Quantum Computing: Applications and Challenges

This book is intended for academics, researchers, Ph.D. students, and industrials to learn about quantum computing and prepare them to understand the future with its cutting-edge tools. Quantum computing holds great promise in terms of human life quality especially when combined with artificial inte...

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Collectivité auteur: SpringerLink (Online service)
Autres auteurs: Drias, Habiba (Éditeur intellectuel), Yalaoui, Farouk (Éditeur intellectuel)
Format: Électronique eBook
Langue:anglais
Publié: Cham : Springer Nature Switzerland : Imprint: Springer, 2024.
Édition:1st ed. 2024.
Collection:Information Systems Engineering and Management, 2
Sujets:
Accès en ligne:https://doi.org/10.1007/978-3-031-59318-5
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Table des matières:
  • Chapter 1. An Overview of Quantum Key Agreement Protocols
  • Chapter 2. Variational Circuit based Hybrid Quantum-Classical Algorithm VC-HQCA
  • Chapter 3 Quantum computing in Non Destructive Testing of Materials
  • Chapter 4. Enhanced Gaussian Quantum Particle Swarm Optimization for the Clustering of Biomedical Data
  • Chapter 5. Quantum Inspired Grey Wolf Optimizer for Convolutional Neural Network Hyperparameter Optimization
  • Chapter 6. Frequent itemsets mining using new quantum inspired elephant swarm algorithm
  • Chapter 7. Quantum Slime Mould Algorithm and application to urgent transportation
  • Chapter 9. Quantum FP-Growth for Association Rules Mining
  • Chapter 10 Gaussian Quantum-behaved PSO strategy for Lithium Battery model optimization
  • Chapter 11. Quantum Recurrent Neural Networks for Soil Profiles Prediction in Türkiye
  • Chapter 12. Q-CODA: Co-Designing Quantum Codes and Architectures for Hardware-Aware Quantum Error Correction
  • Chapter 13. Quantum Computing for Computer Vision: Applications, Challenges, and Research Tracks
  • Chapter 14. A multiparty efficient semi-quantum secret sharing protocol of specific bits
  • Chapter 15. Quantum Convolution for Convolutional Neural Networks
  • Chapter 16. A Quantum-Inspired Deep Learning Models for Skin Lesion Classification.