Quantum inspired computational intelligence : research and applications / [edited by] Siddhartha Bhattacharyya, Ujjwal Maulik and Paramartha Dutta.

1. Quantum Neural Computation of Entanglement is Robust to Noise and Decoherence --
2. Quantum Computing and Supervised Machine Learning: Training, Model Selection, and Error Estimation --
3. Field Computation: A Framework for Quantum-Inspired Computing --
4. Design of Cellular Quantum-Inspired Evolutionary Algorithms With Random Topologies --
5. An Efficient Pure Color Image Denoising Using Quantum Parallel Bidirectional Self-Organizing Neural Network Architecture --
6. Quantum-Inspired Multi-Objective Simulated Annealing for Bilevel Image Thresholding --
7. Quantum Inspired Computational Intelligent Techniques in Image Segmentation --
8. Fuzzy Evaluated Quantum Cellular Automata Approach for Watershed Image Analysis --
9. Quantum-Inspired Evolutionary Algorithm for Scaling Factor Optimization During Manifold Medical Information Embedding --
10. Digital Filter Design Using Quantum-Inspired Multiobjective Cat Swarm Optimization Algorithm --
11. A Novel Graph Clustering Algorithm Based on Discrete-Time Quantum Random Walk --
12. The Schrodinger Equation as Inspiration for a Client Portfolio Simulation Hybrid System Based on Dynamic Bayesian Networks and the REFII Model --
13. A Quantum-Inspired Hybrid Intelligent Position Monitoring System in Wireless Networks.

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.