Quantum Machine Learning (Paperback)

Syed Nisar Hussain Bukhari

ISBN 10: 1041144652 / ISBN 13: 9781041144656

Editore: Taylor & Francis Ltd, London, 2026

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Paperback. 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 the following:Implementing quantum neural networks on near-term quantum hardwareQuantum variational optimization for machine learningQuantum-accelerated neural imputations with large language modelsEmerging trends, addressing hardware limitations, algorithm optimization, and ethical considerationsThis book serves as both a primer and an 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Codice articolo 9781041144656

Dati bibliografici

• Titolo: Quantum Machine Learning (Paperback)
• Editore: Taylor & Francis Ltd, London
• Data di pubblicazione: 2026
• Lingua: Inglese
• ISBN 10: 1041144652
• ISBN 13: 9781041144656
• Rilegatura: Paperback
• Condizione: new

Descrizione articolo

Riassunto

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 the following:

• 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 a primer and an 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.

Informazioni sull?autore

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 bachelor’s and master’s 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.