Call for Book Chapters

About the Book

This edited volume, Quantum Machine Learning in Practice: Frameworks, Applications, and Future Perspectives, aims to bridge the gap between theoretical advancements in quantum computing and their practical deployment in machine learning. With rapid progress in quantum technologies, there is a growing need to explore scalable frameworks, hybrid quantum-classical models, and domain-specific applications that leverage quantum advantage. This book brings together contributions from leading researchers and practitioners to present a comprehensive overview of emerging algorithms, tools, and real-world use cases across diverse sectors such as healthcare, finance, smart cities, cybersecurity, and scientific computing. It also addresses critical challenges, including data encoding, model evaluation, and hardware limitations, while highlighting ethical and societal implications. By integrating foundational concepts with applied research, this volume serves as a valuable resource for academics, industry professionals, and policymakers seeking to understand and advance the evolving landscape of Quantum Machine Learning.

Editors

Dr. Arun Pandian J

Assistant Professor, VIT, Vellore, India

Dr. Rajganesh Nagarajan

Professor, SVCE, Sriperumbudur, India

Dr. Ramkumar Thirunavukarasu

Professor, VIT, Vellore, India

Tentative Topics

• Demystifying Quantum Machine Learning
• Quantum Algorithms for Machine Learning
• Hybrid Quantum-Classical Frameworks
• Benchmarking QML Models
• Quantum Data Encoding Techniques
• Quantum Kernels
• Variational Quantum Classifiers
• Quantum Neural Networks
• Quantum Reinforcement Learning
• QML in Healthcare and Bioinformatics
• QML in Drug Discovery and Molecular Simulation
• QML in Financial Forecasting
• QML in Smart Cities and IoT
• QML in Cybersecurity and Cryptographic Applications
• QML in Robotics and Autonomous Systems
• Open-Source Frameworks for QML
• Ethical and Policy Implications of QML
• Future Research Directions in QML