Quantum Computational AI
Algorithms, Systems, and Applications
- 1st Edition - September 29, 2025
- Latest edition
- Editors: Long Cheng, Nishant Saurabh, Ying Mao
- Language: English
Quantum Computational AI: Algorithms, Systems, and Applications is an emerging field that bridges quantum computing and artificial intelligence. With rapid advancements in both a… Read more
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.
- 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
Computer Science researchers, artificial intelligence researchers, and researchers and practitioners working in the field of quantum computing. The primary audience also includes data analysts, software engineers, as well as researchers and professionals across the fields of science and engineering
PART 1 Algorithms
1. Quantum reinforcement learning
1.1. Introduction
1.2. Quantum neural networks
1.3. Quantum reinforcement learning
1.4. Quantum RL applications and challenges
1.5. Conclusion and outlook
2. Exploring quantum federated learning
2.1. Introduction
2.2. Quantum approaches
2.3. Hybrid approaches
2.4. Applications
2.5. Challenges
2.6. Conclusion
3. Temporal-spatial quantum graph convolutional neural
network
3.1. Introduction
3.2. Related work
3.3. Methodology
3.4. Experimental results
3.5. Summary of the TS-QGCNN model
4. Quantum unsupervised machine learning
4.1. Introduction
4.2. Proposed quantum k-means clustering (Q-KMC)
methodology flow
4.3. Classical k-means clustering (C-KMC) method
4.4. Quantum k-means clustering (Q-KMC) method
4.5. Inference
PART 2 Systems
5. Distributed learning with quantum-classical collaborative
management
5.1. Introduction
5.2. Related work
5.3. System design
5.4. DQuLearn evaluation
5.5. Discussion and conclusion
6. Hybrid quantum-classical reinforcement learning for
scheduling systems
6.1. Introduction
6.2. Related works
6.3. Problem statement
6.4. The proposed hybrid quantum-classic RL approach
6.5. Experimental evaluation
6.6. Conclusion
7. Efficient full-state simulation for quantum AI systems
7.1. Introduction
7.2. Background and motivation
7.3. Approach
7.4. Evaluation
7.5. Conclusion
8. Machine learning in bosonic quantum systems
8.1. Introduction
8.2. Related work
8.3. Background
8.4. Optimizer performance for qumode QVSP
8.5. Conclusion
PART 3 Applications
9. Quantum support vector machine for power quality analysis
9.1. Introduction
9.2. Problem statement
9.3. Detection and identification of PQDs using QSVM
9.4. Experimental results
9.5. Conclusion
10. Quantum computing for automotive applications
10.1. Introduction
10.2. Automotive applications areas
10.3. Optimization
10.4. Simulation
10.5. Materials science and quantum chemistry
10.6. Machine learning
10.7. Benchmarking
10.8. Conclusion and future directions
11. Quantum-enhanced decision-making in ACT-R
11.1. Introduction
11.2. Cognitive neuroscience
11.3. Quantum cognitive processes
11.4. Methodology
11.5. Discussion
11.6. Conclusion
12. Quantum federated learning for speech emotion
recognition
12.1. Introduction
12.2. Related work
12.3. Methodology
12.4. Experiments and analysis
12.5. Conclusion
1. Quantum reinforcement learning
1.1. Introduction
1.2. Quantum neural networks
1.3. Quantum reinforcement learning
1.4. Quantum RL applications and challenges
1.5. Conclusion and outlook
2. Exploring quantum federated learning
2.1. Introduction
2.2. Quantum approaches
2.3. Hybrid approaches
2.4. Applications
2.5. Challenges
2.6. Conclusion
3. Temporal-spatial quantum graph convolutional neural
network
3.1. Introduction
3.2. Related work
3.3. Methodology
3.4. Experimental results
3.5. Summary of the TS-QGCNN model
4. Quantum unsupervised machine learning
4.1. Introduction
4.2. Proposed quantum k-means clustering (Q-KMC)
methodology flow
4.3. Classical k-means clustering (C-KMC) method
4.4. Quantum k-means clustering (Q-KMC) method
4.5. Inference
PART 2 Systems
5. Distributed learning with quantum-classical collaborative
management
5.1. Introduction
5.2. Related work
5.3. System design
5.4. DQuLearn evaluation
5.5. Discussion and conclusion
6. Hybrid quantum-classical reinforcement learning for
scheduling systems
6.1. Introduction
6.2. Related works
6.3. Problem statement
6.4. The proposed hybrid quantum-classic RL approach
6.5. Experimental evaluation
6.6. Conclusion
7. Efficient full-state simulation for quantum AI systems
7.1. Introduction
7.2. Background and motivation
7.3. Approach
7.4. Evaluation
7.5. Conclusion
8. Machine learning in bosonic quantum systems
8.1. Introduction
8.2. Related work
8.3. Background
8.4. Optimizer performance for qumode QVSP
8.5. Conclusion
PART 3 Applications
9. Quantum support vector machine for power quality analysis
9.1. Introduction
9.2. Problem statement
9.3. Detection and identification of PQDs using QSVM
9.4. Experimental results
9.5. Conclusion
10. Quantum computing for automotive applications
10.1. Introduction
10.2. Automotive applications areas
10.3. Optimization
10.4. Simulation
10.5. Materials science and quantum chemistry
10.6. Machine learning
10.7. Benchmarking
10.8. Conclusion and future directions
11. Quantum-enhanced decision-making in ACT-R
11.1. Introduction
11.2. Cognitive neuroscience
11.3. Quantum cognitive processes
11.4. Methodology
11.5. Discussion
11.6. Conclusion
12. Quantum federated learning for speech emotion
recognition
12.1. Introduction
12.2. Related work
12.3. Methodology
12.4. Experiments and analysis
12.5. Conclusion
- Edition: 1
- Latest edition
- Published: September 29, 2025
- Language: English
LC
Long Cheng
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.
Affiliations and expertise
Full Professor in the School of Control and Computer Engineering at North China Electric Power University in Beijing.NS
Nishant Saurabh
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.
Affiliations and expertise
Assistant Professor in the Department of Information and Computing Sciences at Utrecht University in the Netherlands.YM
Ying Mao
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.
Affiliations and expertise
Associate Professor in the Department of Computer and Information Sciences at Fordham University in New York, USA.Read Quantum Computational AI on ScienceDirect