Quantum-Inspired Approaches for Intelligent Data Processing

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.

• Preface xvii1 Introduction to Soft Computing for Intelligent Data Processing 1Tiyas Sarkar, Manik Rakhra and Baljinder Kaur1.1 Introduction 21.2 Literature Review 61.3 Proposed Methodology 81.4 Results and Discussions 131.5 Conclusion 162 Foundations of Quantum Computing: Overview, Foundation and Scope 21Mohit Chandra Saxena and Abhishek Tamrakar2.1 Overview of Quantum Computing 212.2 Quantum Algorithms: Unleashing Quantum Power for Data Processing 272.3 Advantages and Challenges of Quantum Computing 312.4 Quantum Computing Technologies: Building the Quantum Toolbox 352.5 Scope of Quantum Computing: Security, Optimization, and Machine Learning 402.6 The Future of Quantum Computing 473 Integration of Quantum Computing with Soft Computing for Data Processing 51Vanya Arun, Kapil Deo Bodha, Ankita Awasthi and Munish Sabharwal3.1 Introduction to Quantum Computing and Soft Computing 523.2 Interrelation Between Quantum Computing and Soft Computing 563.3 Mathematical Analysis of the Interrelation between Quantum Computing and Soft Computing 573.4 Quantum-Inspired Algorithms for Enhanced Data Processing 603.5 Trade-Offs Between Computational Error and Processing Speed 643.6 Data Mining, Control Systems, and Pattern Recognition 653.7 Challenges and Limitations of Classical Soft Computing in Large Datasets 673.8 Quantum Computing Platforms for Soft Computing Integration 693.9 Case Studies of Quantum and Soft Computing Integration in Industry 713.10 Introduction to Quantum Cryptography and Data Privacy 733.11 Quantum Algorithms for Privacy Preservation in Computation and Communication 743.12 Future Prospects and Emerging Research Gaps 763.13 Security and Privacy Challenges in Quantum-Enhanced Soft Computing 783.14 Potential for Quantum-Inspired Tools in Artificial Intelligence and Big Data Analytics 793.15 Impact of Quantum and Soft Computing Integration on Data Processing 803.16 Outlook on Future Applications in AI, Optimization, and Big Data 824 Quantum-Soft Fusion: Transforming the Future of Data Handling 89Sandeep Kumar, Jagjit Singh Dhatterwal and Kuldeep Singh Kaswan4.1 Introduction 904.2 Literature Work 914.3 Proposed Work 924.4 Results 1034.5 Conclusion and Future Scope 1055 Quantum-Inspired Soft Computing for Intelligent IoT Big Data Processing 109Firoz Khan, Amutha Prabakar Muniyandi and Balamurugan Balusamy5.1 Introduction to Quantum-Inspired Soft Computing and IoT Big Data 1105.2 Quantum-Inspired Genetic Algorithms (QIGAs) 1115.3 Quantum-Inspired Particle Swarm Optimization (QIPSO) Algorithm 1155.4 Quantum Annealing Algorithm 1175.5 Quantum-Inspired Artificial Neural Networks (QIA-NN) 1195.6 Performance Evaluation of Quantum Inspired Soft Computing Techniques 1225.7 Role of QI Soft Computing Techniques for IoT Big Data Processing 1266 Quantum-Inspired Optimization Techniques for IoT-Driven Big Data Analysis 129Firoz Khan, Amutha Prabakar Muniyandi and Balamurugan Balusamy6.1 Overview of Internet of Things (IoT) and Big Data 1306.2 Challenges in Handling Big Data in IoT 1306.3 The Role of Optimization in IoT Data Analysis 1316.4 Quantum-Inspired Optimization Techniques 1326.5 Quantum-Inspired Optimization Algorithms for IoT 1336.6 Performance Evaluation of Quantum-Inspired Optimization Techniques 1406.7 Quantum-Inspired Optimization Techniques for Big Data Analysis 1446.8 Summary 1467 Quantum-Inspired Soft Computing for Intelligent Data Processing in Real-Life Scenarios 149Kuldeep Singh Kaswan, Jagjit Singh Dhatterwal, Kiran Malik, Santar Pal Singh and S. Viveka7.1 Introduction 1507.2 Fundamentals of Quantum-Inspired Soft Computing 1517.3 Key Concepts: Superposition, Entanglement, and Interference 1527.4 Soft Computing Techniques: Fuzzy Logic, Genetic Algorithms, and Neural Networks 1587.5 Quantum-Inspired Algorithms for Intelligent Data Processing 1587.6 Quantum-Inspired Neural Networks 1597.7 Hybrid Quantum Approaches in Soft Computing 1607.8 Applications of Quantum-Inspired Soft Computing in Real-Life Scenarios 1627.9 IoT and Edge Computing in Industry 4.0 1637.10 Energy Management in Smart Grids 1647.11 Fraud Detection in E-Commerce 1647.12 Challenges and Limitations of Quantum-Inspired Soft Computing 1647.13 Ethical and Social Implications in Data Handling 1667.14 Future Trends in Quantum-Inspired Soft Computing 1677.15 Case Studies and Practical Implementations 1687.16 Conclusion 1698 Market Trends in Quantum-Inspired Soft Computing for Intelligent Data Processing 173Shubh Kapoor and Vikas Garg8.1 Introduction 1748.2 Understanding Quantum-Inspired Soft Computing regarding Quantum-Inspired Soft Computing 1748.3 Current Market Landscape 1778.4 Hardware Developments 1848.5 Algorithmic Innovations 1858.6 Interfaces with AI and Machine Learning 1878.7 Computational Constraints 1898.8 Standardization Issues 1908.9 Skill Gaps 1918.10 New Areas of Use in QISC 1938.11 Partnership and Ecosystem Creation 1958.12 Towards Quantum Computing: The Hybrid Future 1978.13 Conclusion 1989 Security and Privacy Aspects in Quantum-Inspired Soft Computing for Intelligent Data Processing 201Kuldeep Singh Kaswan, Jagjit Singh Dhatterwal, Kiran Malik, Naresh Kumar, S. S. Sridhar and S. Babeetha9.1 Introduction 2029.2 Foundations of Quantum-Inspired Soft Computing 2039.3 Security Challenges in Quantum-Inspired Soft Computing 2049.4 Vulnerabilities in Quantum-Inspired Algorithms 2059.5 Security Threats in Intelligent Data Processing 2059.6 Case Studies of Security Breaches 2069.7 Privacy Concerns in Quantum-Inspired Soft Computing 2069.8 Privacy Risks in Data Processing 2079.9 Quantum-Related Privacy Issues 2079.10 Data Anonymization and Protection Mechanisms 2109.11 Current Security Models for Quantum-Inspired Soft Computing 2109.12 Security Models and Protocols 2109.13 Cryptographic Techniques for Quantum-Inspired Systems 2119.14 Comparative Analysis of Existing Models 2139.15 Privacy-Preserving Techniques in Intelligent Data Processing 2149.16 Case Studies of Security and Privacy in Real-Life Applications 2169.17 Future Directions and Emerging Trends 2179.18 Conclusion 21910 Applications of Quantum-Inspired Soft Computing for Intelligent Data Processing in Real-Life Scenarios 223Priyanka Suyal, Kamal Kumar Gola, Camellia Chakraborty, Rohit Kanauzia, Mohit Suyal and Mridula10.1 Healthcare and Medical Diagnosis 22410.2 Financial Services 22610.3 Supply Chain and Logistics 22910.4 Cybersecurity 23110.5 Energy Management 23410.6 Environmental Monitoring 23610.7 Transportation 23910.8 Traffic Management 24010.9 Autonomous Vehicles 24010.10 Telecommunications 24110.11 Manufacturing 24410.12 Retail and E-Commerce 24610.13 Recommendation Systems 24810.14 Customer Behavior Analysis 24910.15 Smart Cities 25010.16 Urban Planning 25010.17 Public Safety 25110.18 Agriculture 25210.19 Conclusion 25511 Exploring the Key Challenges and Future Directions for Quantum-Inspired Soft Computing 259Ishu Chaudhary, Ankesh Kumar and KrashnKant Gupta11.1 Introduction 26011.2 Limitations of Intelligent Data Processing in Quantum-Inspired Soft Computing 26111.3 Open Challenges to Intelligent Data Processing in Quantum-Inspired Computing 26611.4 Achieving Low Latency in Quantum-Inspired Soft Models while Working with Real-Time Applications 27311.5 Cross-Disciplinary Challenges and Opportunities in Quantum-Inspired Soft Computing 27611.6 Future Trends and Emerging Technologies in Quantum-Inspired Soft Computing for Intelligent Data Processing 27911.7 Conclusion 282References 282Bibliography 284Index 285