Quantum Machine Learning: A Guide To Quantum AI by Mrs.Hyma Birudaraju,Dr Koneti Krishnaiah,Mrs.Mary Teresa ,Mr.M.Kumar

The convergence of quantum computing and machine learning represents one of the most groundbreaking evolutions in modern science and technology. This book, Quantum Machine Learning: A Guide to Quantum AI, emerges as a collaborative effort by experts and educators who share a common vision—to bridge the gap between quantum theory and artificial intelligence through a comprehensive and accessible learning resource.
In an era where computational complexity, data-driven intelligence, and quantum mechanics increasingly intersect, the motivation behind this work is to empower learners and researchers to understand the principles, architectures, and algorithms that define Quantum Artificial Intelligence (QAI). By integrating the mathematical foundations of quantum physics with the algorithmic depth of machine learning, we aim to offer a unified perspective that reveals how qubits, superposition, and entanglement can enrich traditional learning paradigms.
Each chapter in this book is a testament to the synergy of expertise across quantum mechanics, artificial intelligence, data science, and computational engineering. The work introduces readers to quantum machine learning (QML) models, such as quantum classifiers, quantum neural networks, quantum generative models, and hybrid architectures that merge classical and quantum learning systems. It also provides practical exposure through conceptual simulations and programming exercises using popular platforms like Qiskit and PennyLane—making the complex world of QAI approachable for scholars, researchers, and industry practitioners alike.
This volume is more than a textbook—it is a guide for innovation, written to inspire both early-career students venturing into the quantum landscape and seasoned professionals exploring new dimensions of AI computation. Our joint ambition is to ensure that readers not only gain theoretical insights but also cultivate the ability to design and implement real-world quantum-enhanced algorithms that define the future of intelligent systems.
We, the contributing authors, extend our gratitude to the academic institutions, research collaborators, and technical communities whose commitment to open innovation and interdisciplinary research made this project possible. Together, we acknowledge the ever-growing global community of researchers shaping the frontier of Quantum Machine Learning—their groundbreaking work continues to motivate and refine our collective understanding of this nascent yet rapidly expanding field.
May this book serve as both a foundation and a catalyst—laying down the cornerstone for the exploration of Quantum AI systems that redefine the boundaries of computational intelligence.