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Quantum Computational AI: Algorithms, Systems, and Applications

Editat de Long Cheng, Nishant Saurabh, Ying Mao
en Limba Engleză Paperback – 29 sep 2025
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
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Specificații

ISBN-13: 9780443302596
ISBN-10: 0443302596
Pagini: 304
Dimensiuni: 191 x 235 x 18 mm
Greutate: 0.62 kg
Editura: ELSEVIER SCIENCE

Cuprins

PART 1 Algorithms
1. Quantum reinforcement learning
2. Exploring quantum federated learning
3. Temporal-spatial quantum graph convolutional neural
network
4. Quantum unsupervised machine learning

PART 2 Systems
5. Distributed learning with quantum-classical collaborative
management
6. Hybrid quantum-classical reinforcement learning for
scheduling systems
7. Efficient full-state simulation for quantum AI systems
8. Machine learning in bosonic quantum systems

PART 3 Applications
9. Quantum support vector machine for power quality analysis
10. Quantum computing for automotive applications
11. Quantum-enhanced decision-making in ACT-R
12. Quantum federated learning for speech emotion
recognition