Quantum Inspired Computational Intelligence: Research and Applications explores the latest quantum computational intelligence approaches, initiatives, and applications in computing, engineering, science, and business. The book explores this emerging field of research that applies principles of quantum mechanics to develop more efficient and robust intelligent systems. Conventional computational intelligence—or soft computing—is conjoined with quantum computing to achieve this objective. The models covered can be applied to any endeavor which handles complex and meaningful information.
1. 1 Introduction - p. 33 2. 2 The Supervised Learning Problem: Training, Model Selection, and Error Estimation - p. 35 3. 3 Classical and Quantum Computing - p. 40 4. 4 Quantum Computing for Training - p. 48 5. 5 Quantum Computing for Model Selection and Error Estimation - p. 59 6. 6 Conclusions - p. 75 7. Chapter 3: Field computation: A framework for quantum-inspired computing - p. 83 8. Chapter 4: Design of cellular quantum-inspired evolutionary algorithms with random topologies - p. 110 9. Part II: Applications - p. 146 10. Chapter 5: An efficient pure color image denoising using quantum parallel bidirectional self-organizing neural network architecture - p. 146 11. Chapter 6: Quantum-inspired multi-objective simulated annealing for bilevel image thresholding - p. 205 12. Chapter 7: Quantum inspired computational intelligent techniques in image segmentation - p. 232 13. Chapter 8: Fuzzy evaluated quantum cellular automata approach for watershed image analysis - p. 258 14. Chapter 9: Quantum-inspired evolutionary algorithm for scaling factor optimization during manifold medical information embedding - p. 284 15. Chapter 10: Digital filter design using quantum-inspired multiobjective cat swarm optimization algorithm - p. 326 16. Chapter 11: A novel graph clustering algorithm based on discrete-time quantum random walk - p. 359 17. Chapter 12: The Schrödinger equation as inspiration for a client portfolio simulation hybrid system based on dynamic Bayesian networks and the REFII model - p. 389 18. Chapter 13: A quantum-inspired hybrid intelligent position monitoring system in wireless networks - p. 416 19. Author Index - p. 453 20. Subject Index - p. 467
Quantum Inspired Computational Intelligence: Research and Applications explores the latest quantum computational intelligence approaches, initiatives, and applications in computing, engineering, science, and business. The book explores this emerging field of research that applies principles of quantum mechanics to develop more efficient and robust intelligent systems. Conventional computational intelligence—or soft computing—is conjoined with quantum computing to achieve this objective. The models covered can be applied to any endeavor which handles complex and meaningful information.
1. 1 Introduction - p. 33 2. 2 The Supervised Learning Problem: Training, Model Selection, and Error Estimation - p. 35 3. 3 Classical and Quantum Computing - p. 40 4. 4 Quantum Computing for Training - p. 48 5. 5 Quantum Computing for Model Selection and Error Estimation - p. 59 6. 6 Conclusions - p. 75 7. Chapter 3: Field computation: A framework for quantum-inspired computing - p. 83 8. Chapter 4: Design of cellular quantum-inspired evolutionary algorithms with random topologies - p. 110 9. Part II: Applications - p. 146 10. Chapter 5: An efficient pure color image denoising using quantum parallel bidirectional self-organizing neural network architecture - p. 146 11. Chapter 6: Quantum-inspired multi-objective simulated annealing for bilevel image thresholding - p. 205 12. Chapter 7: Quantum inspired computational intelligent techniques in image segmentation - p. 232 13. Chapter 8: Fuzzy evaluated quantum cellular automata approach for watershed image analysis - p. 258 14. Chapter 9: Quantum-inspired evolutionary algorithm for scaling factor optimization during manifold medical information embedding - p. 284 15. Chapter 10: Digital filter design using quantum-inspired multiobjective cat swarm optimization algorithm - p. 326 16. Chapter 11: A novel graph clustering algorithm based on discrete-time quantum random walk - p. 359 17. Chapter 12: The Schrödinger equation as inspiration for a client portfolio simulation hybrid system based on dynamic Bayesian networks and the REFII model - p. 389 18. Chapter 13: A quantum-inspired hybrid intelligent position monitoring system in wireless networks - p. 416 19. Author Index - p. 453 20. Subject Index - p. 467
