Quantum Computational AI: Algorithms, Systems, and Applications

Long Cheng · Nishant Saurabh · Ying Mao

Oct 2025 · Morgan Kaufmann

Ebook

250

Pages

Eligible

This book will become available on October 1, 2025. You will not be charged until it is released.

About this ebook

Quantum Computational AI: Algorithms, Systems, and Applications represents an emerging, fast-evolving field. The rapid advancements in both quantum computing and AI necessitate a new resource that encapsulates the latest theories, algorithms, and practical applications at the intersection of these domains. Quantum Computational AI: Algorithms, Systems and Applications dives into the intersection of quantum computing and artificial intelligence, showcasing how they can come together to form powerful new computational frameworks. Through the lens of expert contributors, this book navigates through quantum algorithms, explores the design of quantum systems, and demonstrates real-world applications across various sectors. Designed to be both informative and accessible, this book is perfect for academics, researchers, industry professionals, and students keen to be on the forefront of quantum and AI technologies. With a blend of theory and practical examples, the book provides a solid understanding of how quantum principles can be leveraged to advance AI, opening doors to unprecedented possibilities. This book provides a consolidated, comprehensive, and accessible resource that elucidates the synergy between quantum algorithms and AI systems, showcasing how they can be harmoniously integrated to unlock new computational paradigms and solve complex real-world problems. - Consolidates key concepts of quantum computing and AI into one accessible resource, bridging the existing knowledge gap - Provides the latest insights and developments in Quantum Computational AI, offering readers up-to-date information - Offers practical guidance on applying quantum principles in AI across various real-world sectors, bridging theory and practice - Aids in skill development for designing, analyzing, and implementing quantum algorithms and systems in AI applications - Stimulates innovative thinking by providing a thorough understanding of the interdisciplinary field of Quantum Computational AI

About the author

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.

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