Quantum Machine Learning

Quantum computing and machine learning represent two of the most transformative technological advancements of the 21st century. Quantum computing leverages principles such as superposition and entanglement to process information beyond classical limits, while machine learning enables systems to learn patterns and make intelligent decisions from data. The convergence of these fields—Quantum Machine Learning (QML)—promises exponential computational advantages, enhanced optimization capabilities, and breakthroughs across finance, healthcare, cybersecurity, telecommunications, and digital transformation.

This book offers a structured and in-depth exploration of Quantum Machine Learning, 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.

This 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.