, such as AI-based optimisation models, scalable quantum-secure communication protocols.