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Record Nr. |
UNINA9911054512803321 |
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Autore |
Jadhav Dipti |
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Titolo |
Quantum-Inspired Approaches for Intelligent Data Processing |
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Pubbl/distr/stampa |
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Newark : , : John Wiley & Sons, Incorporated, , 2026 |
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©2026 |
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ISBN |
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1-394-33644-6 |
1-394-33643-8 |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (313 pages) |
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Disciplina |
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Soggetti |
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Quantum computing |
Soft computing |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Introduction to Soft Computing for Intelligent Data Processing -- 1.1 Introduction -- 1.1.1 Limitations of Traditional Computing -- 1.1.2 The Philosophy of Soft Computing -- 1.1.3 Core Components of Soft Computing -- 1.1.4 Data Processing and Its Importance -- 1.1.5 Advantages of Soft Computing for Intelligent Data Processing -- 1.2 Literature Review -- 1.3 Proposed Methodology -- 1.3.1 Fuzzy-Neural Hybrid Systems -- 1.3.2 Evolutionary Fuzzy Systems -- 1.3.3 Neuro-Evolutionary Learning -- 1.3.4 Deep Learning with Soft Computing Integration -- 1.4 Results and Discussions -- 1.5 Conclusion -- References -- Chapter 2 Foundations of Quantum Computing: Overview, Foundation and Scope -- 2.1 Overview of Quantum Computing -- 2.1.1 Classical vs. Quantum Systems in Computing Techniques for Data Processing -- 2.1.2 Superposition and Entanglement in Quantum Computing for Enhanced Performance -- 2.1.2.1 Qubits and Quantum States -- 2.1.2.2 Superposition and Entanglement -- 2.1.2.3 Quantum Gates and Circuits -- 2.1.3 The Probabilistic Nature of Quantum Computing -- 2.1.4 Quantum Measurement and Observables in Computing Environment -- 2.2 Quantum Algorithms: Unleashing Quantum Power for Data Processing -- 2.2.1 Implementation of Shor's Algorithm for Integer Factorization |
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-- 2.2.2 Implementation of Grover's Algorithm for Unstructured Search -- 2.2.3 Quantum Approximation and Optimization Algorithms in the Present Scenario -- 2.3 Advantages and Challenges of Quantum Computing -- 2.3.1 Quantum Supremacy in Computing Technology -- 2.3.2 Challenges and Limitations in Quantum Computing -- 2.3.3 Quantum Error Correction Techniques -- 2.3.3.1 Errors in Quantum Systems-Sources of Errors -- 2.3.3.2 Quantum Error Correction Code (QECC) -- 2.3.3.3 Surface Code. |
2.3.3.4 Threshold Theorem -- 2.4 Quantum Computing Technologies: Building the Quantum Toolbox -- 2.4.1 The Significance of Superconducting Qubits in Quantum Computing -- 2.4.2 Physical Implementation of Trapped Ions and Quantum Dots in Quantum Computing -- 2.4.3 Topological Quantum Computing Strategy for Effective Solutions -- 2.4.3.1 Braiding of Anyons and Fault Tolerance -- 2.4.3.2 Topological Quantum Gates -- 2.5 Scope of Quantum Computing: Security, Optimization, and Machine Learning -- 2.5.1 Key Distribution and Secure Communication in Quantum Cryptography -- 2.5.2 Securing IoT Devices Using Encryption and Blockchain -- 2.5.3 Solving Combinatorial Optimization with Quantum Speedup -- 2.5.3.1 Quantum Approximate Optimization Algorithm (QAOA) for Combinatorial Problems -- 2.5.3.2 Quantum Annealing for Optimization -- 2.5.4 Quantum-Enhanced Machine Learning: Optimizing Energy Consumption with Quantum Algorithms -- 2.5.4.1 Key Concepts and Benefits in QML -- 2.5.4.2 Quantum Support Vector Machines (QSVM) -- 2.5.4.3 Quantum Neural Networks (QNN) -- 2.5.4.4 Quantum Reinforcement Learning (QRL) -- 2.6 The Future of Quantum Computing -- 2.6.1 Quantum Computing and Industry Applications -- 2.6.2 Quantum Cloud Computing -- 2.6.3 Quantum Computing's Role in National Security -- 2.6.4 Looking Ahead: Challenges and Opportunities -- Bibliography -- Chapter 3 Integration of Quantum Computing with Soft Computing for Data Processing -- 3.1 Introduction to Quantum Computing and Soft Computing -- 3.1.1 Comparative Analysis -- 3.2 Interrelation Between Quantum Computing and Soft Computing -- 3.2.1 Quantum Computing Advantage of Speed and Scalability Vs Soft Computing Advantages of 'Soft' and Approximations -- 3.3 Mathematical Analysis of the Interrelation between Quantum Computing and Soft Computing -- 3.3.1 Representing Quantum States and Qubits. |
3.3.2 Quantum-Soft Computing Hybrid Model -- 3.3.3 Quantum Probability and Fuzzy Membership Interrelation -- 3.3.4 Quantum-Soft Superposition for Approximation -- 3.3.5 Optimization Using Quantum-Soft Algorithms -- 3.3.6 Hybrid Error Minimization -- 3.4 Quantum-Inspired Algorithms for Enhanced Data Processing -- 3.4.1 Quantum Genetic Algorithms (QGAs) -- 3.4.2 Quantum Neural Networks (QNNs) -- 3.4.3 Quantum Particle Swarm Optimization (QPSO) and Its Role in Large-Scale Optimization -- 3.4.4 Quantum Particle Swarm Optimization (QPSO) -- 3.4.5 Advantages of Quantum-Inspired Algorithms in Data Processing and Optimization -- 3.4.6 Quantum Computing in Big Data Analytics -- 3.4.7 Parallel Data Processing in Modern Quantum Computing -- 3.5 Trade-Offs Between Computational Error and Processing Speed -- 3.6 Data Mining, Control Systems, and Pattern Recognition -- 3.6.1 Data Mining -- 3.6.2 Control Systems -- 3.6.3 Pattern Recognition -- 3.7 Challenges and Limitations of Classical Soft Computing in Large Datasets -- 3.7.1 Challenges Related to Size in Soft Computing Techniques -- 3.8 Quantum Computing Platforms for Soft Computing Integration -- 3.8.1 Overview of Quantum Development Platforms -- 3.9 Case Studies of Quantum and Soft Computing Integration in Industry -- 3.9.1 Security and |
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Privacy in Quantum-Enhanced Soft Computing -- 3.10 Introduction to Quantum Cryptography and Data Privacy -- 3.11 Quantum Algorithms for Privacy Preservation in Computation and Communication -- 3.12 Future Prospects and Emerging Research Gaps -- 3.12.1 Demand for Physical Quantum Algorithms and Well-Defined Theoretical Models -- 3.13 Security and Privacy Challenges in Quantum-Enhanced Soft Computing -- 3.14 Potential for Quantum-Inspired Tools in Artificial Intelligence and Big Data Analytics -- 3.15 Impact of Quantum and Soft Computing Integration on Data Processing. |
3.15.1 Benefits and Potential of Quantum-Soft Computing Synergy -- 3.16 Outlook on Future Applications in AI, Optimization, and Big Data -- References -- Chapter 4 Quantum-Soft Fusion: Transforming the Future of Data Handling -- 4.1 Introduction -- 4.2 Literature Work -- 4.3 Proposed Work -- 4.4 Results -- 4.5 Conclusion and Future Scope -- References -- Chapter 5 Quantum-Inspired Soft Computing for Intelligent IoT Big Data Processing -- 5.1 Introduction to Quantum-Inspired Soft Computing and IoT Big Data -- 5.2 Quantum-Inspired Genetic Algorithms (QIGAs) -- 5.2.1 Mathematical Model for Quantum Principles -- 5.2.1.1 Quantum-Inspired Selection -- 5.2.1.2 Quantum-Inspired Crossover -- 5.2.1.3 Quantum-Inspired Mutation -- 5.2.1.4 Fitness Evaluation -- 5.3 Quantum-Inspired Particle Swarm Optimization (QIPSO) Algorithm -- 5.4 Quantum Annealing Algorithm -- 5.5 Quantum-Inspired Artificial Neural Networks (QIA-NN) -- 5.5.1 Mathematical Model of Quantum Inspired Artificial Neural Networks -- 5.6 Performance Evaluation of Quantum Inspired Soft Computing Techniques -- 5.7 Role of QI Soft Computing Techniques for IoT Big Data Processing -- 5.7.1 Benefits of Quantum-Inspired Soft Computing for Big Data -- References -- Chapter 6 Quantum-Inspired Optimization Techniques for IoT-Driven Big Data Analysis -- 6.1 Overview of Internet of Things (IoT) and Big Data -- 6.2 Challenges in Handling Big Data in IoT -- 6.3 The Role of Optimization in IoT Data Analysis -- 6.4 Quantum-Inspired Optimization Techniques -- 6.4.1 Key Principles of Quantum Mechanics in QIO -- 6.4.2 Popular Quantum-Inspired Algorithms -- 6.5 Quantum-Inspired Optimization Algorithms for IoT -- 6.5.1 Basics of Quantum-Inspired Algorithms -- 6.5.2 Quantum Particle Swarm Optimization (QPSO) -- 6.5.3 Quantum-Inspired Evolutionary Algorithm (QIEA) -- 6.5.4 Quantum Annealing Inspired Optimization (QAIO). |
6.6 Performance Evaluation of Quantum-Inspired Optimization Techniques -- 6.7 Quantum-Inspired Optimization Techniques for Big Data Analysis -- 6.7.1 Applications of Quantum-Inspired Optimization Technique in Big-Data Analytics -- 6.8 Summary -- Bibliography -- Chapter 7 Quantum-Inspired Soft Computing for Intelligent Data Processing in Real-Life Scenarios -- 7.1 Introduction -- 7.2 Fundamentals of Quantum-Inspired Soft Computing -- 7.3 Key Concepts: Superposition, Entanglement, and Interference -- 7.4 Soft Computing Techniques: Fuzzy Logic, Genetic Algorithms, and Neural Networks -- 7.5 Quantum-Inspired Algorithms for Intelligent Data Processing -- 7.6 Quantum-Inspired Neural Networks -- 7.7 Hybrid Quantum Approaches in Soft Computing -- 7.8 Applications of Quantum-Inspired Soft Computing in Real-Life Scenarios -- 7.8.1 Healthcare Data Processing -- 7.8.2 Financial Data Analytics -- 7.8.3 Traffic Management and Smart Cities -- 7.9 IoT and Edge Computing in Industry 4.0 -- 7.10 Energy Management in Smart Grids -- 7.11 Fraud Detection in E-Commerce -- 7.12 Challenges and Limitations of Quantum-Inspired Soft Computing -- 7.12.1 Computational Complexity and Scalability -- 7.12.2 Data Noise and Uncertainty -- 7.12.3 Hardware and Algorithmic Limitations -- 7.13 Ethical and Social |
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Implications in Data Handling -- 7.13.1 Impact on Data Privacy and Security -- 7.13.2 Ethical Use of AI and Quantum Technologies in Decision-Making -- 7.13.3 Addressing Bias and Fairness -- 7.14 Future Trends in Quantum-Inspired Soft Computing -- 7.15 Case Studies and Practical Implementations -- 7.16 Conclusion -- References -- Chapter 8 Market Trends in Quantum-Inspired Soft Computing for Intelligent Data Processing -- 8.1 Introduction -- 8.2 Understanding Quantum-Inspired Soft Computing regarding Quantum-Inspired Soft Computing -- 8.2.1 Overview and Essential Ideas. |
8.2.2 Fundamental Elements. |
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Sommario/riassunto |
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Stay ahead of the technological curve with this comprehensive, practical guide that showcases how the fusion of quantum principles and soft computing is delivering transformative solutions across finance, healthcare, and manufacturing. |
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