Overview

Quantum Computational AI: Algorithms, Systems, and Applications is an emerging field that bridges quantum computing and artificial intelligence. With rapid advancements in both areas, this book serves as a vital resource, capturing the latest theories, algorithms, and practical applications at their intersection. It aims to be both informative and accessible, making it perfect for academics, researchers, industry professionals, and students eager to lead in these technologies. The book explores quantum algorithms, system design, and demonstrates real-world applications across various sectors. It provides a comprehensive understanding of how quantum principles can advance AI, revealing unprecedented possibilities and benefits.

Full Product Details

Author:   Long Cheng (Full Professor in the School of Control and Computer Engineering at North China Electric Power University in Beijing.) ,  Nishant Saurabh, Ph.D. (Assistant Professor in the Department of Information and Computing Sciences at Utrecht University in the Netherlands.) ,  Ying Mao (Associate Professor in the Department of Computer and Information Sciences at Fordham University in New York, USA.)
Publisher:   Elsevier Science & Technology
Imprint:   Morgan Kaufmann Publishers In
Weight:   0.450kg
ISBN:  

9780443302596

ISBN 10:   0443302596
Pages:   250
Publication Date:   29 August 2025
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Forthcoming
Availability:   Not yet available  
This item is yet to be released. You can pre-order this item and we will dispatch it to you upon its release.

Table of Contents

1. Introduction to Quantum Computational AI: Overview of quantum computing and artificial intelligence, setting the stage for their intersection 2. Fundamental Quantum Algorithms: Exploration of basic quantum algorithms crucial for quantum-enhanced AI applications 3. Quantum Machine Learning Algorithms: Delve into quantum machine learning algorithms and their superiority over classical machine learning algorithms 4. Quantum Neural Networks (QNNs): Exploration of Quantum Neural Networks, their structure, and advantages over classical neural networks 5. Architecture of Quantum Systems: Discussion on the architectural design of quantum systems and their relevance in AI applications 6. Quantum Programming Languages: Overview of quantum programming languages and their role in developing quantum AI applications 7. Quantum Hardware for AI: Examination of quantum hardware technologies and their impact on the performance of AI applications 8. Error Correction in Quantum Computing: Discussing the challenges and solutions associated with error correction in quantum computing for reliable AI applications 9. Scalability of Quantum Systems: Investigating the scalability challenges and solutions in integrating quantum systems with AI applications 10. Quantum Cryptography and Security: Exploration of the role of quantum cryptography in securing AI applications and data 11. Real-world Applications of Quantum Computational AI: Case studies showcasing the application of Quantum Computational AI across various sectors like finance, healthcare, and cybersecurity 12. Challenges and Future Directions: Discussion on the challenges faced in Quantum Computational AI and prospective future developments

Reviews

Author Information

Long Cheng is a Full Professor in the School of Control and Computer Engineering at North China Electric Power University in Beijing. He was an Assistant Professor at Dublin City University, and a Marie Curie Fellow at University College Dublin. He also has worked at organizations such as Huawei Technologies Germany, IBM Research Dublin, TU Dresden and TU Eindhoven. He has published more than 80 papers in journals and conferences like TPDS, TON, TC, TSC, TASE, TCAD, TCC, TBD, TITS, TVLSI, TVT, TSMC, JPDC, IEEE Network, IEEE Systems Journal, HPCA, CIKM, ICPP and Euro-Par, etc. His research focuses on distributed systems, deep learning, cloud computing and process mining. Prof Cheng is a Senior Member of the IEEE and a Co-Chair of Journal of Cloud Computing. Nishant Saurabh is a tenured Assistant Professor in the Department of Information and Computing Sciences at Utrecht University in the Netherlands. He obtained his Ph.D. in Computer Science from the University of Innsbruck in 2021 and later worked as a postdoctoral researcher at Klagenfurt University, Austria. His research interest includes hybrid distributed systems, cloud and edge computing, performance modelling, optimization, and observability. He has published over 25 publications in journal and conferences like TPDS, JPDC, IPDPS, CCGrid, QSW, IST, ICFEC, and Euro-Par etc. He is an associate editor for Springer’s JoCCASA journal, editorial board and steering committee member for Springer’s book series and conference on frontiers of AI. He also served as scientific coordinator and WP leader in several EU and Austrian projects and is currently a member of IBM’s working committee on HPC-Quantum integration. Ying Mao is a tenured Associate Professor in the Department of Computer and Information Science at Fordham University in New York City. In addition, he serves as the Associate Chair for Undergraduate Studies. He obtained his Ph.D. in Computer Science from the University of Massachusetts Boston in 2016 and is currently a Fordham-IBM research fellow. His research interests include advanced computing systems, service virtualization, systems deep learning, edge intelligence, and cloud-edge-CPS applications. He has published over 40 research articles in leading international conferences and journals, such as TPDS, TCC, TC, IEEE Systems Journal, MLSys, ICNP and ICPP. His research projects have been funded by various agencies, such as NSF, Google Research, IBM, IonQ and Microsoft Research.

Tab Content 6

Author Website:  

Countries Available

All regions