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.

• PART 1 Algorithms1. Quantum reinforcement learning 2. Exploring quantum federated learning 3. Temporal-spatial quantum graph convolutional neuralnetwork 4. Quantum unsupervised machine learning PART 2 Systems5. Distributed learning with quantum-classical collaborativemanagement 6. Hybrid quantum-classical reinforcement learning forscheduling systems 7. Efficient full-state simulation for quantum AI systems 8. Machine learning in bosonic quantum systems PART 3 Applications9. Quantum support vector machine for power quality analysis10. Quantum computing for automotive applications 11. Quantum-enhanced decision-making in ACT-R 12. Quantum federated learning for speech emotionrecognition

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.

• PART 1 Algorithms1. Quantum reinforcement learning 2. Exploring quantum federated learning 3. Temporal-spatial quantum graph convolutional neuralnetwork 4. Quantum unsupervised machine learning PART 2 Systems5. Distributed learning with quantum-classical collaborativemanagement 6. Hybrid quantum-classical reinforcement learning forscheduling systems 7. Efficient full-state simulation for quantum AI systems 8. Machine learning in bosonic quantum systems PART 3 Applications9. Quantum support vector machine for power quality analysis10. Quantum computing for automotive applications 11. Quantum-enhanced decision-making in ACT-R 12. Quantum federated learning for speech emotionrecognition

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.

• PART 1 Algorithms1. Quantum reinforcement learning 2. Exploring quantum federated learning 3. Temporal-spatial quantum graph convolutional neuralnetwork 4. Quantum unsupervised machine learning PART 2 Systems5. Distributed learning with quantum-classical collaborativemanagement 6. Hybrid quantum-classical reinforcement learning forscheduling systems 7. Efficient full-state simulation for quantum AI systems 8. Machine learning in bosonic quantum systems PART 3 Applications9. Quantum support vector machine for power quality analysis10. Quantum computing for automotive applications 11. Quantum-enhanced decision-making in ACT-R 12. Quantum federated learning for speech emotionrecognition