Quantum Computational AI

Algorithms, Systems, and Applications
Erscheinungsjahr 2025
ISBN: 978-0-443-30259-6
Verlag: Elsevier Science

Buch, Englisch, 250 Seiten, Format (B × H): 191 mm x 235 mm

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, which 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.

Autoren/Hrsg.

Cheng, Long

Saurabh, Nishant

Mao, Ying

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsarchitektur
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Informationstheorie, Kodierungstheorie

Inhaltsverzeichnis

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

Über Autor(innen)

Cheng, Long
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.

Saurabh, Nishant
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.

Mao, Ying
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.