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Quantum Machine Learning for 6G Networks 

Edited by Pallavi Sapkale, Shilpa Mehta and S. Balamurugan
Series: Industry 5.0 Transformation Applications
Copyright: 2025   |   Expected Pub Date:2025/07/30
ISBN: 9781394238088  |  Hardcover  |  
452 pages

One Line Description
Secure your expertise in the next frontier of wireless technology with this essential book, which provides a deep dive into the integration of machine learning and quantum computing to build the necessary infrastructure for 6G communication networks.

Audience
Scientists, industry professionals, researchers, academicians, instructors, and students working in quantum computing and machine learning, especially in the context of advanced wireless communication technology.

Description
Despite the potential benefits of 6G, the technology to enable its realization is not yet available. As a result, the development of technology to solve these challenges must be met before we can start working towards 6G. The primary applications of machine learning within 6G are to create necessary infrastructure advantages as the technology matures. Additionally, 6G communication networks use quantum computing to detect, mitigate, and prevent security vulnerabilities. By integrating machine learning and quantum computing into 5G and 6G technology, intelligent base stations will be able to make decisions for themselves, and mobile devices will be able to create dynamically adaptable clusters based on learned data. This book highlights the role of real-time network learning and the integration of quantum computing, machine learning, and quantum machine learning to enhance service quality. It provides a deep dive into the interplay of these technologies within 6G networks, starting from 5G fundamentals. The book elaborates on how these advanced technologies will underpin 6G’s architecture to meet comprehensive service demands, including those for smart city applications requiring extensive coverage, ultra-low latency, and reliable connectivity. The book details how the synergy between quantum computing, machine learning, and 6G technologies will transform communications, revolutionize markets, and enable groundbreaking applications globally.
Readers will find the volume:
• Explores real-world scenarios for illustrating the integration of quantum computing and machine learning in 6G;
• Covers an extensive range of applications to illustrate the full picture of 6G that implements machine learning and quantum computing approaches;
• Offers expert insights through a comprehensive collection of literature reviews and research articles;
• Introduces the interdisciplinary innovations and potential of 6G across multiple industries.

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Author / Editor Details
Pallavi Sapkale, PhD is an Assistant Professor, Ramrao Adik Institute of Technology, D.Y. Patil University, Navi Mumbai, Maharashtra, India, with more than 17 years of experience. She has published four books, more than 25 research articles in various international journals and conferences, four international patents, and 12 Indian patents. Her research focuses on quantum computing, machine learning, wireless communication, 5G mobility management, and next-generation networks like 6G.

Shilpa Mehta, PhD is a Teaching Assistant at the Auckland University of Technology, New Zealand, with more than five years of teaching experience. She has worked on various interdisciplinary research projects and edited several internationally published books. Her research interests include radio frequency integrated circuits, RF front ends, optimization, Internet of Things, wireless communication, artificial intelligence, healthcare, radars, and smart cities.

S. Balamurugan, PhD is the Director of Albert Einstein Engineering and Research Labs, Coimbatore, Tamilnadu, India. He has published more than 60 books, 300 articles in national and international journals and conferences, and 200 patents. He is also the Vice-Chairman of Renewable Energy Society of India (RESI). He also serves as a research consultant for many companies, startups, and micro-, small, and medium enterprises.

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Table of Contents
Preface
Acknowledgement
Part I: Introduction
1. Introduction to Wireless Communication and Transition from 1G to 6G
Krupali Dhawale, Pranali Bhope, Kunika Dhapodkar and Sejal Kumbhare
1.1 Introduction to Wireless Communication
1.1.1 Definition and Importance of Wireless Communication
Importance of Wireless Communications
1.1.2 Role of Wireless Communication in Connecting People and Devices Globally
1.1.3 Evolution of Wireless Communication Technologies
1.2 Generations of Wireless Communication
1.2.1 1G (Analog Cellular)
1.2.2 2G (Digital Cellular)
1.2.3 3G (Mobile Broadband)
1.2.4 4G (LTE and Beyond)
1.2.5 5G (Next-Generation Connectivity)
1.2.6 Anticipating 6G (Future Evolution)
1.3 1G to 4G: Evolution of Wireless Standards
1.3.1 Overview of 1G to 4G Transitions
1.3.2 Advancements in Digital Modulation and Compression
1.3.3 Shift from Analog to Digital Transmission
1.3.4 Introduction of Data Services and Mobile Internet
1.4 Industry and Research Initiatives for 6G
1.4.1 Involvement of Academia, Industry, and Standardization Bodies
1.4.2 Research Goals and Technological Roadmaps
Conclusion
References
2. The State-of-the-Art and Future Visioning 6G Wireless Network
Payal Bansal
2.1 Introduction
2.1.1 Heterogeneous Wireless Networks
2.1.2 Vertical Handover
2.2 Handover Management in 6G
2.2.1 History of Handover System
2.2.2 Handover Process
2.2.3 Single-Tier Networks with Handover Skipping Process
2.2.3.1 Coverage Probability
2.2.3.2 Handover Cost
2.2.3.3 Average Throughput
2.3 Two-Tier Network Handover Skipping
Bibliography
Part II: Quantum Computing
3. Introduction to Quantum Computing
Shilpa Mehta and Celestine Iwendi
3.1 Introduction
3.1.1 Historical Background
3.1.2 Classical Computing vs Quantum Computing
3.1.3 Why Quantum Computers?
3.1.4 Bits versus Qubits
3.1.5 Quantum Registers
3.1.6 Key Principles of Quantum Computing
3.2 Quantum Gates
3.3 Quantum Algorithms
3.3.1 Fourier Transform–Based Algorithms
3.3.1.1 Overview of Discrete Fourier Transform
3.3.1.2 Quantum Fourier Transform
3.3.2 Amplitude Amplification–Based Algorithms
3.3.2.1 Grover’s Algorithm
3.3.2.2 Quantum Counting
3.3.3 Quantum Walk Based Algorithms
3.3.3.1 Boson Sampling Problem
3.3.3.2 Element Distinctness Problem
3.3.3.3 Triangle Finding Problem
3.3.4 Bounded-Error Quantum Polynomial Time Problems
3.3.4.1 Quantum Simulation
3.3.5 Hybrid Algorithms
3.3.5.1 Quantum Approximate Optimization Algorithm
3.3.5.2 Variational Quantum Eigensolver Algorithm
3.3.5.3 Contracted Quantum Eigensolver Algorithm
3.4 Quantum Hardware and Software
3.4.1 Quantum Hardware
3.4.1.1 Types of Quantum Hardware
3.4.2 Quantum Software
3.5 Applications
3.6 Challenges of Quantum Computing
3.7 Current State-of-the-Art
3.8 Summary and Future Scope
References
4. Quantum-Secured Concealed Identifier for 6G Technology
Pratham Desai and Dipali Kasat
Introduction
4.1 Quantum Mechanical Properties for Security
4.1.1 Entanglement with Bell-State Example
4.1.2 Entanglement for Bipartite System
4.1.3 No Cloning Theorem
4.2 Quantum Key Distribution Technique (QKD)
4.3 BB84 Algorithm
4.4 Concept of Identifiers
4.5 Drawbacks of Classical Algorithms
4.6 Quantum Concealed Identifiers for 6G Technology
4.6.1 QKD Protocol with a Multiple Coding Basis
4.6.2 Parameters and Basic Equipment
4.6.3 Pseudo-Random Number Seed Key Construction Protocol for Security (PRNSKC)
4.7 A Post-Quantum SUCI for 6G
4.7.1 How SUCI is Vulnerable to Quantum Attacks
4.7.2 Post-Quantum Secure SUCI
4.7.3 Selecting the Perfect KEM for KEMSUCI
4.7.4 Understanding the Kyber Algorithm
4.8 Comparison Between the Existing Schemes
Conclusion
Bibliography
5. Quantum Cryptography: Present and Future 6G
Dhananjay Manohar Dakhane, Vaibhav Eknath Narawade and Pallavi Sapkale
5.1 Introduction
5.2 Quantum Cryptography
5.3 Quantum Key Distribution
5.4 Post Quantum Cryptography
5.5 Conclusions
References
6. Network Intelligence with Quantum Computing for 6G
H. Bhoomeeswaran, G. Joshva Raj, J. Mangaiyarkkarasi and J. Shanthalakshmi Revathy
6.1 Introduction
6.2 Quantum Computing
6.3 Spintronic QC
6.4 Literature Survey
6.5 SHSTNO
6.6 Photonic QC
6.7 Conclusion
6.8 Future Scope
References
Part III: Machine Learning
7. Introduction to Machine Learning: Conceptualization, Implementation, and Research Perspective
Snehasis Dey
7.1 Introduction to Machine Learning: Conceptualization Perspective
7.1.1 Basics of Machine Learning
7.1.2 Literature Survey
7.1.3 Problem Statement and Proposed Model
7.1.4 Evolution of Machine Learning
7.1.5 Machine Learning as a Powerful Tool for Future Advancement
7.2 A Dive Into Machine Learning: Implementation Perspective
7.2.1 Correlations and Differences Between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)
7.2.2 Learning Techniques in Machine Learning
7.2.3 Algorithms in Machine Learning
7.3 Recent Trends in Machine Learning: Research Perspective
7.3.1 Machine Learning in Fourth Industry Revolution or Industry 4.0 (4IR)
7.3.2 Machine Learning in Real-World Applications: ML for Everything, for Everywhere and for Everyone
7.3.3 Machine Learning in 5G Wireless Communications and Beyond
7.4 Conclusion
References
8. 6G Wireless Networks: Pioneering with Machine Learning Technologies
Krupali Dhawale, Shraddha Jha, Mishri Gube, Shivraj Guduri and Khwaish Asati
8.1 Introduction
8.2 Introduction to 6G Wireless Networks and Machine Learning
8.2.1 6G Wireless Network and Its Significance
8.2.2 Challenges for 6G Networks
8.2.3 Goals for 6G Networks
8.2.4 Introduction to Machine Learning and Its Relevance in Wireless Networks
8.2.4.1 Importance of Machine Learning in Wireless Networks
8.2.5 Potential Benefits of Integrating Machine Learning in 6G Technology
8.3 Machine Learning Techniques for 6G Wireless Networks
8.3.1 Signal Processing and Optimization
8.3.2 Adaptive Beamforming and Spatial Processing Using Machine Learning
8.3.3 Benefits of Adaptive Rendering and Spatial Processing through Machine Learning
8.3.4 Signal Denoising, Interference Mitigation, and Resource Allocation
8.3.5 Spectrum Management and Allocation
8.4 Driven Network Management and Security
8.4.1 Self-Organizing Networks (SON)
8.4.2 Automatic Network Configuration and Optimization through AI
8.4.2.1 Network Management
8.4.2.2 Network Security
8.4.3 Fault Detection, Self-Healing, and Network Maintenance
8.5 Challenges and Future Directions
8.5.1 Data Privacy and Ethics Issues and Challenges
8.5.2 Future Directions in Data Privacy and Ethical Considerations
8.5.3 Balancing Data Usage and User Privacy in AI-Driven Networks
8.6 Conclusion
References
9. Machine Learning–Based Communication and Network Automation: Advancements, Challenges, and Prospects
J. Shanthalakshmi Revathy and J. Mangaiyarkkarasi
9.1 Introduction
9.2 Advancements in Machine Learning for Communication and Network Automation
9.2.1 Machine Learning Fundamentals
9.2.1.1 Supervised, Unsupervised, and Reinforcement Learning in Network Automation
9.2.2 Applications in Network Automation
9.2.2.1 Predictive Maintenance and Fault Detection
9.2.2.2 Quality of Service (QoS) Optimization
9.2.2.3 Traffic Engineering and Load Balancing
9.2.3 Data Sources and Preprocessing
9.2.3.1 Data Collection Methods in Network Environments
9.2.3.2 Data Preprocessing Techniques
9.2.3.3 Feature Selection and Engineering
9.2.4 Model Training and Deployment
9.3 Challenges in Implementing Machine Learning for Network Automation
9.3.1 Data Quality and Availability
9.3.2 Scalability and Resource Constraints
9.3.3 Interoperability and Standards
9.3.3.1 Need for Standardization
9.3.3.2 Compatibility with Existing Network Infrastructure
9.3.3.3 Vendor-Specific Challenges
9.3.4 Ethical and Regulatory Considerations
9.3.4.1 Bias and Fairness in Machine Learning Algorithms
9.3.4.2 Regulatory Compliance in Network Automation
9.3.4.3 Ethical Implications of Automation in Communication
9.4 Prospects and Future Directions
9.4.1 Emerging Technologies
9.4.2 AI-Driven Autonomous Networks
9.4.2.1 Toward Fully Autonomous Networks
9.4.2.2 Self-Healing and Self-Optimizing Networks
9.4.2.3 Human-Machine Collaboration in Network Management
9.5 Research and Development Trends
9.5.1 Current Research Trends in Machine Learning and Network Automation
9.5.2 Industry Collaborations and Academic Contributions
9.5.3 The Importance of Open-Source Projects
9.6 Conclusion
References
10. Empowering 6G Communication Systems: Harnessing Machine Learning for Advancements in Flexible and 3D-Printed Antennas
Duygu Nazan Gençoğlan and Shilpa Mehta
10.1 Introduction
10.2 Flexible and 3D-Printed Antennas
10.3 Challenges in 6G Antenna Design
10.4 Machine Learning for Antenna Design
10.5 Data-Driven Antenna Optimization
10.6 Topology Optimization with ML
10.7 Material Selection and Optimization
10.8 Simulation and Modeling with ML
10.9 Hardware-Software Co-Design for ML-Aided Antennas
10.10 Experimental Validation and Prototyping
10.11 Conclusion and Future Directions
10.12 Future Directions
References
11. Potential Communication in B5G Networks Through Hybrid Millimeter-Wave Beamforming and Machine Learning: Basics, Challenges, and Future Path
Snehasis Dey
11.1 Introduction
11.2 Literature Survey
11.3 HBF Open Challenges
11.4 Conclusion
Bibliography
12. Device-to-Device Communication in 6G Using Machine Learning
J. Shanthalakshmi Revathy, J. Mangaiyarkkarasi and J. Matcha Rani
12.1 Introduction
12.2 Fundamentals of Device-to-Device Communication
12.3 Evolution from Previous Generations
12.3.1 Early Foundations: Peer-to-Peer and Ad Hoc Networks
12.3.2 Device-to-Device Communications in Cellular Networks
12.3.3 The 5G Era
12.3.4 Enhancement in 6G
12.4 Role of Machine Learning in 6G D2D Communication
12.4.1 Supervised Learning
12.4.2 Unsupervised Learning
12.4.3 Reinforcement Learning
12.4.4 Integration of Machine Learning in 6G Networks
12.5 Applications of Machine Learning in D2D Communication Resource Allocation and Spectrum Management
12.6 Challenges and Solutions
12.7 Case Studies
12.7.1 Smart Cities and Urban IoT Networks
12.7.2 Autonomous Vehicles and Vehicular Communication
12.7.3 Healthcare and Wearable Devices
12.7.4 Augmented Reality (AR) and Immersive Media
12.8 Challenges and Future Scope
12.9 Conclusion
References
Part IV: Quantum Computing and Machine Learning
13. Integrating Quantum Computing and Machine Learning in 6G Networks
Ogobuchi D. Okey, Theodore T. Chiagunye, Henrietta U. Udeani, Ikechukwu Nicholas, Renata L. Rosa and Demóstenes R. Zegarra
13.1 Introduction
13.2 Background Study
13.2.1 Technology Evolutionary Trends Toward 6G Network
13.2.2 Unique Features of 6G Networks
13.2.3 The Principle of Quantum Computing
13.2.4 Machine Learning
13.3 Quantum Machine Learning Algorithms and Implementation Frameworks
13.4 Resource Allocation in QML-Enabled 6G Network
13.5 Security Challenges and Prospects in QML 6G
13.6 Limitations, Benefits, and Future Directions
13.7 Conclusion
References
14. A Quantum Computing Perspective in 6G Networks: The Challenge of Adaptive Network Intelligence
Pallavi Sapkale
14.1 Introduction
14.1.1 Quantum Computing in 6G
14.2 What is Network Intelligence in Quantum Computing?
14.2.1 Methods to Achieve the Network Intelligence in Quantum Computing
14.3 How to Accomplish Network Intelligence
14.4 Quantum Computing Opportunities with 6G
14.5 Challenges and Research Scope in Quantum Computing with 6G
14.5.1 Main Challenges in Quantum Computing with 6G
14.5.2 Research Scope in Quantum Computing
14.6 Conclusion
References
15. Role of QML in 6G Integrated Vehicular Networks
R. Palanivel, Muthulakshmi P., Snehasis Dey, Shilpa Mehta and Pallavi Sapkale
15.1 Introduction
15.2 Literature Survey
15.3 Methodology
15.3.1 Quantum Machine Learning for Traffic Prediction in 6G Networks
15.3.1.1 Environment Setup
15.3.1.2 Quantum Traffic Prediction Model
15.3.1.3 Quantum Circuit Representation
15.3.1.4 Safety Analysis
15.3.1.5 Interpretation
15.3.2 QML for Network Security in Vehicular Communication
15.3.2.1 Environment Setup
15.3.2.2 Quantum Key Distribution (QKD)
15.3.2.3 Node1’s Side (Initialization)
15.3.2.4 Quantum Communication Channel (Simulated)
15.3.2.5 Node2’s Measurement
15.3.2.6 Security and Key Sharing
15.3.2.7 Interpretation
15.3.3 6G Network Slicing and Resource Management by QML
15.3.3.1 Environment Setup
15.3.3.2 Implementation Process
15.3.3.3 Interpretation
15.3.4 Quality-of-Service (QoS) Optimization by QML
15.3.4.1 Environment Setup and Parameters
15.3.4.2 Implementation Process
15.3.4.3 Interpretation
15.4 Results and Discussion
15.5 Conclusion
References
Part V: Applications
16. Smart Irrigation Technique Using IoT Based on 5G
Jyoti B. Deone and Khan Rahat Afreen
16.1 Introduction
16.2 Related Work
16.3 5G Network on Smart Farming
16.3.1 The Following Decade Will See the Development of 5G and Smart Farming
16.4 Proposed Methodology
16.5 Working Modules of the System
16.5.1 Login and Registration Module
16.5.2 Change Number Module
16.5.3 Check Status Module
16.5.4 Start Water Pump Module
16.5.5 Stop Water Pump Module
16.5.6 Force Start Water Pump Module
16.5.7 Auto Stopped Module
16.6 Experimental Result Analysis and Working
16.7 Conclusion
References
17. Modeling and Development of Low-Cost Visible Light Communication System
Mrinmoyee Mukherjee, Kevin Noronha and Ravi Kumar Bandi
17.1 Learning Objectives
17.2 Introduction to VLC
17.3 VLC System Description
17.3.1 Key Parameters - Light-Emitting Diode
17.3.2 Key Parameters - Photodiode
17.3.3 Key Parameters - VLC Channel
17.4 Experimental Implementation of the VLC System
17.4.1 Block Diagram and Technical Specifications
17.4.2 Results and Discussions
17.5 Simulation and Modeling of the VLC System
References
Index

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