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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Contributors - p. xi 7. About the editors - p. xv 8. Preface - p. xvii 9. Part 1: Algorithms - p. xvii 10. 1: Quantum reinforcement learning - p. 3 11. 2: Exploring quantum federated learning - p. 25 12. 3: Temporal-spatial quantum graph convolutional neural network - p. 51 13. 4: Quantum unsupervised machine learning - p. 69 14. Part 2: Systems - p. 93 15. 5: Distributed learning with quantum-classical collaborative management - p. 97 16. 6: Hybrid quantum-classical reinforcement learning for scheduling systems - p. 117 17. 7: Efficient full-state simulation for quantum AI systems - p. 135 18. 8: Machine learning in bosonic quantum systems - p. 153 19. Part 3: Applications - p. 170 20. 9: Quantum support vector machine for power quality analysis - p. 173 21. 10: Quantum computing for automotive applications - p. 181 22. 11: Quantum-enhanced decision-making in ACT-R - p. 215 23. 12: Quantum federated learning for speech emotion recognition - p. 251 24. Index - p. 273
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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Contributors - p. xi 7. About the editors - p. xv 8. Preface - p. xvii 9. Part 1: Algorithms - p. xvii 10. 1: Quantum reinforcement learning - p. 3 11. 2: Exploring quantum federated learning - p. 25 12. 3: Temporal-spatial quantum graph convolutional neural network - p. 51 13. 4: Quantum unsupervised machine learning - p. 69 14. Part 2: Systems - p. 93 15. 5: Distributed learning with quantum-classical collaborative management - p. 97 16. 6: Hybrid quantum-classical reinforcement learning for scheduling systems - p. 117 17. 7: Efficient full-state simulation for quantum AI systems - p. 135 18. 8: Machine learning in bosonic quantum systems - p. 153 19. Part 3: Applications - p. 170 20. 9: Quantum support vector machine for power quality analysis - p. 173 21. 10: Quantum computing for automotive applications - p. 181 22. 11: Quantum-enhanced decision-making in ACT-R - p. 215 23. 12: Quantum federated learning for speech emotion recognition - p. 251 24. Index - p. 273
