Quantum inspired computational intelligence : research and applications

Quantum inspired computational intelligence research and applications

Detalles Bibliográficos
Otros Autores: Bhattacharyya, Siddhartha, 1975- editor (editor), Maulik, Ujjwal, editor, Dutta, Paramartha, editor
Formato: Libro electrónico
Idioma:Inglés
Publicado: Cambridge, MA, United States : Morgan Kaufmann [2017]
Materias:
Computational intelligence.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630257906719
Tabla de Contenidos:
  • Front Cover
  • Quantum Inspired Computational Intelligence: Research and Applications
  • Copyright
  • Dedication
  • Contents
  • 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
  • 1 Introduction and Literature Background
  • 2 Dynamic Learning of an Entanglement Indicator
  • 3 Learning with Noise
  • 4 Decoherence
  • 5 Noise Plus Decoherence
  • 6 Conclusions
  • Acknowledgments
  • References
  • Chapter 2: Quantum computing and supervised machine learning: Training, model selection, and error estimation
  • 1 Introduction
  • 2 The Supervised Learning Problem: Training, Model Selection, and Error Estimation
  • 3 Classical and Quantum Computing
  • 3.1 Classical Computing
  • 3.2 Quantum Computing
  • 4 Quantum Computing for Training
  • 4.1 Bounded Loss Functions
  • 4.1.1 Example: The problems behind the convex relaxation
  • 4.2 Energy-Efficient Models
  • 4.3 Sparse Solutions
  • 4.4 Gibbs and Bayes Classifiers
  • 5 Quantum Computing for Model Selection and Error Estimation
  • 5.1 Out-of-Sample Methods: Hold-Out, Cross Validation, and Bootstrap
  • 5.2 Vapnik-Chervonenkis Theory
  • 5.3 (Local) Rademacher Complexity Theory
  • 5.4 PAC-Bayes Theory
  • 5.4.1 Algorithmic stability
  • 5.4.2 Compression bound
  • 6 Conclusions
  • References
  • Chapter 3: Field computation: A framework for quantum-inspired computing
  • 1 Introduction
  • 2 Fields
  • 3 Field computation
  • 4 Derivatives of Field Transformations
  • 5 Examples of Field Computation
  • 5.1 Neural Network-Style Computation
  • 5.2 Discrete Basis Function Networks
  • 5.3 Continua of Basis Functions
  • 5.4 Approximations of Spatial Integration and Differentiation
  • 5.5 Iterative Field Computation
  • 5.6 Field Differential Equations.
  • 5.7 Reaction-Diffusion Computation
  • 6 Change of Field Domain
  • 7 Cortical Field Computation
  • 7.1 Information Fields
  • 7.2 Nonlinear Computation by Topographic Maps
  • 7.3 Field Representations of Discrete Symbols
  • 7.4 Gabor Representation and the Uncertainty Principle
  • 8 Universal Field Computation
  • 9 General-Purpose Field Computers
  • 10 Conclusions and Future Work
  • References
  • Chapter 4: Design of cellular quantum-inspired evolutionary algorithms with random topologies
  • 1 Introduction
  • 2 Literature Survey
  • 3 Cellular Quantum-Inspired Evolutionary Algorithms
  • 4 Benchmark Problems
  • 4.1 P-PEAKS Problems
  • 4.2 0-1 Knapsack Problems
  • 5 Testing, Results, and Analysis
  • 5.1 Static Random Topologies
  • 5.1.1 P-PEAKS problems
  • 5.1.2 0-1 knapsack problems
  • 5.2 Dynamic Random Topology
  • 5.3 Adaptive Random Topology
  • 5.4 Comparative Study
  • 6 Conclusions and Future Work
  • Acknowledgments
  • References
  • Part II: Applications
  • Chapter 5: An efficient pure color image denoising using quantum parallel bidirectional self-organizing neural network arc ...
  • 1 Introduction
  • 2 Review of the Literature
  • 3 Proposed Work
  • 4 Fundamentals of Fuzzy Sets
  • 4.1 Fuzzy Set Concepts
  • 4.2 Fuzzy Set Operations
  • 4.3 Fuzzy Cardinality
  • 5 Quantum Computing Fundamentals
  • 5.1 Concept of Qubits
  • 5.2 Fundamentals of a Rotation Gate
  • 5.3 Quantum Measurement
  • 6 Parallel Bidirectional Self-Organizing Neural Network Architecture
  • 7 Hopfield Network
  • 8 Quantum Parallel Bidirectional Self-Organizing Neural Network Architecture
  • 8.1 Dynamics of Networks
  • 8.2 Network Weight Adjustment
  • 8.3 Network Parallel Self-Supervision Algorithm
  • 9 Experimental Results
  • 9.1 Kolmogorov-Smirnov Test
  • 10 Conclusion
  • References
  • Chapter 6: Quantum-inspired multi-objective simulated annealing for bilevel image thresholding.
  • 1 Introduction
  • Segmentation
  • Thresholding
  • Quantum computing
  • Metaheuristic algorithm
  • Optimization
  • 2 Literature Survey
  • 3 Overview of Simulated Annealing
  • 4 Multi-objective Optimization
  • 5 Quantum Computing Overview
  • 6 Thresholding Technique
  • 6.1 Huang's Method for Bilevel Image Thresholding
  • 7 Proposed Method
  • 7.1 Complexity Analysis
  • 8 Experiments and Discussion
  • 8.1 Thresholding Results of the Techniques Investigated
  • 8.2 Efficiency of Techniques Investigated
  • 8.3 Conclusion and Future Prospects
  • References
  • Chapter 7: Quantum inspired computational intelligent techniquesin image segmentation
  • 1 Introduction
  • 1.1 Computational Intelligence (CI)
  • 1.2 Quantum Computing (QC)
  • 1.2.1 Quantum theory
  • 1.2.2 Quantum theory's essential elements
  • 1.2.3 Differences between conventional and QC
  • 1.3 Image Segmentation
  • 2 Quantum Inspired CI Techniques
  • 2.1 Inspired by Neural Network
  • 2.2 Inspired by Fuzzy System
  • 2.3 Inspired by Evolutionary Methods
  • 3 Image Segmentation Using Quantum Inspired Evolutionary Methods
  • 3.1 Case Study 1: Quantum Inspired Multiobjective Evolutionary Clustering Algorithm (QMEC)
  • 3.1.1 The qubit individuals population initialization
  • 3.1.2 Observing operator
  • 3.1.3 Fitness function
  • 3.1.4 Nondominate sort and elitism
  • 3.1.5 Q-gate updating
  • 3.1.6 Solution selection scheme
  • 3.1.7 Evaluate indexes
  • 3.1.8 The complexity of computations
  • 3.1.9 Experimental setup
  • 3.1.10 Results on simulated SAR image
  • 3.1.11 Results on remote sensing images
  • 3.2 Case Study 2: A Quantum Inspired Evolutionary Algorithm for Multiobjective Image Segmentation
  • 3.2.1 Results of the experiment
  • 4 Conclusion
  • References
  • Chapter 8: Fuzzy evaluated quantum cellular automata approach for watershed image analysis
  • 1 Introduction
  • 2 Fuzzy C-Means Algorithm.
  • 3 Cellular Automata Model
  • 4 Quantum Cellular Automata
  • 5 Partitioned Quantum Cellular Automata
  • 6 Quantum-Dot Cellular Automata
  • 7 Hybrid Fuzzy-Partitioned Quantum Cellular Automata Clustering Approach
  • 8 Cellular Automata-Based Neighborhood Priority Correction Method
  • 9 Partitioned Quantum Cellular Approach Using Majority Voting
  • 10 Application to Pixel Classification
  • 11 Quantitative Analysis
  • 12 Statistical Analysis
  • 13 Future Research Directions
  • 14 Conclusion
  • References
  • Chapter 9: Quantum-inspired evolutionary algorithm for scaling factor optimization during manifold medical information embed
  • 1 Introduction
  • 2 Related Work
  • 3 Mathematical Transformation
  • 3.1 Discrete Wavelet Transform
  • 3.2 Discrete Cosine Transform
  • 3.3 Singular Value Decomposition
  • 4 Evolutionary Algorithms and Quantum-Inspired Algorithms
  • 4.1 Overview of Evolutionary Algorithms
  • 4.1.1 Complexity analysis of evolutionary algorithms
  • 4.2 Overview of Quantum Computing
  • 4.2.1 Qubit representation
  • 4.2.2 Quantum operator
  • 4.3 Genetic Algorithm
  • 4.3.1 Complexity analysis of genetic algorithms
  • 4.4 Quantum-Inspired Genetic Algorithm
  • 4.4.1 Complexity analysis of the quantum-inspired genetic algorithm
  • 4.5 Quantum-Inspired Evolutionary Algorithm
  • 4.5.1 Complexity analysis of the quantum-inspired evolutionary algorithm
  • 5 Proposed Method
  • 5.1 Watermark Embedding
  • 5.2 Watermark Extraction
  • 5.3 Generation of Optimal Scaling Factors With Use of the Genetic Algorithm, Quantum-Inspired Genetic Algorithm, or Quantum-
  • 6 Results and Discussion
  • 6.1 Performance Evaluation
  • 6.2 Comparative Study of the Convergence Graphs
  • 7 Conclusion
  • Acknowledgments
  • References
  • Chapter 10: Digital filter design using a quantum-inspired multiobjective cat swarm optimization algorithm
  • 1 Introduction.
  • 2 Finite Impulse Response Filter Design as a Multiobjective Optimization Problem
  • 3 Hilbert Transformer Design Using Finite Impulse Response Filters
  • 4 Quantum-Inspired Multiobjective Cat Swarm Optimization Algorithm
  • 4.1 Multiobjective Cat Swarm Optimization
  • 4.2 Quantum-Inspired Multiobjective Cat Swarm Optimization
  • 5 Other Multiobjective Optimization Algorithms Used
  • 5.1 Nondominated Sorting Genetic Algorithm II
  • 5.2 Multiobjective Particle Swarm Optimization
  • 5.3 Multiobjective Differential Evolution
  • 6 Results and Discussion
  • Stage I: Application of the Proposed Quantum-Inspired Multiobjective Cat Swarm Optimization in Hilbert Transformer Design
  • Stage 2: Application of the Proposed Quantum-Inspired Multiobjective Cat Swarm Optimization in Low-Power Finite Impulse Resp
  • Stage 3: Statistical Analysis
  • 7 Conclusion
  • References
  • Chapter 11: A novel graph clustering algorithm based on discrete-time quantum random walk
  • 1 Introduction
  • 2 Classical Approach of Clustering
  • 2.1 Hierarchical Clustering Algorithms
  • 2.2 Nearest-Neighbor Algorithm
  • 3 Quantum Gates and Quantum Circuits
  • Commonly used gates
  • 3.1 The Controlled NOT Gate
  • 3.2 The Toffoli Gate
  • 3.3 The Hadamard Gate
  • 4 Quantum Computation and Quantum Random Walk
  • 5 Continuous-Time Quantum Random Walk
  • 6 Discrete Time Quantum Random Walk
  • 6.1 Discrete Time Quantum Random Walks on a Line
  • 6.1.1 Grover operator
  • 6.2 Discrete Fourier Transform Coin
  • 6.3 Discrete-Time Quantum Random Walks on Graphs
  • 7 Quantum Computing Language
  • 7.1 Features of Quantum Computing Language
  • 8 Encoding Test Graphs for Discrete-Time Quantum Random Walk
  • 8.1 Discrete-Time Quantum Random Walk on Testgraph1
  • 8.1.1 Result for the first iteration
  • 8.1.2 Result for the second iteration
  • 8.1.3 Result for the third iteration.
  • 9 Quantum Circuits for the Proposed Quantum Algorithm.

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