Quantum Machine Learning

From Theory to Near-Term Application

By: Ajit Singh

Narrated by: Virtual Voice

"Quantum Machine Learning: From Theory to Near-Term Application" is a comprehensive, hands-on textbook designed to guide students and professionals from the fundamentals of classical machine learning and quantum computing to the practical implementation of cutting-edge hybrid quantum-classical algorithms. This book is meticulously structured to serve as both a university-level textbook and a professional's guide to this transformative field.


Philosophy

The core philosophy of this book is to bridge the gap between abstract theory and tangible application. We operate on the principle that the best way to understand a complex concept is to build it. While providing the necessary theoretical rigor, the book prioritizes intuitive explanations, visual aids, and practical coding examples over dense mathematical formalism. The focus is squarely on the capabilities and limitations of today's Noisy Intermediate-Scale Quantum (NISQ) hardware, ensuring that the skills you acquire are relevant and applicable now, not in a distant future.


Key Features

1. Beginner to Advanced Trajectory: The book starts with foundational concepts, assuming only a basic knowledge of Python and linear algebra, and progressively builds to advanced topics like Quantum Generative Adversarial Networks and Quantum Kernel Methods.
2. Focus on Near-Term (NISQ) Reality: All algorithms and examples are presented with a practical awareness of the constraints of current quantum hardware, including dedicated sections on noise, error mitigation, and performance on real devices.
3. Real-World Case Studies: Explores the application of QML in diverse domains such as finance (portfolio optimization), chemistry (molecular simulation), and machine learning (enhanced classification).
4. Two Major Frameworks: Provides in-depth tutorials and examples for both IBM's Qiskit and Xanadu's PennyLane, giving readers versatile and highly sought-after skills.
5. Complete Capstone Project: A full chapter is dedicated to a step-by-step, fully-coded DIY project on Quantum Transfer Learning for Image Classification, including data preparation, model design, implementation, and analysis of results.



To Whom This Book Is For

This book is primarily intended for:

1. B.Tech/M.Tech Students: In Computer Science, Information Technology, and related fields, as a primary textbook for a one-semester course on Quantum Computing or Quantum Machine Learning. It aligns with AICTE and international university syllabi.
2. Machine Learning Researchers and Data Scientists: Who want to explore the potential of quantum computing to enhance their existing models and tackle new classes of problems.
3. Software Engineers and Developers: Looking to upskill and enter the high-growth field of quantum software development.
4. Physicists and Quantum Computing Engineers: Who wish to understand the practical applications of quantum hardware in the domain of machine learning.


Ultimately, this book is for anyone with a curious mind and a passion for technology who wants to be at the forefront of the next computing revolution.