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Quantum-Inspired Approaches for Intelligent Data Processing

Edited by Balamurugan Balusamy, Suman Avdhesh Yadav, S. Ramesh, and M. Vinoth Kumar
Copyright: 2025   |   Expected Pub Date: 2025
ISBN: 9781394336418  |  Hardcover  |  
318 pages
Price: $225 USD
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One Line Description
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.

Audience
Professors, researchers, industrialists, and advanced-level computer science students, seeking advanced insights at the dynamic intersection of quantum and soft computing.

Description
Quantum-Inspired Approaches for Intelligent Data Processing explores the cutting-edge fusion of quantum computing principles and soft computing techniques, unraveling the synergistic potential of these two paradigms. The book uses a comprehensive interdisciplinary approach, delving into the foundations of quantum mechanics and soft computing essentials, including fuzzy logic, genetic algorithms, and neural networks. Distinctive in its practical focus, the book showcases how this integration enhances intelligent data processing across various industries, offering tangible solutions to complex challenges. Through real-world applications, this book illuminates the transformative impact of quantum-inspired soft computing across multiple industries, from finance and healthcare to manufacturing. It incorporates case studies, examples, and market analyses, providing a holistic understanding of the subject and exploring emerging trends, challenges, and future opportunities, making it an invaluable resource for researchers and industrialists navigating the dynamic intersection of quantum computing and soft computing in intelligent data processing.

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Author / Editor Details
Balamurugan Balusamy, PhD is an Associate Dean of Students at Shiv Nadar University with more than 12 years of academic experience. He has published more than 200 articles in international journals and conferences, authored and edited more than 80 books, and given more than 195 talks in international symposia. His research focuses on engineering education, blockchain, and data sciences.

Suman Avdhesh Yadav is an Assistant Professor in the Department of Computer Science Engineering and Head of the Internal Quality Assurance Cell at Amity University. She has published one book, six book chapters, three patents, and more than 33 articles in peer-reviewed journals and conferences of international repute. Her research interests include IoT, soft computing, wireless sensor networks, network security, cloud computing, and AI.

S. Ramesh, PhD is an Associate Professor in the Department of Applied Machine Learning in the Saveetha School of Engineering at the Saveetha Institute of Medical and Technical Sciences with more than 13 years of teaching and research experience. He has published more than 60 research articles and holds 19 patents. His research interests involve machine learning, artificial intelligence, computer vision, and the Internet of Things.

M. Vinoth Kumar, PhD is an Assistant Professor in the Department of Electronics and Communication Engineering at the SRM Institute of Science and Technology. He has more than 25 publications in international journals and conferences. His research interests are optical fiber communication networks, free-space optical communication systems, photonics, and radio-over-fiber.

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Table of Contents
Preface
1. Introduction to Soft Computing for Intelligent Data Processing
Tiyas Sarkar, Manik Rakhra and Baljinder Kaur
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
2. Foundations of Quantum Computing: Overview, Foundation and Scope
Mohit Chandra Saxena and Abhishek Tamrakar
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
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
References
3. Integration of Quantum Computing with Soft Computing for Data Processing
Vanya Arun, Kapil Deo Bodha, Ankita Awasthi and Munish Sabharwal
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 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
4. Quantum-Soft Fusion: Transforming the Future of Data Handling
Sandeep Kumar, Jagjit Singh Dhatterwal and Kuldeep Singh Kaswan
4.1 Introduction
4.2 Literature Work
4.3 Proposed Work
4.4 Results
4.5 Conclusion and Future Scope
References
5. Quantum-Inspired Soft Computing for Intelligent IoT Big Data Processing
Firoz Khan, Amutha Prabakar Muniyandi and Balamurugan Balusamy
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
6. Quantum-Inspired Optimization Techniques for IoT-Driven Big Data Analysis
Firoz Khan, Amutha Prabakar Muniyandi and Balamurugan Balusamy
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
7. Quantum-Inspired Soft Computing for Intelligent Data Processing in Real-Life Scenarios
Kuldeep Singh Kaswan, Jagjit Singh Dhatterwal, Kiran Malik, Santar Pal Singh and S. Viveka
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 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
8. Market Trends in Quantum-Inspired Soft Computing for Intelligent Data Processing
Shubh Kapoor and Vikas Garg
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
8.2.2.1 Quantum Principles
8.2.2.2 Soft Computing Techniques
8.2.2.3 Hybrid Models
8.2.3 Benefits and Advantages
8.2.3.1 Big Data
8.2.3.2 Lower Computational Cost
8.2.3.3 Scalability
8.3 Current Market Landscape
8.3.1 Current Market Size and Future Market Size and Growth Trends
8.3.1.1 Key Growth Drivers
8.3.2 Key Industry Players
8.3.2.1 Microsoft Azure Quantum
8.3.2.2 D-Wave Systems
8.3.2.3 IBM Quantum
8.3.2.4 Implications for the Market
8.3.3 Industry Adoption
8.3.4 The Implication of Increasing the Usage of ICTs
8.3.5 Updated Technology Intelligence for Quantum-Inspired Soft Computing
8.4 Hardware Developments
8.4.1 Role of Modern GPUs and TPUs
8.5 Algorithmic Innovations
8.5.1 Investment Strategies and Trading with Hybrid Quantum Systems: Applications of Quantum Approximate Optimization Algorithm (QAOA)
8.5.2 Tensor Net Based on Quantum Computational
8.6 Interfaces with AI and Machine Learning
8.7 Computational Constraints
8.8 Standardization Issues
8.9 Skill Gaps
8.10 New Areas of Use in QISC
8.10.1 Autonomous Systems: Managing Road Mapping and Decision Making
8.10.2 Natural Language Processing (NLP): Enhancing Language Models
8.11 Partnership and Ecosystem Creation
8.11.1 NQI and P3
8.12 Towards Quantum Computing: The Hybrid Future
8.12.1 Exploring the Coupling of Mechanical and Field Systems
8.12.2 The Path to Hybrid Systems
8.13 Conclusion
References
9. Security and Privacy Aspects in Quantum-Inspired Soft Computing for Intelligent Data Processing
Kuldeep Singh Kaswan, Jagjit Singh Dhatterwal, Kiran Malik, Naresh Kumar, S. S. Sridhar and S. Babeetha
9.1 Introduction
9.2 Foundations of Quantum-Inspired Soft Computing
9.3 Security Challenges in Quantum-Inspired Soft Computing
9.4 Vulnerabilities in Quantum-Inspired Algorithms
9.5 Security Threats in Intelligent Data Processing
9.6 Case Studies of Security Breaches
9.7 Privacy Concerns in Quantum-Inspired Soft Computing
9.8 Privacy Risks in Data Processing
9.9 Quantum-Related Privacy Issues
9.10 Data Anonymization and Protection Mechanisms
9.11 Current Security Models for Quantum-Inspired Soft Computing
9.12 Security Models and Protocols
9.13 Cryptographic Techniques for Quantum-Inspired Systems
9.14 Comparative Analysis of Existing Models
9.15 Privacy-Preserving Techniques in Intelligent Data Processing
9.15.1 Differential Privacy in Quantum-Inspired Soft Computing
9.15.2 Homomorphic Encryption and Its Role
9.15.3 Secure Multi-Party Computation
9.16 Case Studies of Security and Privacy in Real-Life Applications
9.16.1 Quantum-Inspired Systems in Healthcare
9.16.2 Finance and Security Implications
9.16.3 IoT and Smart City Applications
9.17 Future Directions and Emerging Trends
9.17.1 Advances in Quantum Cryptography
9.17.2 Potential Threats from Quantum Computing to Classical Security Models
9.17.3 Integration of AI for Enhanced Security and Privacy
9.18 Conclusion
References
10. Applications of Quantum-Inspired Soft Computing for Intelligent Data Processing in Real-Life Scenarios
Priyanka Suyal, Kamal Kumar Gola, Camellia Chakraborty, Rohit Kanauzia, Mohit Suyal and Mridula
10.1 Healthcare and Medical Diagnosis
10.1.1 Disease Prediction and Diagnosis
10.2 Financial Services
10.2.1 Algorithmic Trading
10.2.2 Pattern Recognition
10.2.3 Risk Management
10.2.4 Portfolio Optimization
10.3 Supply Chain and Logistics
10.3.1 Route Optimization
10.3.2 Inventory Management
10.4 Cybersecurity
10.4.1 Threat Detection
10.4.2 Cryptography
10.5 Energy Management
10.5.1 Smart Grids
10.5.2 Renewable Energy Forecasting
10.6 Environmental Monitoring
10.6.1 Climate Modeling
10.6.2 Pollution Control
10.7 Transportation
10.8 Traffic Management
10.9 Autonomous Vehicles
10.10 Telecommunications
10.10.1 Network Optimization
10.10.2 Data Compression
10.11 Manufacturing
10.11.1 Process Optimization
10.11.2 Predictive Maintenance
10.12 Retail and E-Commerce
10.13 Recommendation Systems
10.14 Customer Behavior Analysis
10.15 Smart Cities
10.16 Urban Planning
10.17 Public Safety
10.18 Agriculture
10.18.1 Crop Yield Prediction
10.18.2 Pest and Disease Control
10.19 Conclusion
References
11. Challenges and Future Directions for Quantum-Inspired Soft Computing
Ishu Chaudhary, Ankesh Kumar and KrashnKant Gupta
11.1 Introduction
11.2 Limitations of Intelligent Data Processing in Quantum-Inspired Soft Computing
11.2.1 Scalability Challenges in Quantum-Inspired Computing Environment
11.2.2 Quantum Information Leakage and Entanglement Loss during Data Handling
11.2.3 Quantum Entanglement Preservation in Soft Computing
11.2.4 Quantum Error Correction and Fault Tolerance in Complex Computations
11.3 Open Challenges to Intelligent Data Processing in Quantum-Inspired Computing
11.3.1 Interoperability Challenges Between Classical and Quantum Systems
11.3.1.1 Fundamental Differences between Classical vs Quantum Computation
11.3.1.2 Theoretical Challenges
11.3.1.3 Practical Challenges
11.3.1.4 Current Solutions and Approaches
11.3.1.5 Future Directions
11.3.2 Hybrid Architectures of Modern Cloud Applications Used for Intelligent Data Processing
11.4 Achieving Low Latency in Quantum-Inspired Soft Models while Working with Real-Time Applications
11.5 Cross-Disciplinary Challenges and Opportunities in Quantum-Inspired Soft Computing
11.6 Future Trends and Emerging Technologies in Quantum-Inspired Soft Computing for Intelligent Data Processing
11.6.1 Evolutionary Quantum Machine Learning Models Using Neural Networks and Deep Learning
11.6.1.1 Quantum-Inspired Computing on Edge Devices with Cloud Computing Integration
11.6.1.2 Quantum-Inspired Genetic Algorithms and Swarm Intelligence for Optimization
11.6.1.3 Security-Enhanced Soft Quantum Models for Quantum Key Distribution and Quantum Cryptography
11.6.1.4 Quantum-Inspired Soft Computing for Sustainable Technologies
11.7 Conclusion
References
Index

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