Quantum Machine Learning (QML): Platform, Tools and Applications

Inbunden, Engelska, 2026

2 529 kr

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Quantum Machine Learning (QML): Platform, Tools and Applications, Volume 140 in the Advances in Computers series, explores the intersection of quantum computing and artificial intelligence, highlighting advances that promise to revolutionize computational science. The book introduces foundational concepts in quantum computing and circuits, building toward the practical implementation of quantum machine learning (QML) algorithms. Chapters address challenges such as the gradient vanishing problem in variational quantum circuits and explore powerful optimization methods enabled by quantum mechanics. The volume also covers advanced applications, including quantum approaches to smart grid management, quantum Monte Carlo simulations, and predictive modeling in numerical solvers using quantum neural networks.

Real-world relevance is underscored through discussions of transformative quantum algorithms and their potential to reshape machine learning, enabling unprecedented performance in data analysis, optimization, and beyond.

Contains novel subject matter that is relevant to computer science
Includes the expertise of contributing authors
Presents an easy to comprehend writing style

Produktinformation

Utgivningsdatum2026-02-10
Mått152 x 229 x undefined mm
Vikt450 g
FormatInbunden
SpråkEngelska
SerieAdvances in Computers
Antal sidor314
FörlagElsevier Science
ISBN9780443223822

Tillhör följande kategorier

Matematik inom Naturvetenskap och teknik
Programmeringsböcker inom Data och IT

Shiho Kim is a professor in the school of integrated technology at Yonsei University, Seoul, Korea. His previous assignment includes, System on chip design engineer, at LG Semicon Ltd. (currently SK Hynix), Korea, Seoul [1995-1996], Director of RAVERS (Research center for Advanced Hybrid Electric Vehicle Energy Recovery System, a government-supported IT research center. Associate Director of the ICT consilience program, which is a Korea National program for cultivating talented engineers in the field of information and communication Technology, Korea [2011-2012], Director of Seamless Transportation Lab, at Yonsei university, Korea [since 2011-]. His main research interest includes Development of software and hardware technologies for intelligent vehicles, Blockchain technology for intelligent transportation systems, and reinforcement learning for autonomous vehicles. He is the member of the editorial board and reviewer for various Journals and International conferences. So far he has organized 2 International Conference as Technical Chair/General Chair. He is a member of IEIE (Institute of Electronics and Information Engineers of Korea), KSAE (Korean Society of Automotive Engineers), vice president of KINGC (Korean Institute of Next Generation Computing), and a senior member of IEEE. He is the co-author for over 100 papers and holding more than 50 patents in the area of information and communication technology. Ganesh Chandra Deka is currently Deputy Director (Training) at Directorate General of Training, Ministry of Skill Development and Entrepreneurship, Government of India, New Delhi-110001, India. His research interests include e-Governance, Big Data Analytics, NoSQL Databases and Vocational Education and Training.He has 2 books on Cloud Computing published by LAP Lambert, Germany. He is the Co-author for 4 text books on Fundamentals of Computer Science (3 books published by Moni Manik Prakashan, Guwahati, Assam, India and 1 IGI Global, USA). As of now he has edited 14 books (6 IGI Global, USA, 5 CRC Press, USA, 2 Elsevier & 1 Springer) on Big data, NoSQL and Cloud Computing and authored 10 Book Chapters.He has published around 47 research papers in various IEEE conferences. He has organized 08 IEEE International Conferences as Technical Chair in India. He is the Member of the editorial board and reviewer for various Journals and International conferences. Member of IEEE, the Institution of Electronics and Telecommunication Engineers, India and Associate Member, the Institution of Engineers, India

1. Introduction to Quantum Machine Learning (QML)2. Quantum Computing Basics3. Basic circuits for Quantum Computations4. Quantum Machine Learning5. Addressing the Gradient Vanishing Problem in Parametrized Quantum Circuit Training and Optimization6. Quantum Optimization techniques and applications7. Next-Gen Smart Grids: A Quantum Approach8. Quantum Monte Carlo simulations9. QPDE - Quantum Neural Network Based Stabilization Parameter Prediction for Numerical Solvers for Partial Differential Equations10. Transforming Machine Learning: An In-Depth Exploration of Quantum Algorithms and Their Applications