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Submit a ProposalQuantum Machine Learning
 | Artificial Intelligence for Smart Internet of Things Applications Edited by R. Bala Krishnan, N. Rajesh Kumar, Subramaniyaswamy V. Copyright: 2026 | Expected Pub Date:12/30/2025 ISBN: 9781394347049 | Hardcover | 626 pages |
One Line Description
Revolutionize your IoT infrastructure with this guide to mastering quantum-enhanced machine learning algorithms and theoretical frameworks that are shattering the boundaries of classical computing to deliver unprecedented network performance and security.
Audience
Academics, researchers, AI and IoT professionals, data scientists, industry leaders, tech enthusiasts, and policymakers looking to the future of the smart internet of Things.
Academics, researchers, AI and IoT professionals, data scientists, industry leaders, tech enthusiasts, and policymakers looking to the future of the smart internet of Things.
Description
In a world increasingly reliant on interconnected devices and data-driven insights, the limitations of classical computing become ever more apparent. The convergence of quantum computing, machine learning, and the Internet of Things (IoT) heralds a new era of technological advancement, one where the boundaries of computational possibility are continually redefined. This book offers an in-depth examination of how quantum algorithms are utilized to improve the performance, security, and efficiency of IoT devices and networks. It connects theoretical concepts with practical applications, providing a comprehensive look at fundamental principles and advanced techniques in this rapidly growing field. Using case studies and real-world insights, this book gives readers the latest developments in quantum machine learning, artificial intelligence, and the smart Internet of Things, and their potential to create an accessible pathway to the future.
Readers will find the volume:
• Demonstrates how to seamlessly integrate quantum computing and machine learning for next-gen IoT solutions;
• Explores the emerging field of quantum machine learning and its various applications for the AI-driven Internet of Things;
• Provides real-world examples and case studies demonstrating the power of quantum machine learning in smart IoT environments;
• Comprehensively covers a wide range of topics, from fundamental concepts to advanced algorithms.
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In a world increasingly reliant on interconnected devices and data-driven insights, the limitations of classical computing become ever more apparent. The convergence of quantum computing, machine learning, and the Internet of Things (IoT) heralds a new era of technological advancement, one where the boundaries of computational possibility are continually redefined. This book offers an in-depth examination of how quantum algorithms are utilized to improve the performance, security, and efficiency of IoT devices and networks. It connects theoretical concepts with practical applications, providing a comprehensive look at fundamental principles and advanced techniques in this rapidly growing field. Using case studies and real-world insights, this book gives readers the latest developments in quantum machine learning, artificial intelligence, and the smart Internet of Things, and their potential to create an accessible pathway to the future.
Readers will find the volume:
• Demonstrates how to seamlessly integrate quantum computing and machine learning for next-gen IoT solutions;
• Explores the emerging field of quantum machine learning and its various applications for the AI-driven Internet of Things;
• Provides real-world examples and case studies demonstrating the power of quantum machine learning in smart IoT environments;
• Comprehensively covers a wide range of topics, from fundamental concepts to advanced algorithms.
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Author / Editor Details
R. Bala Krishnan, PhD is an Assistant Professor in the Department of Computer Science and Engineering at the Srinivasa Ramanujan Centre at SASTRA University, Kumbakonam, India, with more than 15 years of experience. He has published more than 50 research papers in international journals and his interests include quantum computing, machine learning, artificial intelligence, intrusion detection and prevention systems.
R. Bala Krishnan, PhD is an Assistant Professor in the Department of Computer Science and Engineering at the Srinivasa Ramanujan Centre at SASTRA University, Kumbakonam, India, with more than 15 years of experience. He has published more than 50 research papers in international journals and his interests include quantum computing, machine learning, artificial intelligence, intrusion detection and prevention systems.
N. Rajesh Kumar, PhD is an Assistant Professor in the Department of Computer Science and Engineering at the Srinivasa Ramanujan Centre at SASTRA University, Kumbakonam, India. He has published more than 30 research articles in journals and conferences of repute. His research interests include information hiding, image processing, and visual cryptography.
V. Subramaniyaswamy, PhD is a Professor in the School of Computer Science and Engineering at the Vellore Institute of Technology. Vellore. Tamil Nadu, India. He has internationally published more than 200 articles and book chapters. His technical competencies lie in recommender systems, blockchain networks, artificial intelligence, machine learning, and big data analytics.
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Table of Contents
Preface
Part I: Building Effective Artificial Intelligence Based Machine Learning Systems & IoT Applications
1. Essentials of Data Analytics for Smart IoT
J.E. Judith, C.R. Jothy, C. Dhayananth Jegan and A.J. Anju
1.1 Introduction
1.2 Analytics Techniques for IoT Data
1.3 IoT Data Storage and Management
1.4 IoT Data Analytics—Classification Based on Technological Infrastructure
1.5 Smart IoT Architecture
1.6 Data Analysis and Visualization in IoT
1.7 Data Analytics for Smart IoT Applications
1.8 Real-World Examples
1.9 Businesses Benefit from the Smart Internet of Things Analytics
1.10 Challenges, Particularly Regarding Data Privacy, Security, and Scalability in Smart IoT
1.11 Implementation Process of IoT Analytics
1.12 Conclusion
References
2. Profiling Crop Water Stress Using Modular Neural Network Classifier for an IoT-Enabled Crop Irrigation System
Jeyapandian Munisamy and Durga Karthik
2.1 Introduction
2.2 Related Works
2.3 Materials and Methods
2.3.1 Dataset Preparation
2.3.2 Building the Model
2.4 Results and Discussions
2.5 Conclusion
References
3. Integrating AI and Machine Learning (ML) with the Internet of Things (IoT)
Swetha Margaret T. A., Renuka Devi D. and Diana Judith I.
3.1 Introduction
3.2 Literature Review: Integration of AI, ML, and IoT
3.3 Advancements in AI, ML, and IoT Integration
3.4 Future Challenges, and Directions
3.5 Conclusion
References
4. Integrating AI and Machine Learning to Enhance Data Security in Intelligent IoT Systems
M. T. Vasumathi, Manju Sadasivan, M. Kamarasan and G. Manikandan
4.1 Introduction to Quantum Computing and IoT
4.2 Data Security Challenges in IoT Ecosystems
4.3 Overview of AI and Machine Learning in Cybersecurity
4.4 AI-Driven Solutions to Enhance IoT Data Security
4.5 Machine Learning Techniques for IoT Security
4.6 Security through Blockchain and IoT Integration
4.7 Ethical and Regulatory Considerations in AI-Driven IoT Security
4.8 Future Trends in AI, Machine Learning, and IoT Security
4.9 Conclusion
Bibliography
5. Quantum Salp Swarm Algorithm for Optimizing Task Offloading in Edge Computing-Based IoT Systems
Sasikumar A., Logesh Ravi, Malathi Devarajan, Selvalakshmi A., Harishankar K. Nair, Ali Wagdy Mohamed and Subramaniyaswamy V.
5.1 Introduction
5.2 Background
5.3 Related Works
5.4 The Proposed Optimization Model Using Quantum-Inspired SSA
5.5 Computational Model Analysis
5.6 Conclusion
References
6. Object Detection and Distance Measurement Using ToF Sensor
Venkatesan R., Jeyapandian M. and Durga Karthik
6.1 Introduction
6.2 Literature Survey
6.3 System Architecture
6.4 Materials and Methodology
6.5 Implementation
6.6 Performance Analysis
6.7 Conclusion
6.8 Future Enhancement
References
7. Development of Sensor-Based Smart Stick with Alert System for Visually Impaired Individuals Using Machine Learning Technique
Sivaramapriya Karuppaiyan, Mathivathani Ganesan, Roja Karuppaiyan and Bhuvaneswari Swaminathan
7.1 Introduction
7.2 Related Works
7.3 Proposed Method
7.4 Results and Discussions
7.5 Conclusion
References
8. Smarter IoT: Integrating AI and Machine Learning for Sustainable Systems and Intelligent Automation
K. Megala, K. Tulip Raaj, R. Bala Krishnan, N. Rajesh Kumar and G. Manikandan
8.1 Introduction
8.2 Related Works
8.3 Integrations of AI/ML in Smarter IoT
8.4 The Impact of Smarter IoT on the World
8.5 National Benefits of Smarter IoT in India
8.6 The Increasing Prospects of Smarter IoT: International and Domestic Views
8.7 India’s National Potential for Smarter IoT
8.8 Conclusion
Bibliography
Part II: Quantum Computing Meets Machine Learning: Algorithms, Applications, and Security
9. Quantum Computing Meets Machine Learning: A New Frontier
Renuka Devi D., Diana Judith I. and Swetha Margaret T.A.
9.1 Introduction
9.2 Data Encoding and Quantum State Preparation
9.3 Quantum Supervised Learning
9.4 Challenges and Future Directions
9.5 Quantum Reinforcement Learning (QRL)
9.6 Quantum Annealing and Real-World Applications
9.7 Advanced Algorithms in QML
9.8 Quantum Kernels and Distance Metrics
9.9 Scalability Limits of QNNS and QRL
9.10 QML Real Time Applications in Medical Diagnosis and Finance
9.11 Conclusion
References
10. An Introduction to Quantum Machine Learning Algorithms and Its Applications
Jeevan George and Asha Sebastian
10.1 Introduction
10.2 QML Algorithms
10.3 Quantum Support Vector Machines (QSVM)
10.4 Quantum k-Nearest Neighbor (QKNN) Algorithm
10.5 Quantum Principal Component Analysis (QPCA) Algorithm
10.6 Variational Quantum Classifier (VQC)
10.7 Conclusion
References
11. Quantum Machine Learning-Based Personalized Transportation Recommender System
D. Manju, Logesh Ravi, Ali Wagdy Mohamed and Subramaniyaswamy V.
11.1 Introduction
11.2 Literature Review
11.3 Challenges in Large-Scale Recommendations
11.4 Preliminaries and Explanations
11.5 Methodology
11.6 Experimental Setup
11.7 Scenarios for Evaluation
11.8 Results and Discussions
11.9 Summary of Achievements
11.10 Conclusion and Future Work
References
12. Data-Driven Decision Making for Sustainable Transportation, Quantum Machine Learning, and Collaborative Filtering
D. Manju, Logesh Ravi, Ali Wagdy Mohamed and Subramaniyaswamy V.
12.1 Introduction
12.2 Literature Review
12.3 Fundamental Concepts
12.4 Methodology
12.5 Analysis Model
12.6 Summary of Achievements
12.7 Conclusion
Bibliography
13. Quantum Machine Learning-Based Framework for Predictive Maintenance in Smart Manufacturing Industries
B.S. Kiruthika Devi, Lekshmi S. Raveendran, Minu Susan Jacob, Balasubramanian Prabhu Kavin and Priyan Malarvizhi Kumar
13.1 Introduction
13.2 Literature Survey
13.3 Quantum Machine Learning Framework for Predictive Maintenance
13.4 Advantages and Limitations of the Proposed Methodology
13.5 Conclusions and Future Research Directions
Bibliography
14. Quantum Machine Learning-Based Smart IoT Model for Precision Agriculture
D. Anu Disney, V. Akilandeswari, G. Suseela, Balasubramanian Prabhu Kavin and Priyan Malarvizhi Kumar
14.1 Introduction
14.2 Literature Review
14.3 A Comprehensive Quantum Machine Learning-Based Smart IoT Model
14.4 Advantages and Challenges of the Framework
14.5 Conclusions and Future Works
Bibliography
15. A Comprehensive Survey on Quantum Learning and Quantum Machine Learning: Dissimilarities, Revolutions, and Upcoming Directions
Abiramasundari S. and Umamaheswari P.
15.1 Introduction
15.2 Need for Quantum Computing
15.3 Literature Survey and Case Studies
15.4 Comparison of Methods/Algorithms
15.5 Conclusion
References
16. Quantum Machine Learning Model for Finance: A Deep Portfolio Investment System
Sasikumar A., Logesh Ravi, Malathi Devarajan, Selvalakshmi A., Harishankar K. Nair, Ali Wagdy Mohamed and Subramaniyaswamy V.
16.1 Introduction
16.2 Toward a Quantum-Enhanced Deep Portfolio Investment System
16.3 Related Work
16.4 The Proposed Quantum Machine Learning Model for Finance
16.5 The Proposed Investment System Implementation
16.6 Experiment Settings and Analysis
16.7 Conclusion
References
17. Quantum Computing Applications for Internet of Things
Sooraj T.R. and B.K. Tripathy
17.1 Introduction
17.2 Network Optimization in IoT Using Quantum Computing
17.3 Faster Computation at IoT Endpoints/IoT Nodes
17.4 Securing IoT Using QC
17.5 Quantum Sensors for IoT
17.6 Quantum Digital Marketing
17.7 Quantum-Secured Smart Locks
17.8 Challenges and Future Research
17.9 Conclusion
References
18. Entangling Intelligence: Bridging Quantum and Machine Learning
Sharan G., R. Bala Krishnan, Karthikeyan B. and Manikandan G.
18.1 Introduction
18.2 Literature Survey
18.3 Basics of Quantum Computing
18.4 Overview of Machine Learning
18.5 Intersection of Quantum Computing and Machine Learning
18.6 Quantum Algorithms for Machine Learning
18.7 Tools and Frameworks for Quantum Machine Learning
18.8 Conclusion
References
19. Delving into the Basics of QC: From Qubits to Quantum Gates
Anishin Raj M.M., Varghese S. Chooralil, Sebastian Terance, Nikesh P.L. and Simina M.P.
19.1 Introduction
19.2 Related Works
19.3 Overview of Classical and Quantum Computing
19.4 Architecture of Quantum Computing System
19.5 Applications of Quantum Computing
19.6 Conclusion
References
Part III: Security and Advanced Concepts in Quantum and IoT Systems
20. Building Effective AI-Based Machine Learning Systems: A Comprehensive Guide to Design Principles
Anishin Raj M.M., Sebastian Terance, Rajasekhar Reddy, Nikesh P.L. and Sabitha Raju
20.1 Introduction
20.2 Current Challenges in Building Effective AI/ML Systems
20.3 Exploring Technological Advances
20.4 Potential Benefits in Building Effective AI-Based Machine Learning Systems
20.5 Limitations on Building Effective AI-Based Machine Learning Systems
20.6 Driving India’s Prospects: Building Effective AI-Based ML Models
20.7 An International Phenomenon: Building Effective AI-Based ML Models
20.8 Literature Survey
20.9 Global and National Impact of Building Effective AI-Based Machine Learning Systems
20.10 Applications for the Effective Utilization of AI-Based ML Systems
20.11 Conclusion
References
21. A Systematic Introduction to Quantum Computing and Quantum Machine Learning for IoT Applications
Mathew Vincent, Parvathy Gopakumar, Asha Sebastian and Rubell Marion Lincy G.
21.1 Introduction
21.2 Foundations of Quantum Machine Learning
21.3 Variational Quantum Eigensolver and Quantum Approximate Optimization Algorithm
21.4 The Scope of Quantum Machine Learning
21.5 Quantum Computing for IoT
References
22. Enhancing Security in the Smart IoT Systems Using Post‑Quantum Cryptographic Block Cipher
Sasikumar A., Logesh Ravi, Malathi Devarajan, Selvalakshmi A., Harishankar K. Nair, Ali Wagdy Mohamed and Subramaniyaswamy V.
22.1 Introduction
22.2 Background
22.3 Related Work
22.4 Proposed Model of Post-Quantum Cryptography for IoT Applications
22.5 Experimental Setup and PQC Performance Investigation
22.6 Conclusion
References
23. A Blockchain-Integrated Quantum Model-Based Fusion for Data Security in Smart IoT
Sasikumar A., Logesh Ravi, Malathi Devarajan, Selvalakshmi A., Harishankar K. Nair, Ali Wagdy Mohamed and Subramaniyaswamy V.
23.1 Introduction
23.2 Background
23.3 Related Work
23.4 Blockchain-Integrated QCNN for Data Security
23.5 The QCNN for IoT Data Fusion
23.6 Results and Discussion
23.7 Conclusion
References
24. Reliable AI in Smart IoT through Quantum Error Correction and Fault-Tolerant Computing
Sameeksha Saraf, Arka De and B. K. Tripathy
24.1 Introduction
24.2 Fault-Tolerant Quantum Computing
24.3 AI in Smart IoT: Challenges and Opportunities
24.4 Integration of QEC and Fault Tolerance in IoT
24.5 Applications in AI-Driven IoT
24.6 Future Scope
24.7 Conclusion
References
25. Cyber Security in Quantum Era: Challenges, Solutions, and Future Directions: A Review
Karthikeyan Vaiapury, Latha Parameswaran, Sridharan Sankaran and Sweety Hansuwa
25.1 Introduction
25.2 Sector-Specific Quantum Cryptography Implications
25.3 Cloud-Computing-Specific Implications
25.4 Understanding Quantum Computing and Cryptographic Vulnerabilities
25.5 Quantum Cryptography for Emerging Technologies: IoT
25.6 Quantum Image Encryption
25.7 Transitioning to Quantum-Resistant Cryptography
25.8 Conclusions and Future Directions in Quantum Cryptography Research
References
Index
Back to Top
Preface
Part I: Building Effective Artificial Intelligence Based Machine Learning Systems & IoT Applications
1. Essentials of Data Analytics for Smart IoT
J.E. Judith, C.R. Jothy, C. Dhayananth Jegan and A.J. Anju
1.1 Introduction
1.2 Analytics Techniques for IoT Data
1.3 IoT Data Storage and Management
1.4 IoT Data Analytics—Classification Based on Technological Infrastructure
1.5 Smart IoT Architecture
1.6 Data Analysis and Visualization in IoT
1.7 Data Analytics for Smart IoT Applications
1.8 Real-World Examples
1.9 Businesses Benefit from the Smart Internet of Things Analytics
1.10 Challenges, Particularly Regarding Data Privacy, Security, and Scalability in Smart IoT
1.11 Implementation Process of IoT Analytics
1.12 Conclusion
References
2. Profiling Crop Water Stress Using Modular Neural Network Classifier for an IoT-Enabled Crop Irrigation System
Jeyapandian Munisamy and Durga Karthik
2.1 Introduction
2.2 Related Works
2.3 Materials and Methods
2.3.1 Dataset Preparation
2.3.2 Building the Model
2.4 Results and Discussions
2.5 Conclusion
References
3. Integrating AI and Machine Learning (ML) with the Internet of Things (IoT)
Swetha Margaret T. A., Renuka Devi D. and Diana Judith I.
3.1 Introduction
3.2 Literature Review: Integration of AI, ML, and IoT
3.3 Advancements in AI, ML, and IoT Integration
3.4 Future Challenges, and Directions
3.5 Conclusion
References
4. Integrating AI and Machine Learning to Enhance Data Security in Intelligent IoT Systems
M. T. Vasumathi, Manju Sadasivan, M. Kamarasan and G. Manikandan
4.1 Introduction to Quantum Computing and IoT
4.2 Data Security Challenges in IoT Ecosystems
4.3 Overview of AI and Machine Learning in Cybersecurity
4.4 AI-Driven Solutions to Enhance IoT Data Security
4.5 Machine Learning Techniques for IoT Security
4.6 Security through Blockchain and IoT Integration
4.7 Ethical and Regulatory Considerations in AI-Driven IoT Security
4.8 Future Trends in AI, Machine Learning, and IoT Security
4.9 Conclusion
Bibliography
5. Quantum Salp Swarm Algorithm for Optimizing Task Offloading in Edge Computing-Based IoT Systems
Sasikumar A., Logesh Ravi, Malathi Devarajan, Selvalakshmi A., Harishankar K. Nair, Ali Wagdy Mohamed and Subramaniyaswamy V.
5.1 Introduction
5.2 Background
5.3 Related Works
5.4 The Proposed Optimization Model Using Quantum-Inspired SSA
5.5 Computational Model Analysis
5.6 Conclusion
References
6. Object Detection and Distance Measurement Using ToF Sensor
Venkatesan R., Jeyapandian M. and Durga Karthik
6.1 Introduction
6.2 Literature Survey
6.3 System Architecture
6.4 Materials and Methodology
6.5 Implementation
6.6 Performance Analysis
6.7 Conclusion
6.8 Future Enhancement
References
7. Development of Sensor-Based Smart Stick with Alert System for Visually Impaired Individuals Using Machine Learning Technique
Sivaramapriya Karuppaiyan, Mathivathani Ganesan, Roja Karuppaiyan and Bhuvaneswari Swaminathan
7.1 Introduction
7.2 Related Works
7.3 Proposed Method
7.4 Results and Discussions
7.5 Conclusion
References
8. Smarter IoT: Integrating AI and Machine Learning for Sustainable Systems and Intelligent Automation
K. Megala, K. Tulip Raaj, R. Bala Krishnan, N. Rajesh Kumar and G. Manikandan
8.1 Introduction
8.2 Related Works
8.3 Integrations of AI/ML in Smarter IoT
8.4 The Impact of Smarter IoT on the World
8.5 National Benefits of Smarter IoT in India
8.6 The Increasing Prospects of Smarter IoT: International and Domestic Views
8.7 India’s National Potential for Smarter IoT
8.8 Conclusion
Bibliography
Part II: Quantum Computing Meets Machine Learning: Algorithms, Applications, and Security
9. Quantum Computing Meets Machine Learning: A New Frontier
Renuka Devi D., Diana Judith I. and Swetha Margaret T.A.
9.1 Introduction
9.2 Data Encoding and Quantum State Preparation
9.3 Quantum Supervised Learning
9.4 Challenges and Future Directions
9.5 Quantum Reinforcement Learning (QRL)
9.6 Quantum Annealing and Real-World Applications
9.7 Advanced Algorithms in QML
9.8 Quantum Kernels and Distance Metrics
9.9 Scalability Limits of QNNS and QRL
9.10 QML Real Time Applications in Medical Diagnosis and Finance
9.11 Conclusion
References
10. An Introduction to Quantum Machine Learning Algorithms and Its Applications
Jeevan George and Asha Sebastian
10.1 Introduction
10.2 QML Algorithms
10.3 Quantum Support Vector Machines (QSVM)
10.4 Quantum k-Nearest Neighbor (QKNN) Algorithm
10.5 Quantum Principal Component Analysis (QPCA) Algorithm
10.6 Variational Quantum Classifier (VQC)
10.7 Conclusion
References
11. Quantum Machine Learning-Based Personalized Transportation Recommender System
D. Manju, Logesh Ravi, Ali Wagdy Mohamed and Subramaniyaswamy V.
11.1 Introduction
11.2 Literature Review
11.3 Challenges in Large-Scale Recommendations
11.4 Preliminaries and Explanations
11.5 Methodology
11.6 Experimental Setup
11.7 Scenarios for Evaluation
11.8 Results and Discussions
11.9 Summary of Achievements
11.10 Conclusion and Future Work
References
12. Data-Driven Decision Making for Sustainable Transportation, Quantum Machine Learning, and Collaborative Filtering
D. Manju, Logesh Ravi, Ali Wagdy Mohamed and Subramaniyaswamy V.
12.1 Introduction
12.2 Literature Review
12.3 Fundamental Concepts
12.4 Methodology
12.5 Analysis Model
12.6 Summary of Achievements
12.7 Conclusion
Bibliography
13. Quantum Machine Learning-Based Framework for Predictive Maintenance in Smart Manufacturing Industries
B.S. Kiruthika Devi, Lekshmi S. Raveendran, Minu Susan Jacob, Balasubramanian Prabhu Kavin and Priyan Malarvizhi Kumar
13.1 Introduction
13.2 Literature Survey
13.3 Quantum Machine Learning Framework for Predictive Maintenance
13.4 Advantages and Limitations of the Proposed Methodology
13.5 Conclusions and Future Research Directions
Bibliography
14. Quantum Machine Learning-Based Smart IoT Model for Precision Agriculture
D. Anu Disney, V. Akilandeswari, G. Suseela, Balasubramanian Prabhu Kavin and Priyan Malarvizhi Kumar
14.1 Introduction
14.2 Literature Review
14.3 A Comprehensive Quantum Machine Learning-Based Smart IoT Model
14.4 Advantages and Challenges of the Framework
14.5 Conclusions and Future Works
Bibliography
15. A Comprehensive Survey on Quantum Learning and Quantum Machine Learning: Dissimilarities, Revolutions, and Upcoming Directions
Abiramasundari S. and Umamaheswari P.
15.1 Introduction
15.2 Need for Quantum Computing
15.3 Literature Survey and Case Studies
15.4 Comparison of Methods/Algorithms
15.5 Conclusion
References
16. Quantum Machine Learning Model for Finance: A Deep Portfolio Investment System
Sasikumar A., Logesh Ravi, Malathi Devarajan, Selvalakshmi A., Harishankar K. Nair, Ali Wagdy Mohamed and Subramaniyaswamy V.
16.1 Introduction
16.2 Toward a Quantum-Enhanced Deep Portfolio Investment System
16.3 Related Work
16.4 The Proposed Quantum Machine Learning Model for Finance
16.5 The Proposed Investment System Implementation
16.6 Experiment Settings and Analysis
16.7 Conclusion
References
17. Quantum Computing Applications for Internet of Things
Sooraj T.R. and B.K. Tripathy
17.1 Introduction
17.2 Network Optimization in IoT Using Quantum Computing
17.3 Faster Computation at IoT Endpoints/IoT Nodes
17.4 Securing IoT Using QC
17.5 Quantum Sensors for IoT
17.6 Quantum Digital Marketing
17.7 Quantum-Secured Smart Locks
17.8 Challenges and Future Research
17.9 Conclusion
References
18. Entangling Intelligence: Bridging Quantum and Machine Learning
Sharan G., R. Bala Krishnan, Karthikeyan B. and Manikandan G.
18.1 Introduction
18.2 Literature Survey
18.3 Basics of Quantum Computing
18.4 Overview of Machine Learning
18.5 Intersection of Quantum Computing and Machine Learning
18.6 Quantum Algorithms for Machine Learning
18.7 Tools and Frameworks for Quantum Machine Learning
18.8 Conclusion
References
19. Delving into the Basics of QC: From Qubits to Quantum Gates
Anishin Raj M.M., Varghese S. Chooralil, Sebastian Terance, Nikesh P.L. and Simina M.P.
19.1 Introduction
19.2 Related Works
19.3 Overview of Classical and Quantum Computing
19.4 Architecture of Quantum Computing System
19.5 Applications of Quantum Computing
19.6 Conclusion
References
Part III: Security and Advanced Concepts in Quantum and IoT Systems
20. Building Effective AI-Based Machine Learning Systems: A Comprehensive Guide to Design Principles
Anishin Raj M.M., Sebastian Terance, Rajasekhar Reddy, Nikesh P.L. and Sabitha Raju
20.1 Introduction
20.2 Current Challenges in Building Effective AI/ML Systems
20.3 Exploring Technological Advances
20.4 Potential Benefits in Building Effective AI-Based Machine Learning Systems
20.5 Limitations on Building Effective AI-Based Machine Learning Systems
20.6 Driving India’s Prospects: Building Effective AI-Based ML Models
20.7 An International Phenomenon: Building Effective AI-Based ML Models
20.8 Literature Survey
20.9 Global and National Impact of Building Effective AI-Based Machine Learning Systems
20.10 Applications for the Effective Utilization of AI-Based ML Systems
20.11 Conclusion
References
21. A Systematic Introduction to Quantum Computing and Quantum Machine Learning for IoT Applications
Mathew Vincent, Parvathy Gopakumar, Asha Sebastian and Rubell Marion Lincy G.
21.1 Introduction
21.2 Foundations of Quantum Machine Learning
21.3 Variational Quantum Eigensolver and Quantum Approximate Optimization Algorithm
21.4 The Scope of Quantum Machine Learning
21.5 Quantum Computing for IoT
References
22. Enhancing Security in the Smart IoT Systems Using Post‑Quantum Cryptographic Block Cipher
Sasikumar A., Logesh Ravi, Malathi Devarajan, Selvalakshmi A., Harishankar K. Nair, Ali Wagdy Mohamed and Subramaniyaswamy V.
22.1 Introduction
22.2 Background
22.3 Related Work
22.4 Proposed Model of Post-Quantum Cryptography for IoT Applications
22.5 Experimental Setup and PQC Performance Investigation
22.6 Conclusion
References
23. A Blockchain-Integrated Quantum Model-Based Fusion for Data Security in Smart IoT
Sasikumar A., Logesh Ravi, Malathi Devarajan, Selvalakshmi A., Harishankar K. Nair, Ali Wagdy Mohamed and Subramaniyaswamy V.
23.1 Introduction
23.2 Background
23.3 Related Work
23.4 Blockchain-Integrated QCNN for Data Security
23.5 The QCNN for IoT Data Fusion
23.6 Results and Discussion
23.7 Conclusion
References
24. Reliable AI in Smart IoT through Quantum Error Correction and Fault-Tolerant Computing
Sameeksha Saraf, Arka De and B. K. Tripathy
24.1 Introduction
24.2 Fault-Tolerant Quantum Computing
24.3 AI in Smart IoT: Challenges and Opportunities
24.4 Integration of QEC and Fault Tolerance in IoT
24.5 Applications in AI-Driven IoT
24.6 Future Scope
24.7 Conclusion
References
25. Cyber Security in Quantum Era: Challenges, Solutions, and Future Directions: A Review
Karthikeyan Vaiapury, Latha Parameswaran, Sridharan Sankaran and Sweety Hansuwa
25.1 Introduction
25.2 Sector-Specific Quantum Cryptography Implications
25.3 Cloud-Computing-Specific Implications
25.4 Understanding Quantum Computing and Cryptographic Vulnerabilities
25.5 Quantum Cryptography for Emerging Technologies: IoT
25.6 Quantum Image Encryption
25.7 Transitioning to Quantum-Resistant Cryptography
25.8 Conclusions and Future Directions in Quantum Cryptography Research
References
Index
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