In the exploration of new frontiers in data-driven solutions, the potential of quantum-enhanced machine learning has become too important to overlook. Quantum machine learning, though still in its formative stages, holds the promise to tackle some of the most complex problems that lie beyond the reach of classical computing. Quantum Machine Learning: Concepts, Algorithms, and Applications is a guide to understanding such quantum principles as superposition and entanglement and how they can enhance learning algorithms and data processing capabilities. The book features a carefully structured progression from foundational concepts and core algorithms to application-driven case studies and emerging directions for future exploration.
The book provides a broad and in-depth treatment of topics ranging from quantum data encoding and quantum neural networks to hybrid models and optimization frameworks. Emphasis has also been placed on real-world use cases and the practical tools available for implementation, thereby ensuring that this book serves not only as a reference but also as a springboard for experimentation and innovation. Highlights include:
- Implementing quantum neural networks on near-term quantum hardware
- Quantum variational optimization for machine learning
- Quantum-accelerated neural imputations with large language models
- Emerging trends, addressing hardware limitations, algorithm optimization, and ethical considerations.
This book serves as both primer and advanced guide by providing essential knowledge for understanding and implementing quantum-enhanced AI solutions in various professional contexts. It equips readers to become active participants in the quantum revolution transforming machine learning.
The book explores quantum computing's transformative impact on artificial intelligence and machine learning. Beyond theoretical knowledge, the book emphasizes practical implementation and offers code samples and real-world case studies.
1. Introduction to Quantum Computing
2. Principles, Algorithms, and
Technologies behind Quantum Computing
3. An Overview of Machine Learning:
Concepts, Algorithms, and Practices
4. Quantum Information Theory
5. Quantum
Machine Learning from Theory to Data-Driven Implementations
6. A Mathematical
Perspective on Quantum Information Theory
7. Quantum Neural Networks
8.
Implementing Quantum Neural Networks on Near-Term Quantum Hardware
9. A
Comparative Analysis of Classical and Quantum Approaches for Heart Attack
Prediction
10. Quantum Optimization for Machine Learning
11. Quantum
Variational Optimization for Machine Learning
12. Latest Developments in
Quantum Optimization for Machine Learning
13. Quantum Generative Adversarial
Networks
14. Heart Disease Prediction Analysis using Quantum-Enhanced
Features with Classical and Quantum Machine Learning Models
15.
Quantum-Accelerated Neural Imputation with Large Language Models (LLMs)
16.
Quantum Key Distribution Beyond 5G and 6G: Hybrid Integrations, Testbeds, and
Future Directions
Dr. Syed Nisar Hussain Bukhari is an accomplished academician and researcher, currently serving as Scientist D at the National Institute of Electronics and Information Technology (NIELIT), Srinagar, an institute under the Ministry of Electronics and Information Technology, Government of India. He brings over 12 years of experience in teaching, research, and institutional leadership, with a specialized focus on artificial intelligence, machine learning, deep learning, and their interdisciplinary applications.
Dr. Bukhari completed his bachelors and masters degrees in computer applications from the University of Kashmir and earned his doctorate in machine learning from the University Institute of Computing, Chandigarh University, in 2022. His research contributions have been published in several high-impact journals, including IEEE Transactions, Nature, Springer, and MDPI. In addition to being published in academic journals, his works have been widely cited in international conferences and book chapters.
He has received multiple best paper awards at various international forums and holds patents for his research innovations. His editorial engagements include authoring and editing books published by CRC Press, Taylor & Francis Group. He is a reviewer for leading journals, such as Scientific Reports (Nature), Computers in Biology and Medicine (Elsevier), and Briefings in Bioinformatics (Oxford University Press), and regularly serves as a session chair in Scopus-indexed international conferences. He serves as an academic editor for PLOS One, a prestigious high-impact-factor journal, where he contributes to the peer review and curation of high-quality interdisciplinary research. Additionally, he served as a guest editor for two thematic collections of the Journal of Visualized Experiments titled Recent Advancements in Computational Biology and Bioinformatics and Next-Gen Computational Techniques in Medical Imaging and Signal Processing, highlighting his engagement with emerging research trends and editorial leadership in the field.
As a faculty member, Dr. Bukhari has taught a wide range of technical subjects, including machine learning, Python, web technologies, and data structures to postgraduate students. He has also led training initiatives in artificial intelligence and emerging technologies for engineering students, professionals, and government stakeholders. Dr. Bukhari headed the Department of Information Technology at NIELIT Srinagar, where he played a key role in strengthening academic undergraduate and postgraduate programs and in providing strategic consultancy to government departments in Jammu and Kashmir on various e-governance initiatives. He is currently serving as Academic Head at NIELIT, Srinagar, and Head, Department of Computer Science and Applications at NIELIT (Deemed-to-Be University), Srinagar Campus.
Known for his collaborative spirit, Dr. Bukhari maintains active research partnerships with institutions across India and abroad. He is a member of the Institution of Electronics and Telecommunication Engineers (IETE) and the International Association of Engineers (IAENG). His professional journey reflects a sustained commitment to research excellence, academic mentorship, and the development of impactful, technology-driven solutions.
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