Quantum Machine Learning

This work offers a structured and in-depth exploration of Quantum Machine Learning (QML), beginning with foundational quantum principles and progressing to advanced QML algorithms such as Quantum SVMs, quantum kernels, and quantum neural networks. It bridges theory with real-world implementation through domain-focused chapters covering finance, healthcare, taxation systems, mobile networks, supply chains, cybersecurity, augmented reality dashboards, and e-commerce. By integrating conceptual clarity with applied frameworks, the book presents practical pathways for leveraging quantum-enhanced intelligence across industries. The book is intended for researchers, academicians, postgraduate students, industry professionals, data scientists, technology strategists, and policymakers seeking to understand and apply Quantum Machine Learning in advanced research, enterprise systems, and next-generation digital infrastructures. Key Features: Comprises Comprehensive Coverage: Balances foundational theory with practical applications. Comprises Cutting-Edge Content: Features the latest research and emerging trends in QML. Provides Practical Insights: Includes real-world case studies and examples for applying QML techniques. Comprises Expert Authorship: Written by a leading expert with strong academic and industry experience.