Quantum-Inspired Approaches for Intelligent Data Processing
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.
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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Persons
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.
Content
Preface xvii
1 Introduction to Soft Computing for Intelligent Data Processing 1
Tiyas Sarkar, Manik Rakhra and Baljinder Kaur
1.1 Introduction 2
1.2 Literature Review 6
1.3 Proposed Methodology 8
1.4 Results and Discussions 13
1.5 Conclusion 16
2 Foundations of Quantum Computing: Overview, Foundation and Scope 21
Mohit Chandra Saxena and Abhishek Tamrakar
2.1 Overview of Quantum Computing 21
2.2 Quantum Algorithms: Unleashing Quantum Power for Data Processing 27
2.3 Advantages and Challenges of Quantum Computing 31
2.4 Quantum Computing Technologies: Building the Quantum Toolbox 35
2.5 Scope of Quantum Computing: Security, Optimization, and Machine Learning 40
2.6 The Future of Quantum Computing 47
3 Integration of Quantum Computing with Soft Computing for Data Processing 51
Vanya Arun, Kapil Deo Bodha, Ankita Awasthi and Munish Sabharwal
3.1 Introduction to Quantum Computing and Soft Computing 52
3.2 Interrelation Between Quantum Computing and Soft Computing 56
3.3 Mathematical Analysis of the Interrelation between Quantum Computing and Soft Computing 57
3.4 Quantum-Inspired Algorithms for Enhanced Data Processing 60
3.5 Trade-Offs Between Computational Error and Processing Speed 64
3.6 Data Mining, Control Systems, and Pattern Recognition 65
3.7 Challenges and Limitations of Classical Soft Computing in Large Datasets 67
3.8 Quantum Computing Platforms for Soft Computing Integration 69
3.9 Case Studies of Quantum and Soft Computing Integration in Industry 71
3.10 Introduction to Quantum Cryptography and Data Privacy 73
3.11 Quantum Algorithms for Privacy Preservation in Computation and Communication 74
3.12 Future Prospects and Emerging Research Gaps 76
3.13 Security and Privacy Challenges in Quantum-Enhanced Soft Computing 78
3.14 Potential for Quantum-Inspired Tools in Artificial Intelligence and Big Data Analytics 79
3.15 Impact of Quantum and Soft Computing Integration on Data Processing 80
3.16 Outlook on Future Applications in AI, Optimization, and Big Data 82
4 Quantum-Soft Fusion: Transforming the Future of Data Handling 89
Sandeep Kumar, Jagjit Singh Dhatterwal and Kuldeep Singh Kaswan
4.1 Introduction 90
4.2 Literature Work 91
4.3 Proposed Work 92
4.4 Results 103
4.5 Conclusion and Future Scope 105
5 Quantum-Inspired Soft Computing for Intelligent IoT Big Data Processing 109
Firoz Khan, Amutha Prabakar Muniyandi and Balamurugan Balusamy
5.1 Introduction to Quantum-Inspired Soft Computing and IoT Big Data 110
5.2 Quantum-Inspired Genetic Algorithms (QIGAs) 111
5.3 Quantum-Inspired Particle Swarm Optimization (QIPSO) Algorithm 115
5.4 Quantum Annealing Algorithm 117
5.5 Quantum-Inspired Artificial Neural Networks (QIA-NN) 119
5.6 Performance Evaluation of Quantum Inspired Soft Computing Techniques 122
5.7 Role of QI Soft Computing Techniques for IoT Big Data Processing 126
6 Quantum-Inspired Optimization Techniques for IoT-Driven Big Data Analysis 129
Firoz Khan, Amutha Prabakar Muniyandi and Balamurugan Balusamy
6.1 Overview of Internet of Things (IoT) and Big Data 130
6.2 Challenges in Handling Big Data in IoT 130
6.3 The Role of Optimization in IoT Data Analysis 131
6.4 Quantum-Inspired Optimization Techniques 132
6.5 Quantum-Inspired Optimization Algorithms for IoT 133
6.6 Performance Evaluation of Quantum-Inspired Optimization Techniques 140
6.7 Quantum-Inspired Optimization Techniques for Big Data Analysis 144
6.8 Summary 146
7 Quantum-Inspired Soft Computing for Intelligent Data Processing in Real-Life Scenarios 149
Kuldeep Singh Kaswan, Jagjit Singh Dhatterwal, Kiran Malik, Santar Pal Singh and S. Viveka
7.1 Introduction 150
7.2 Fundamentals of Quantum-Inspired Soft Computing 151
7.3 Key Concepts: Superposition, Entanglement, and Interference 152
7.4 Soft Computing Techniques: Fuzzy Logic, Genetic Algorithms, and Neural Networks 158
7.5 Quantum-Inspired Algorithms for Intelligent Data Processing 158
7.6 Quantum-Inspired Neural Networks 159
7.7 Hybrid Quantum Approaches in Soft Computing 160
7.8 Applications of Quantum-Inspired Soft Computing in Real-Life Scenarios 162
7.9 IoT and Edge Computing in Industry 4.0 163
7.10 Energy Management in Smart Grids 164
7.11 Fraud Detection in E-Commerce 164
7.12 Challenges and Limitations of Quantum-Inspired Soft Computing 164
7.13 Ethical and Social Implications in Data Handling 166
7.14 Future Trends in Quantum-Inspired Soft Computing 167
7.15 Case Studies and Practical Implementations 168
7.16 Conclusion 169
8 Market Trends in Quantum-Inspired Soft Computing for Intelligent Data Processing 173
Shubh Kapoor and Vikas Garg
8.1 Introduction 174
8.2 Understanding Quantum-Inspired Soft Computing regarding Quantum-Inspired Soft Computing 174
8.3 Current Market Landscape 177
8.4 Hardware Developments 184
8.5 Algorithmic Innovations 185
8.6 Interfaces with AI and Machine Learning 187
8.7 Computational Constraints 189
8.8 Standardization Issues 190
8.9 Skill Gaps 191
8.10 New Areas of Use in QISC 193
8.11 Partnership and Ecosystem Creation 195
8.12 Towards Quantum Computing: The Hybrid Future 197
8.13 Conclusion 198
9 Security and Privacy Aspects in Quantum-Inspired Soft Computing for Intelligent Data Processing 201
Kuldeep Singh Kaswan, Jagjit Singh Dhatterwal, Kiran Malik, Naresh Kumar, S. S. Sridhar and S. Babeetha
9.1 Introduction 202
9.2 Foundations of Quantum-Inspired Soft Computing 203
9.3 Security Challenges in Quantum-Inspired Soft Computing 204
9.4 Vulnerabilities in Quantum-Inspired Algorithms 205
9.5 Security Threats in Intelligent Data Processing 205
9.6 Case Studies of Security Breaches 206
9.7 Privacy Concerns in Quantum-Inspired Soft Computing 206
9.8 Privacy Risks in Data Processing 207
9.9 Quantum-Related Privacy Issues 207
9.10 Data Anonymization and Protection Mechanisms 210
9.11 Current Security Models for Quantum-Inspired Soft Computing 210
9.12 Security Models and Protocols 210
9.13 Cryptographic Techniques for Quantum-Inspired Systems 211
9.14 Comparative Analysis of Existing Models 213
9.15 Privacy-Preserving Techniques in Intelligent Data Processing 214
9.16 Case Studies of Security and Privacy in Real-Life Applications 216
9.17 Future Directions and Emerging Trends 217
9.18 Conclusion 219
10 Applications of Quantum-Inspired Soft Computing for Intelligent Data Processing in Real-Life Scenarios 223
Priyanka Suyal, Kamal Kumar Gola, Camellia Chakraborty, Rohit Kanauzia, Mohit Suyal and Mridula
10.1 Healthcare and Medical Diagnosis 224
10.2 Financial Services 226
10.3 Supply Chain and Logistics 229
10.4 Cybersecurity 231
10.5 Energy Management 234
10.6 Environmental Monitoring 236
10.7 Transportation 239
10.8 Traffic Management 240
10.9 Autonomous Vehicles 240
10.10 Telecommunications 241
10.11 Manufacturing 244
10.12 Retail and E-Commerce 246
10.13 Recommendation Systems 248
10.14 Customer Behavior Analysis 249
10.15 Smart Cities 250
10.16 Urban Planning 250
10.17 Public Safety 251
10.18 Agriculture 252
10.19 Conclusion 255
11 Exploring the Key Challenges and Future Directions for Quantum-Inspired Soft Computing 259
Ishu Chaudhary, Ankesh Kumar and KrashnKant Gupta
11.1 Introduction 260
11.2 Limitations of Intelligent Data Processing in Quantum-Inspired Soft Computing 261
11.3 Open Challenges to Intelligent Data Processing in Quantum-Inspired Computing 266
11.4 Achieving Low Latency in Quantum-Inspired Soft Models while Working with Real-Time Applications 273
11.5 Cross-Disciplinary Challenges and Opportunities in Quantum-Inspired Soft Computing 276
11.6 Future Trends and Emerging Technologies in Quantum-Inspired Soft Computing for Intelligent Data Processing 279
11.7 Conclusion 282
References 282
Bibliography 284
Index 285
