book

Quantum Inspired Computational Intelligence

Name: Quantum Inspired Computational Intelligence
ISBN: 9780128044377

by Siddhartha Bhattacharyya, Ujjwal Maulik, Paramartha Dutta

September 2016

Intermediate to advanced

506 pages

15h 38m

English

Morgan Kaufmann

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Cover image
Title page
Table of Contents
Copyright
Dedication
List of Contributors
About the Editors
Foreword
Preface
Acknowledgments

Part I: Research
Chapter 1: Quantum neural computation of entanglement is robust to noise and decoherence
AbstractAcknowledgments1 Introduction and Literature Background2 Dynamic Learning of an Entanglement Indicator3 Learning with Noise4 Decoherence5 Noise Plus Decoherence6 Conclusions
Chapter 2: Quantum computing and supervised machine learning: Training, model selection, and error estimation
Abstract1 Introduction2 The Supervised Learning Problem: Training, Model Selection, and Error Estimation3 Classical and Quantum Computing4 Quantum Computing for Training
5 Quantum Computing for Model Selection and Error Estimation
6 Conclusions
Chapter 3: Field computation: A framework for quantum-inspired computing
Abstract1 Introduction2 Fields3 Field computation4 Derivatives of Field Transformations5 Examples of Field Computation6 Change of Field Domain7 Cortical Field Computation8 Universal Field Computation9 General-Purpose Field Computers10 Conclusions and Future Work
Chapter 4: Design of cellular quantum-inspired evolutionary algorithms with random topologies
AbstractAcknowledgments1 Introduction2 Literature Survey3 Cellular Quantum-Inspired Evolutionary Algorithms4 Benchmark Problems5 Testing, Results, and Analysis6 Conclusions and Future Work
Part II: Applications
Chapter 5: An efficient pure color image denoising using quantum parallel bidirectional self-organizing neural network architecture
Abstract1 Introduction2 Review of the Literature3 Proposed Work4 Fundamentals of Fuzzy Sets5 Quantum Computing Fundamentals6 Parallel Bidirectional Self-Organizing Neural Network Architecture7 Hopfield Network8 Quantum Parallel Bidirectional Self-Organizing Neural Network Architecture9 Experimental Results10 Conclusion
Chapter 6: Quantum-inspired multi-objective simulated annealing for bilevel image thresholding
Abstract1 Introduction2 Literature Survey3 Overview of Simulated Annealing4 Multi-Objective Optimization5 Quantum Computing Overview6 Thresholding Technique7 Proposed Method8 Experiments and Discussion
Chapter 7: Quantum inspired computational intelligent techniques in image segmentation
Abstract1 Introduction2 Quantum Inspired CI Techniques3 Image Segmentation Using Quantum Inspired Evolutionary Methods4 Conclusion
Chapter 8: Fuzzy evaluated quantum cellular automata approach for watershed image analysis
Abstract1 Introduction2 Fuzzy C-Means Algorithm3 Cellular Automata Model4 Quantum Cellular Automata5 Partitioned Quantum Cellular Automata6 Quantum-Dot Cellular Automata7 Hybrid Fuzzy-Partitioned Quantum Cellular Automata Clustering Approach8 Cellular Automata-Based Neighborhood Priority Correction Method9 Partitioned Quantum Cellular Approach Using Majority Voting10 Application to Pixel Classification11 Quantitative Analysis12 Statistical Analysis13 Future Research Directions14 Conclusion
Chapter 9: Quantum-inspired evolutionary algorithm for scaling factor optimization during manifold medical information embedding
AbstractAcknowledgments1 Introduction2 Related Work3 Mathematical Transformation4 Evolutionary Algorithms and Quantum-Inspired Algorithms4.2 Overview of Quantum Computing4.3 Genetic Algorithm4.4 Quantum-Inspired Genetic Algorithm4.5 Quantum-Inspired Evolutionary Algorithm5 Proposed Method6 Results and Discussion7 Conclusion
Chapter 10: Digital filter design using quantum-inspired multiobjective cat swarm optimization algorithm
Abstract1 Introduction2 Finite Impulse Response Filter Design as a Multiobjective Optimization Problem3 Hilbert Transformer Design Using Finite Impulse Response Filters4 Quantum-Inspired Multiobjective Cat Swarm Optimization Algorithm5 Other Multiobjective Optimization Algorithms Used6 Results and Discussion7 Conclusion
Chapter 11: A novel graph clustering algorithm based on discrete-time quantum random walk
Abstract1 Introduction2 Classical Approach of Clustering3 Quantum Gates and Quantum Circuits4 Quantum computation and quantum random walk5 Continuous-Time Quantum Random Walk6 Discrete Time Quantum Random Walk7 Quantum Computing Language8 Encoding Test Graphs for Discrete-Time Quantum Random Walk9 Quantum Circuits for the Proposed Quantum Algorithm10 Mathematical Approach11 Quantum Cluster Analysis12 Proposed Graph-Based Quantum Clustering Algorithm13 Experimental Results14 Performance Analysis of Classical Clustering Algorithms and the Proposed Quantum Clustering Algorithm15 Conclusion
Chapter 12: The Schrödinger equation as inspiration for a client portfolio simulation hybrid system based on dynamic Bayesian networks and the REFII model
Abstract1 Introduction2 Background3 Basic Concept of the Proposed Model4 Case Study: Implementation of the Proposed Model on a Retail Portfolio5 Future Research Directions6 Discussion7 Conclusion
Chapter 13: A quantum-inspired hybrid intelligent position monitoring system in wireless networks
Abstract1 Introduction2 Related Work3 Problem Identification: Open Research Problems4 System Model and Algorithms5 Mathematical Model6 Proofs of Correctness7 Performance Evaluation8 Case Studies9 Conclusion
Author Index
Subject Index

Content preview from Quantum Inspired Computational Intelligence

Chapter 1: Quantum neural computation of entanglement is robust to noise and decoherence

Abstract

Acknowledgments

1 Introduction and Literature Background

2 Dynamic Learning of an Entanglement Indicator

3 Learning with Noise

4 Decoherence

5 Noise Plus Decoherence

6 Conclusions

Chapter 2: Quantum computing and supervised machine learning: Training, model selection, and error estimation

Abstract

1 Introduction

2 The Supervised Learning Problem: Training, Model Selection, and Error Estimation

3 Classical and Quantum Computing

4 Quantum Computing for Training

5 Quantum Computing for Model Selection ...

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Quantum Inspired Computational Intelligence

by Siddhartha Bhattacharyya, Ujjwal Maulik, Paramartha Dutta

Table of Contents

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