Quantum Machine Learning

Where Quantum Computing Meets Machine Learning

Quantum machine learning is one of the most intriguing frontiers in computational intelligence, and this volume from the De Gruyter Frontiers in Computational Intelligence series offers a focused exploration of its foundations and early applications. Edited by Siddhartha Bhattacharyya, Indrajit Pan, Ashish Mani, Sourav De, Elizabeth Behrman, and Susanta Chakraborti, the book collects contributions that examine how quantum principles can reshape the way we approach learning, optimization, and clustering.

Inside the Volume

Across six chapters, the book moves from introductory concepts to specialized techniques. The opening chapter sets the stage with an introduction to quantum machine learning. Later chapters investigate topographic representation, quantum optimization for machine learning, and the transition from classical to quantum machine learning. A comparative study of quantum-inspired automatic clustering algorithms—using genetic and bat algorithms—illustrates how quantum ideas can inspire practical algorithm design. The volume closes with a conclusion that draws together the threads of the discussion.

• Foundations: An introduction to quantum machine learning and its conceptual landscape.
• Representation and Optimization: Topographic representation and quantum optimization strategies for learning tasks.
• Classical-to-Quantum Transition: How machine learning paradigms shift when quantum resources are considered.
• Quantum-Inspired Clustering: A comparative study of genetic and bat algorithms for automatic clustering.

Who Will Find This Useful

The material is aimed at researchers, graduate students, and professionals working at the intersection of quantum computing, machine learning, and computational intelligence. Readers with a background in computer science, mathematics, or physics will find the chapters accessible, while those already engaged in quantum algorithm research will appreciate the focused treatment of optimization and clustering. The chapters are written for a technically minded audience, with an emphasis on computational concepts.

Why This Volume Matters

As quantum hardware continues to develop, the question of how to best use it for learning tasks becomes increasingly relevant. This collection captures a moment of exploration, offering both theoretical perspectives and algorithmic examples. It is a useful reference for those who want to understand how quantum and classical approaches might complement each other in solving complex problems.