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Blind Quantum Artificial Intelligence-Powered Cybersecurity

Edited by Abhishek Kumar, Hemant Kumar Saini, Pramod Singh Rathore, and S. Oswalt Manoj
Copyright: 2026 | Expected Pub Date: 2026
ISBN: 9781394386383 | Hardcover |
318 pages
Price: $225 USD

One Line Description
This essential guide provides the definitive, cutting-edge blueprint for engineering quantum-resistant, AI-driven security frameworks that neutralize advanced cyber threats in real time.

Description
The convergence of quantum computing and artificial intelligence is fundamentally changing the cybersecurity landscape, replacing classical defense models with predictive, quantum-resistant frameworks capable of neutralizing advanced threats in real time. This book addresses the critical need for advanced security frameworks as traditional encryption mechanisms prove increasingly inadequate against evolving cyber threats. By examining the convergence of quantum artificial intelligence, quantum computing, and advanced threat detection algorithms, the text delineates next-generation paradigms capable of processing complex, large-scale data streams to deliver real-time resilience across high-stakes domains such as finance, healthcare, and defense. The work explores the computational intricacies driving AI-driven security protocols and demonstrates how integrating quantum AI techniques into distributed network architectures strengthens infrastructure, mitigates vulnerabilities, and safeguards economic growth. This comprehensive reference serves as an essential resource for academics, researchers, and industry professionals in network security seeking to engineer resilient, quantum-resistant architectures for next-generation digital ecosystems.

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Author / Editor Details
Abhishek Kumar, PhD is an Assistant Director and Associate Professor in the Computer Science and Engineering Department at Chandigarh University, Punjab, India. He has authored seven books, edited 51 books, and published more than 170 publications in reputed, peer-reviewed national and international journals, books, and conferences. His research areas include artificial intelligence, renewable energy, image processing, computer vision, data mining, and machine learning.

Hemant Kumar Saini, PhD is a Postdoctoral Fellow at Lincoln University, Malaysia and works in the Parul School of Engineering and Technology at Parul University, India. He has more than 12 years of teaching experience, demonstrated by his publication of six books and several articles in renowned journals and conference proceedings. His research interests include wireless networks, soft computing, deep and machine learning, Internet of Things (IoT), Industry 4.0, and smart contracts.

Pramod Singh Rathore, PhD is an Assistant Professor in the Department of Computer and Communication Engineering at Manipal University Jaipur. With more than 12 years of academic teaching experience, he has published more than 85 papers in reputable, peer-reviewed national and international journals, books, and conferences. His research interests include NS2, computer networks, and mining.

S. Oswalt Manoj, PhD is an Associate Professor in the Department of Computer Science and Engineering at the Sri Krishna College of Engineering and Technology, Tamil Nadu, India. He has authored one book, edited two books, and published more than 100 publications in reputed, peer-reviewed national and international journals, books, and conferences. His research areas include big data analytics, artificial intelligence, computer vision, machine learning, deep learning, and cloud computing.

Table of Contents
Preface
1. Blind Quantum AI in Security Digital Ecosystem
Shweta Solanki and Dasari Manasa
1.1 Introduction
1.1.1 Overview of Quantum Artificial Intelligence (QAI)
1.1.2 Understanding “Blind” Quantum AI Concept
1.1.3 Importance in the Digital Security Ecosystem
1.1.4 Research Motivation and Scope
1.2 Foundations of Blind Quantum AI
1.2.1 Basics of Quantum Computing and Quantum Information
1.2.2 Principles of Artificial Intelligence in Quantum Context
1.2.3 Concept of Blindness: Privacy-Preserving Computation
1.2.4 Comparative Analysis with Classical AI and Quantum AI
1.3 Security Digital Ecosystem: Challenges and Needs
1.3.1 Evolution of Cybersecurity Landscape
1.3.2 Emerging Threats: Advanced Persistent Threats, Deepfakes, and Quantum Attacks
1.3.3 Privacy, Trust, and Integrity Issues
1.3.4 Role of AI and Quantum in Next-Gen Security
1.4 Blind Quantum AI Architecture
1.4.1 System Design and Components
1.4.2 Encryption and Quantum Key Distribution (QKD) Integration
1.4.3 Blind Quantum Machine Learning (BQML) Models
1.4.4 Communication and Computation Framework
1.5 Applications in Digital Security Ecosystem
1.5.1 Secure Cloud Computing with Blind Quantum AI
1.5.2 Quantum-Safe Cryptography and Authentication
1.5.3 Intrusion Detection and Threat Prediction
1.5.4 Privacy-Preserving Data Analytics
1.5.5 Case Studies
1.6 Implementation Techniques and Algorithms
1.6.1 Quantum Neural Networks with Blind Protocols
1.6.2 Blind Quantum Support Vector Machines (BQ-SVMs)
1.6.3 Hybrid Classical-Quantum Security Algorithms
1.6.4 Optimization for Scalability and Efficiency
1.7 Ethical, Legal, and Societal Implications
1.7.1 Data Sovereignty and Compliance
1.7.2 Ethical Dilemmas of Blind Quantum AI in Surveillance
1.7.3 Human-Centric Trust in Quantum AI Systems
1.7.4 Policy Frameworks and Regulations
1.8 Challenges and Limitations
1.8.1 Technological Constraints of Current Quantum Hardware
1.8.2 Complexity of Blind Protocols
1.8.3 Computational Overheads and Cost Factors
1.8.4 Scalability to Real-World Security Ecosystems
1.9 Future Directions
1.9.1 Integration of Blind Quantum AI with General AI Systems
1.9.2 Cross-Domain Applications (Healthcare, Finance, Defense)
1.9.3 Toward Quantum Internet and Secure Global Ecosystems
1.9.4 Research Gaps and Open Problems
1.10 Conclusion
References
2. Quantum Cryptography in Secured IoT Devices
Brijesh Kumar Bhardwaj, Kavita Srivastava, Rishi Shrivastav,
Mohd Hasan, Shikhar Tiwari and Shashikant Soni
2.1 Introduction
2.1.1 Evolution of IoT and the Rising Need for Security
2.1.1.1 Introduction of Quantum Cryptography as a Game Changer
2.1.2 Core Principles of Quantum Cryptography
2.1.2.1 Why Quantum Cryptography for IoT?
2.2 Fundamentals of Quantum Cryptography
2.2.1 Principles of Quantum Mechanics Used in Cryptography
2.2.2 Quantum Key Distribution (QKD): Concept and Mechanisms
2.2.2.1 BB84 Protocol
2.2.2.2 E91 Protocol
2.3 Security Challenges in IoT Ecosystems
2.3.1 Resource Constraints (Memory, Power, Processing)
2.3.2 Scalability and Heterogeneity of IoT Devices
2.3.3 Vulnerabilities in Current Encryption Methods
2.3.4 Emerging Threats from Quantum Computing
2.4 Integration of Quantum Cryptography with IoT Devices
2.4.1 Architecture of Quantum-Secured IoT Systems
2.4.2 Lightweight QKD Protocols for Resource-Constrained
Devices
2.4.3 Deployment Strategies for Quantum Keys in IoT Networks
2.4.4 Case Study: Quantum-Secured Smart Home or Smart City Networks
2.5 Quantum Key Management in IoT
2.5.1 Key Generation, Distribution, and Storage Mechanisms
2.5.2 Quantum Random Number Generators (QRNGs) in IoT Security
2.5.2.1 Session Key Renewal and Rotation in Quantum-Enabled IoT
2.6 Post-Quantum Cryptographic Algorithms for IoT
2.6.1 Overview of NIST Standardized Post-Quantum Algorithms
2.6.2 Comparison of Lattice-Based, Code-Based, and Multivariate Cryptography
2.6.3 Suitability of Post-Quantum Cryptography in Low-Power
IoT Environments
2.7 Conclusion
2.8 Implementation Challenges and Constraints
2.8.1 Hardware Requirements for Quantum Cryptography in IoT
2.8.2 Communication Overheads and Energy Efficiency
2.8.3 Cost and Scalability Issues
2.9 Recent Developments and Pilot Projects
2.9.1 Real-World Deployments of Quantum-Secured IoT Devices
2.9.2 Quantum Networks for Industrial IoT
References
3. Enhancing Cybersecurity with Quantum AI in Industrial
Systems via Neurocomputing Empirical Results
P. Balaji Srikaanth, Raja Kumar Kolli, K. Manikandan, Bijay Kishor Shishir Sekhar Pattanaik, Preshni Shrivastava and R. Senthamil Selvan
3.1 Introduction
3.2 Method of Dynamic Watermarking
3.2.1 DW Security Assessments
3.2.2 Basic Mathematical Model of the Paired Water Tanks
3.3 Experimental Configuration
3.3.1 Hardware Configuration
3.3.2 HIL Configuration
3.4 State of the Art (SOTA)
3.5 Experimental Outcomes and Conversation
3.5.1 Watermarking Sign Validation
3.5.2 Model Accuracy Validation
3.5.3 Verification of System Identification
3.5.4 Situation 1 of the Attack: Random Noise Injection Attack
3.5.5 Attack Scenario 2: The Attack of Fraudulent Data Injection
3.5.6 Situation 3 of the Attack: Repeat Attack
3.6 Conclusion
References
4. Quantum AI-Based Deep Learning Model Optimisation for Real-Time Neurocomputing Techniques
Parimala Rajeshwari, T. Aditya Sai Srinivas, Venkateswara Raju Konduru, J. Balamurugan and R. Senthamil Selvan
4.1 Introduction
4.2 Antara NPU Infrastructure and Execution Flow
4.3 Antara NPU’s Best Practice for Quickly Identifying Objects
4.3.1 Evaluation of Task
4.3.2 Quantifying and Comparing Models
4.3.3 Queue-Based Processing and Double Buffering
4.4 Methodology
4.4.1 Preparing the Environment
4.4.2 Assessment
4.5 Discussion and Limitations
4.6 Conclusions
References
5. Quantum AI-Driven Cognitive Computing: Neurocomputing
Models for Human–Machine Collaboration
R. Purushothaman, Jayanthi G., Aaluri Seenu, J. Balamurugan and R. Senthamil Selvan
5.1 Introduction
5.2 Methodology
5.3 Result Evaluation
5.4 Discussion
5.5 Conclusion
5.6 Future Work
References
6. Analysing the Effectiveness of Quantum AI-Enhanced Neurocomputing in Marketing Optimisation
Anandkumar K. Shelat, Chinmoy Kumar, Suresh Ranganathan, Shailavi Modi, Shweta Tewari and R. Senthamil Selvan
6.1 Introduction
6.2 Methodology
6.2.1 Data Collection
6.2.2 Data Pre-Processing
6.2.3 AI-Enhanced Neurocomputing
6.2.4 Marketing Performance Prediction
6.2.5 Model Evaluation
6.3 Results
6.3.1 Data Processing Impact on Method Performance
6.3.2 Training Loss Over Time
6.3.3 Classification Accuracy of the AI Method
6.3.4 AI-Enhanced CNN vs. Traditional Methods Comparison
6.3.5 Transmission vs. Training Time Cost Analysis
6.3.6 Model Performance with Varying Dataset Sizes
6.4 Discussion
6.4.1 Comparison with Traditional and Modern AI Methods
6.5 Limitations and Future Directions
6.6 Conclusion
References
7. Quantum-Enabled DoS Attacks in Cybersecurity of Robotics
and Industrial Mechanical Systems
Harish Maurya, Mohit Mishra and Dharmendra Kumar Dubey
7.1 Overview of Denial-of-Service (DoS) Threats in Modern
Cybersecurity
7.1.1 Why DoS Matters for Robotics and Industrial Systems (RIS)
7.1.2 Safety Hazards
7.1.3 Production Downtime and Financial Impact
7.1.4 Rise of Quantum Computing and Its Influence on Traditional Cyber Attacks
7.2 Robotics and Industrial Mechanical Systems (RIS)
7.2.1 Definition and Core Components
7.2.2 Typical Communication Architectures in RIS
7.2.3 Denial-of-Service (DoS) Attacks
7.2.4 Quantum Computing Basics
7.3 Mechanics of Quantum-Enabled DoS Attacks
7.3.1 Overview: The Concept of Quantum-Enabled DoS
7.3.2 Attack Lifecycle: Step-by-Step Mechanics
7.4 Attack Surface in Robotics and Industrial Mechanical Systems
7.4.1 Introduction: Understanding the Attack Surface in the RIS Context
7.4.2 Communication Protocols as Primary Attack Surface
7.5 Case Study: Quantum-Enabled DoS Attack on an Industrial
Robotic Line
7.5.1 Objective of Case Study
7.5.2 System Description (Target Environment)
7.6 Detection Challenges: Why Traditional IDS/IPS Fail against Quantum-Enabled DoS
7.6.1 Introduction: Why is Detection the Biggest Challenge
7.6.2 Limitations of Signature-Based IDS
7.7 Defense Mechanisms and Quantum-Resilient Countermeasures
7.7.1 Introduction: Why is a New Approach to Defense Necessary
7.7.2 Network-Level Defense Strategies
7.8 Conclusion
Bibliography
8. Quantum AI and Autonomous Cybersecurity Systems
Arjit Tomar, Shardha Purohit and Jaswinder Singh
8.1 Introduction to Quantum AI in Cybersecurity
8.1.1 Background and Motivation
8.1.2 Evolution of the Cyber Threat Landscape
8.1.3 Limitations of Classical AI in Cybersecurity
8.1.4 Need for Quantum-Enhanced Autonomous Security Systems
8.2 Fundamentals of Quantum Computing for Cybersecurity
8.2.1 Basic Principles of Quantum Mechanics
8.2.2 Qubits, Superposition, and Entanglement
8.2.3 Quantum Gates and Quantum Circuits
8.2.4 Quantum Computing Models for Security Systems
8.3 Artificial Intelligence for Cybersecurity
8.3.1 Role of AI in Modern Cyber Defense
8.3.2 Machine Learning vs Deep Learning in Cybersecurity
8.4 Concept of Quantum Artificial Intelligence
8.4.1 Definition and Architecture of Quantum AI
8.4.2 Hybrid Quantum–Classical Learning Models
8.4.3 Quantum Machine Learning Techniques
8.4.4 Quantum AI as an Enabler of Autonomous Cybersecurity
8.5 Architecture of Autonomous Cybersecurity Systems
8.5.1 Layered Architecture of Quantum AI-Based Autonomous Cybersecurity Systems
8.5.2 Quantum Intelligence Layer
8.6 Quantum AI-Driven Threat Detection Mechanisms
8.6.1 Quantum Pattern Recognition for Cyber Threats
8.7 Quantum Cryptography and Secure Communications
8.7.1 Limitations of Classical Cryptography in the Quantum Era
8.7.2 Fundamentals of Quantum Cryptography
8.7.3 Quantum Key Distribution (QKD)
8.7.4 Post-Quantum Cryptography (PQC)
8.8 Conclusion and Future Outlook
Bibliography
9. Military Applications of Quantum AI in Autonomous Robotics and Advanced Mechanical Systems
Dharmendra Kumar Dubey, Surendra Kumar Maurya, Kamlesh Ashok Tripathi and Mahendra Pratap Yadav
9.1 Introduction
9.1.1 Evolution of IoT and the Rising Need for Security
9.2 Evolution of Military Robotics and Mechanical Systems
9.2.1 Early Mechanical Military Systems: Human-Centric Control
9.2.2 Introduction of Automation in Military Systems
9.2.3 Emergence of Semi-Autonomous Military Robotics
9.2.4 AI-Driven Autonomous Military Systems
9.2.5 Mechanical Systems to Intelligent Mechatronic Platforms
9.2.6 Limitations of Classical AI-Based Military Robotics
9.3 Fundamentals of Quantum Computing for Military Applications
9.3.1 Classical Computing vs. Quantum Computing
9.3.2 Qubits: The Building Block of Quantum Systems
9.3.3 Superposition and Battlefield Parallelism
9.3.4 Quantum Entanglement and Secure Military Coordination
9.3.5 Quantum Interference and Decision Optimization
9.3.6 Quantum Gates and Circuits
9.3.7 Quantum Algorithms Relevant to Military Systems
9.3.8 Noise, Decoherence, and Military Constraints
9.3.9 Role of Quantum Computing in Future Military Systems
9.4 Quantum Artificial Intelligence: Concepts, Models, and Architectures
9.4.1 From Classical AI to Quantum AI
9.4.2 Core Components of Quantum AI
9.4.3 Quantum Data Encoding for Military Sensors
9.4.4 Quantum Machine Learning Models
9.4.4.1 Quantum Neural Networks (QNNs)
9.4.4.2 Variational Quantum Circuits (VQCs)
9.4.4.3 Quantum Support Vector Machines (QSVMs)
9.4.5 Quantum Reinforcement Learning for Autonomous Combat Systems
9.5 Integration of Quantum AI with Autonomous Robotics and Advanced Mechanical Systems
9.5.1 System-Level View of Quantum AI-Enabled Military Robots
9.5.2 Quantum AI–Enhanced Perception and Sensor Fusion
9.5.3 Quantum AI–Driven Decision Making in Autonomous Robots
9.5.4 Integration with Advanced Mechanical Control Systems
9.6 Military Use Cases of Quantum AI-Enabled Autonomous
Systems
9.6.1 Unmanned Aerial Vehicles (UAVs) and Combat Drones
9.6.2 Autonomous Ground Vehicles (UGVs)
9.6.3 Naval and Underwater Autonomous Systems
9.7 Architecture of Quantum AI–Driven Autonomous Military Systems
9.7.1 Layered Architecture Overview
9.8 Challenges and Research Gaps in Quantum AI–Driven Military Autonomous Systems
9.8.1 Quantum Hardware Limitations
9.8.2 Hybrid Quantum–Classical Integration Challenges
9.8.3 Scalability and Real-Time Constraints
9.8.4 Mechanical System Adaptation and Reliability
9.8.5 Security and Adversarial Threats
9.8.6 Ethical and Governance Constraints
9.9 Conclusion and Future Outlook
References
10. Real-Time Fraud Detection in Banking Systems Using a Quantum AI-Enabled Adaptive Neurocomputing Framework
B. Veerajyothi, L. Suresh Kumar, Pramoda Patro and R. Deepa
10.1 Introduction
10.2 Methodology
10.2.1 Data Acquisition and Preprocessing
10.2.2 Neural Feature Extraction
10.2.3 Neurocomputing-Based Classification
10.2.4 Real-Time Monitoring and Adaptive Learning
10.2.5 Evaluation and Implementation
10.3 Results
10.4 Discussion
10.5 Conclusion
References
11. Enhancing Financial Fraud Detection through Quantum-Inspired Spiking Neural Networks and Deep Feature Learning
Sasanko Sekhar Gantayat and S. Swapna
11.1 Introduction
11.2 Methodology
11.2.1 Architecture of the Proposed Model
11.2.2 Data Acquisition and Preprocessing
11.2.3 Model Design and Implementation
11.2.4 Feature Selection and Engineering
11.2.5 Case Study Design
11.2.6 Training and Testing Protocols
11.2.7 Performance Metrics and Comparative Analysis in Fraud Detection
11.2.8 Computational Infrastructure and Ethical Compliance in Fraud Detection Systems
11.3 Results
11.3.1 Synthetic Fraud Injection Analysis
11.3.2 Real-World Sandboxed Deployment Results
11.3.3 Case Study—Banking Sector (Credit Card Fraud) and E-Commerce (Payment Fraud)
11.4 Discussion
11.5 Conclusion
References
12. Quantum AI-Driven Performance Optimization for Strategic Decision-Making and Business
Omprakash B., Kavitha S. Patil, Siva Surendra. Nandam and Y. Srinivasa Rao
12.1 Introduction
12.2 Methodology
12.3 Predictive Analytics
12.3.1 Regression Analysis
12.3.2 Time Series Evaluation
12.3.3 ML Algorithms
12.3.4 Cluster Assessment
12.4 Experimental Evaluation
12.4.1 Regression Analysis
12.4.2 Time Series Assessment
12.4.2.1 AR Element
12.4.2.2 I Element
12.4.2.3 MA Element
12.4.3 Decision Trees
12.4.3.1 Tree Creation
12.4.3.2 Splitting of Nodes
12.4.3.3 Allocation of Leaf Nodes
12.4.3.4 Pruning
12.4.4 Rf
12.4.4.1 Bootstrapped Sampling
12.4.4.2 Development of DT
12.4.4.3 Averaging (Regression) or Voting (Classification)
12.4.5 Gradient Boosting
12.4.6 Svm
12.4.7 Cluster Analysis
12.5 Analysis of Result
12.6 Conclusion
References
13. Quantum AI-Powered Cognitive Decision Support for Advanced Business Forecasting
Rama Devi P., Harrison Sunil D., Manoj Kumar Mishra and Pramoda Patro
13.1 Introduction
13.2 Methodology
13.2.1 Data Acquisition and Pre-Processing
13.2.2 Feature Engineering and Dimensionality Reduction
13.2.3 AI Development and Training
13.2.4 Cognitive Reasoning and Inference Layer
13.2.5 Decision Support System Integration
13.3 Results
13.3.1 Comparison of Actual vs. Predicted Business Sales
13.3.2 Performance of AI Methods: Forecast Accuracy and MSE
13.3.3 Feature Importance Analysis
13.3.4 Correlation Heatmap of Business Variables
13.3.5 Classification Performance: Confusion Matrix
13.3.6 Decision Support Dashboard Output
13.4 Discussion
13.5 Conclusion
13.6 Limitations and Future Work
References
14. Quantum Computing in Data Security
Pramod Singh Rathore and Vuyyuru Lalitha
14.1 Introduction
14.2 Essentials of Quantum Computing
14.3 Classical Data Security Mechanisms
14.3.1 Security Assumption Model
14.4 Quantum Threats to Data Security
14.5 Impact of Quantum Computing on Encryption
14.6 Quantum-Based Data Security Solutions
14.7 Post-Quantum Cryptography and Secure Transition
14.8 Quantum Computing in Cloud and Big Data Security
14.9 Implications on Ethics and Privacy
14.10 Future Directions and Research Challenges
14.11 Conclusion
References
15. Ethics and Privacy in Quantum AI in Cybersecurity
Fateh Bahadur Kunwar, Rajesh Pathak, Udai Bhan Trivedi, Vikas Misra and Narendra Singh Rathore
15.1 Introduction
15.2 Foundations of Quantum AI Cybersecurity
15.2.1 Overview of Quantum Computing Principles
15.2.2 Role of Artificial Intelligence in Cybersecurity
15.2.3 Concept of Quantum AI in Cybersecurity
15.2.4 Quantum AI-Enabled Cybersecurity Architecture
15.3 Ethical Dimensions of Quantum AI
15.3.1 Ethics in Advanced Cyber Technologies
15.3.2 Autonomy vs. Human Control
15.4 Privacy Challenges in Quantum AI Cybersecurity
15.4.1 Nature of Data in Quantum AI Cybersecurity Systems
15.4.2 Quantum-Enhanced Data Analytics and Privacy Risks
15.4.3 De-Anonymization and Re-Identification Risks
15.4.4 Quantum Threats to Encryption and Confidentiality
15.5 Quantum Threats to Cryptographic Privacy
15.5.1 Dependence of Privacy on Classical Cryptography
15.5.2 Quantum Algorithms and Cryptographic Vulnerabilities
15.5.2.1 Impact of Shor’s Algorithm
15.6 Ethical AI Design for Quantum Cybersecurity
15.6.1 Concept of Responsible Quantum AI
15.6.2 Ethics-by-Design Approach
15.6.3 Explainability and Transparency in Quantum AI
15.7 Legal, Regulatory, and Policy Perspectives
15.7.1 Applicability of Existing Data Protection Laws
15.7.2 Consent and Lawful Processing Challenges
15.7.3 Accountability and Liability Issues
15.8 Ethical Challenges in Military and Critical Infrastructure Security
15.8.1 Quantum AI in Military Cyber Operations
15.8.2 Autonomous Cyber Weapons and Ethical Risks
15.8.3 Ethical Challenges in Critical Infrastructure Protection
15.9 Case Studies and Use Cases
15.10 Conclusion
Bibliography
Index

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Blind Quantum Artificial Intelligence-Powered Cybersecurity