Quantum Inspired Computational Intelligence

Table of contents

1. Cover image
2. Title page
3. Table of Contents
4. Copyright
5. Dedication
6. List of Contributors
7. About the Editors
8. Foreword
9. Preface
10. Acknowledgments
11. Part I: Research
    1. Chapter 1: Quantum neural computation of entanglement is robust to noise and decoherence
       1. Abstract
       2. Acknowledgments
       3. 1 Introduction and Literature Background
       4. 2 Dynamic Learning of an Entanglement Indicator
       5. 3 Learning with Noise
       6. 4 Decoherence
       7. 5 Noise Plus Decoherence
       8. 6 Conclusions
    2. Chapter 2: Quantum computing and supervised machine learning: Training, model selection, and error estimation
       1. Abstract
       2. 1 Introduction
       3. 2 The Supervised Learning Problem: Training, Model Selection, and Error Estimation
       4. 3 Classical and Quantum Computing
       5. 4 Quantum Computing for Training
       6. 5 Quantum Computing for Model Selection and Error Estimation
       7. 6 Conclusions
    3. Chapter 3: Field computation: A framework for quantum-inspired computing
       1. Abstract
       2. 1 Introduction
       3. 2 Fields
       4. 3 Field computation
       5. 4 Derivatives of Field Transformations
       6. 5 Examples of Field Computation
       7. 6 Change of Field Domain
       8. 7 Cortical Field Computation
       9. 8 Universal Field Computation
       10. 9 General-Purpose Field Computers
       11. 10 Conclusions and Future Work
    4. Chapter 4: Design of cellular quantum-inspired evolutionary algorithms with random topologies
       1. Abstract
       2. Acknowledgments
       3. 1 Introduction
       4. 2 Literature Survey
       5. 3 Cellular Quantum-Inspired Evolutionary Algorithms
       6. 4 Benchmark Problems
       7. 5 Testing, Results, and Analysis
       8. 6 Conclusions and Future Work
12. Part II: Applications
    1. Chapter 5: An efficient pure color image denoising using quantum parallel bidirectional self-organizing neural network architecture
       1. Abstract
       2. 1 Introduction
       3. 2 Review of the Literature
       4. 3 Proposed Work
       5. 4 Fundamentals of Fuzzy Sets
       6. 5 Quantum Computing Fundamentals
       7. 6 Parallel Bidirectional Self-Organizing Neural Network Architecture
       8. 7 Hopfield Network
       9. 8 Quantum Parallel Bidirectional Self-Organizing Neural Network Architecture
       10. 9 Experimental Results
       11. 10 Conclusion
    2. Chapter 6: Quantum-inspired multi-objective simulated annealing for bilevel image thresholding
       1. Abstract
       2. 1 Introduction
       3. 2 Literature Survey
       4. 3 Overview of Simulated Annealing
       5. 4 Multi-Objective Optimization
       6. 5 Quantum Computing Overview
       7. 6 Thresholding Technique
       8. 7 Proposed Method
       9. 8 Experiments and Discussion
    3. Chapter 7: Quantum inspired computational intelligent techniques in image segmentation
       1. Abstract
       2. 1 Introduction
       3. 2 Quantum Inspired CI Techniques
       4. 3 Image Segmentation Using Quantum Inspired Evolutionary Methods
       5. 4 Conclusion
    4. Chapter 8: Fuzzy evaluated quantum cellular automata approach for watershed image analysis
       1. Abstract
       2. 1 Introduction
       3. 2 Fuzzy C-Means Algorithm
       4. 3 Cellular Automata Model
       5. 4 Quantum Cellular Automata
       6. 5 Partitioned Quantum Cellular Automata
       7. 6 Quantum-Dot Cellular Automata
       8. 7 Hybrid Fuzzy-Partitioned Quantum Cellular Automata Clustering Approach
       9. 8 Cellular Automata-Based Neighborhood Priority Correction Method
       10. 9 Partitioned Quantum Cellular Approach Using Majority Voting
       11. 10 Application to Pixel Classification
       12. 11 Quantitative Analysis
       13. 12 Statistical Analysis
       14. 13 Future Research Directions
       15. 14 Conclusion
    5. Chapter 9: Quantum-inspired evolutionary algorithm for scaling factor optimization during manifold medical information embedding
       1. Abstract
       2. Acknowledgments
       3. 1 Introduction
       4. 2 Related Work
       5. 3 Mathematical Transformation
       6. 4 Evolutionary Algorithms and Quantum-Inspired Algorithms
       7. 4.2 Overview of Quantum Computing
       8. 4.3 Genetic Algorithm
       9. 4.4 Quantum-Inspired Genetic Algorithm
       10. 4.5 Quantum-Inspired Evolutionary Algorithm
       11. 5 Proposed Method
       12. 6 Results and Discussion
       13. 7 Conclusion
    6. Chapter 10: Digital filter design using quantum-inspired multiobjective cat swarm optimization algorithm
       1. Abstract
       2. 1 Introduction
       3. 2 Finite Impulse Response Filter Design as a Multiobjective Optimization Problem
       4. 3 Hilbert Transformer Design Using Finite Impulse Response Filters
       5. 4 Quantum-Inspired Multiobjective Cat Swarm Optimization Algorithm
       6. 5 Other Multiobjective Optimization Algorithms Used
       7. 6 Results and Discussion
       8. 7 Conclusion
    7. Chapter 11: A novel graph clustering algorithm based on discrete-time quantum random walk
       1. Abstract
       2. 1 Introduction
       3. 2 Classical Approach of Clustering
       4. 3 Quantum Gates and Quantum Circuits
       5. 4 Quantum computation and quantum random walk
       6. 5 Continuous-Time Quantum Random Walk
       7. 6 Discrete Time Quantum Random Walk
       8. 7 Quantum Computing Language
       9. 8 Encoding Test Graphs for Discrete-Time Quantum Random Walk
       10. 9 Quantum Circuits for the Proposed Quantum Algorithm
       11. 10 Mathematical Approach
       12. 11 Quantum Cluster Analysis
       13. 12 Proposed Graph-Based Quantum Clustering Algorithm
       14. 13 Experimental Results
       15. 14 Performance Analysis of Classical Clustering Algorithms and the Proposed Quantum Clustering Algorithm
       16. 15 Conclusion
    8. Chapter 12: The Schrödinger equation as inspiration for a client portfolio simulation hybrid system based on dynamic Bayesian networks and the REFII model
       1. Abstract
       2. 1 Introduction
       3. 2 Background
       4. 3 Basic Concept of the Proposed Model
       5. 4 Case Study: Implementation of the Proposed Model on a Retail Portfolio
       6. 5 Future Research Directions
       7. 6 Discussion
       8. 7 Conclusion
    9. Chapter 13: A quantum-inspired hybrid intelligent position monitoring system in wireless networks
       1. Abstract
       2. 1 Introduction
       3. 2 Related Work
       4. 3 Problem Identification: Open Research Problems
       5. 4 System Model and Algorithms
       6. 5 Mathematical Model
       7. 6 Proofs of Correctness
       8. 7 Performance Evaluation
       9. 8 Case Studies
       10. 9 Conclusion
13. Author Index
14. Subject Index