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Submit a ProposalEvolution and Applications of Quantum Computing
 | Edited by Sachi Nandan Mohanty, Rajanikanth Aluvalu and Sarita Mohanty Copyright: 2023 | Status: Published ISBN: 9781119904861 | Hardcover | 350 pages Price: $195 USD  |
One Line Description
The book is about the Quantum Model replacing traditional computing’s
classical model and gives a state-of-the-art technical overview of the current
efforts to develop quantum computing and applications for Industry 4.0.
Audience
The book will be very useful for researchers in computer science, artificial intelligence and quantum physics as well as students who want to understand the history of quantum computing along with its applications and have a technical state-of-the-art overview.
The book will be very useful for researchers in computer science, artificial intelligence and quantum physics as well as students who want to understand the history of quantum computing along with its applications and have a technical state-of-the-art overview.
Description
A holistic approach to the revolutionary world of quantum computing is presented in this book, which reveals valuable insights into this rapidly emerging technology. The book reflects the dependence of quantum computing on the physical phenomenon of superposition, entanglement, teleportation, and interference to simplify difficult mathematical problems which would have otherwise taken years to derive a definite solution for. An amalgamation of the information provided in the multiple chapters will elucidate the revolutionary and riveting research being carried out in the brand-new domain encompassing quantum computation, quantum information and quantum mechanics. Each chapter gives a concise introduction to the topic.
The book comprises 18 chapters and describes the pioneering work on the interaction between artificial intelligence, machine learning, and quantum computing along with their applications and potential role in the world of big data. Subjects include:
• Combinational circuits called the quantum multiplexer with secured quantum gate (CSWAP);
• Detecting malicious emails and URLs by using quantum text mining algorithms to distinguish between phishing and benign sites;
• Quantum data traffic analysis for intrusion detection systems;
• Applications of quantum computation in banking, netnomy and vehicular ad-hoc networks, virtual reality in the education of autistic children, identifying bacterial diseases and accelerating drug discovery;
• The critical domain of traditional classical cryptography and quantum cryptography.
Back to Top
A holistic approach to the revolutionary world of quantum computing is presented in this book, which reveals valuable insights into this rapidly emerging technology. The book reflects the dependence of quantum computing on the physical phenomenon of superposition, entanglement, teleportation, and interference to simplify difficult mathematical problems which would have otherwise taken years to derive a definite solution for. An amalgamation of the information provided in the multiple chapters will elucidate the revolutionary and riveting research being carried out in the brand-new domain encompassing quantum computation, quantum information and quantum mechanics. Each chapter gives a concise introduction to the topic.
The book comprises 18 chapters and describes the pioneering work on the interaction between artificial intelligence, machine learning, and quantum computing along with their applications and potential role in the world of big data. Subjects include:
• Combinational circuits called the quantum multiplexer with secured quantum gate (CSWAP);
• Detecting malicious emails and URLs by using quantum text mining algorithms to distinguish between phishing and benign sites;
• Quantum data traffic analysis for intrusion detection systems;
• Applications of quantum computation in banking, netnomy and vehicular ad-hoc networks, virtual reality in the education of autistic children, identifying bacterial diseases and accelerating drug discovery;
• The critical domain of traditional classical cryptography and quantum cryptography.
Back to Top
Author / Editor Details
Sachi Nandan Mohanty received his PhD from IIT Kharagpur, India in 2015, with MHRD scholarship from the Govt of India. He is now an associate professor, VIT-AP University, Andhra Pradesh. He has published more than 100 research articles in international journals as well as edited 24 books including many with the Wiley-Scrivener imprint. His research areas include data mining, big data analysis, cognitive science, fuzzy decision-making, brain-computer interface, cognition, and computational intelligence.
Sachi Nandan Mohanty received his PhD from IIT Kharagpur, India in 2015, with MHRD scholarship from the Govt of India. He is now an associate professor, VIT-AP University, Andhra Pradesh. He has published more than 100 research articles in international journals as well as edited 24 books including many with the Wiley-Scrivener imprint. His research areas include data mining, big data analysis, cognitive science, fuzzy decision-making, brain-computer interface, cognition, and computational intelligence.
Rajanikanth Aluvalu, PhD, is a professor in the Department of IT, Chaitanya Bharathi Institute of Technology, Hyderabad. He is a senior member of IEEE and his specialization is in high-performance computing. He has published 90 + research articles in peer-reviewed journals and conferences.
Sarita Mohanty is an assistant professor in the Department of Master in Computer Application, Centre for Post Graduate Study, OUAT, Govt of Odisha, India. Her research areas include digital forensics and cybersecurity. She has more than 10 years of teaching experience.
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Table of Contents
Preface
1. Introduction to Quantum Computing
V. Padmavathi, C. N. Sujatha, V. Sitharamulu, K. Sudheer Reddy and A. Mallikarjuna Reddy
1.1 Quantum Computation
1.2 Importance of Quantum Mechanics
1.3 Security Options in Quantum Mechanics
1.4 Quantum States and Qubits
1.5 Quantum Mechanics Interpretation
1.6 Quantum Mechanics Implementation
1.6.1 Photon Polarization Representation
1.7 Quantum Computation
1.7.1 Quantum Gates
1.8 Comparison of Quantum and Classical Computation
1.9 Quantum Cryptography
1.10 QKD
1.11 Conclusion
References
2. Fundamentals of Quantum Computing and Significance of Innovation
Swapna Mudrakola, Uma Maheswari V., Krishna Keerthi Chennam and MVV Prasad Kantidpudi
2.1 Quantum Reckoning Mechanism
2.2 Significance of Quantum Computing
2.3 Security Opportunities in Quantum Computing
2.4 Quantum States of Qubit
2.5 Quantum Computing Analysis
2.6 Quantum Computing Development Mechanism
2.7 Representation of Photon Polarization
2.8 Theory of Quantum Computing
2.9 Quantum Logical Gates
2.9.1 I-Qubit GATE
2.9.2 Hadamard-GATE
2.9.3 NOT_GATE_QUANTUM or Pauli_X-GATE
2.9.3.1 Pauli_Y-GATE
2.9.3.2 Pauli_Z-GATE
2.9.3.3 Pauli_S-Gate
2.9.4 Two-Qubit GATE
2.9.5 Controlled NOT(C-NOT)
2.9.6 The Two-Qubits are Swapped Using SWAP_GATE
2.9.7 C-Z-GATE (Controlled Z-GATE)
2.9.8 C-P-GATE (Controlled-Phase-GATE)
2.9.9 Three-Qubit Quantum GATE
2.9.9.1 GATE: Toffoli Gate
2.9.10 F-C-S GATE (Fredkin Controlled Swap-GATE)
2.10 Quantum Computation and Classical Computation Comparison
2.11 Quantum Cryptography
2.12 Quantum Key Distribution – QKD
2.13 Conclusion
References
3. Analysis of Design Quantum Multiplexer Using CSWAP and Controlled-R Gates
Virat Tara, Navneet Sharma, Pravindra Kumar and Kumar Gautam
3.1 Introduction
3.2 Mathematical Background of Quantum Circuits
3.2.1 Hadamard Gate
3.2.2 CSWAP Gates
3.2.3 Controlled-R Gates
3.3 Methodology of Designing Quantum Multiplexer (QMUX)
3.3.1 QMUX Using CSWAP Gates
3.3.1.1 Generalization
3.3.2 QMUX Using Controlled-R Gates
3.4 Analysis and Synthesis of Proposed Methodology
3.5 Complexity and Cost of Quantum Circuits
3.6 Conclusion
References
4. Artificial Intelligence and Machine Learning Algorithms in Quantum Computing Domain
Syed Abdul Moeed, P. Niranjan and G. Ashmitha
4.1 Introduction
4.1.1 Quantum Computing Convolutional Neural Network
4.2 Literature Survey
4.3 Quantum Algorithms Characteristics Used in Machine Learning Problems
4.3.1 Minimizing Quantum Algorithm
4.3.2 K-NN Algorithm
4.3.3 K-Means Algorithm
4.4 Tree Tensor Networking
4.5 TNN Implementation on IBM Quantum Processor
4.6 Neurotomography
4.7 Conclusion and Future Scope
References
5. Building a Virtual Reality-Based Framework for the Education of Autistic Kids
Kanak Pandit, Aditya Mogare, Achal Shah, Prachi Thete and Megharani Patil
5.1 Introduction
5.2 Literature Review
5.3 Proposed Work
5.3.1 Methodology
5.3.2 Work Flow of Neural Style Transfer
5.3.3 A-Frame
5.3.3.1 Setting Up the Virtual World and Adding Components
5.3.3.2 Adding Interactivity Through Raycasting
5.3.3.3 Animating the Components
5.3.4 Neural Style Transfer
5.3.4.1 Choosing the Content and Styling Image
5.3.4.2 Image Preprocessing and Generation of a Random Image
5.3.4.3 Model Design and Extraction of Content and Style
5.3.4.4 Loss Calculation
5.3.4.5 Model Optimization
5.4 Evaluation Metrics
5.5 Results
5.5.1 A-Frame
5.5.2 Neural Style Transfer
5.6 Conclusion
References
6. Detection of Phishing URLs Using Machine Learning and Deep Learning Models Implementing a URL Feature Extractor
Abishek Mahesh, Prithvi Seshadri, Shruti Mishra and Sandeep Kumar Satapathy
6.1 Introduction
6.2 Related Work
6.3 Proposed Model
6.3.1 URL Feature Extractor
6.3.2 Dataset
6.3.3 Methodologies
6.3.3.1 AdaBoost Classifier
6.3.3.2 Gradient Boosting Classifier
6.3.3.3 K-Nearest Neighbors
6.3.3.4 Logistic Regression
6.3.3.5 Artificial Neural Networks
6.3.3.6 Support Vector Machines (SVM)
6.3.3.7 Naïve Bayes Classifier
6.4 Results
6.5 Conclusions
References
7. Detection of Malicious Emails and URLs Using Text Mining
Heetakshi Fating, Aditya Narawade, Sandeep Kumar Satapathy and Shruti Mishra
7.1 Introduction
7.2 Related Works
7.3 Dataset Description
7.4 Proposed Architecture
7.5 Methodology
7.5.1 Methodology for the URL Dataset
7.5.2 Methodology for the Email Dataset
7.5.2.1 Overcoming the Overfitting Problem
7.5.2.2 Tokenization
7.5.2.3 Applying Machine Learning Algorithms
7.5.3 Detecting Presence of Malicious URLs in Otherwise Non-Malicious Emails
7.5.3.1 Preparation of Dataset
7.5.3.2 Creation of Features
7.5.3.3 Applying Machine Learning Algorithms
7.6 Results
7.6.1 URL Dataset
7.6.2 Email Dataset
7.6.3 Final Dataset
7.7 Conclusion
References
8. Quantum Data Traffic Analysis for Intrusion Detection System
Anshul Harish Khatri, Vaibhav Gadag, Simrat Singh, Sandeep Kumar Satapathy and Shruti Mishra
8.1 Introduction
8.2 Literature Overview
8.3 Methodology
8.3.1 Autoviz
8.3.2 Dataset
8.3.3 Proposed Models
8.3.3.1 Decision Tree
8.3.3.2 Random Forest Classifier Algorithm
8.3.3.3 AdaBoost Classifier
8.3.3.4 Ridge Classifier
8.3.3.5 Logistic Regression
8.3.3.6 SVM-Linear Kernel
8.3.3.7 Naive Bayes
8.3.3.8 Quadratic Discriminant Analysis
8.4 Results
8.5 Conclusion
References
9. Quantum Computing in Netnomy: A Networking Paradigm in e-Pharmaceutical Setting
Sarthak Dash, Sugyanta Priyadarshini, Sachi Nandan Mohanty, Sukanya Priyadarshini and Nisrutha Dulla
9.1 Introduction
9.2 Discussion
9.2.1 Exploring Market Functioning via Quantum Network Economy
9.2.1.1 Internal Networking Marketing
9.2.1.2 Layered Marketing
9.2.1.3 Role of Marketing in Pharma Network Organizations
9.2.1.4 Role of Marketing in Vertical Networking Organizations
9.2.1.5 Generic e-Commerce Entity Model in Pharmaceutical Industry
9.2.2 Analyzing the Usability of Quantum Netnomics in Attending Economic Development
9.2.2.1 Theory of 4Ps in Pharma Marketing Mix
9.2.2.2 Buying Behavior of the e-Consumers
9.2.2.3 Maintaining of Privacy and Security via Quantum Technology in e-Structure
9.2.2.4 Interface Influencing Sales
9.3 Results
9.4 Conclusion
References
10. Machine Learning Approach in the Indian Service Industry: A Case Study on Indian Banks
Pragati Priyadarshinee
10.1 Introduction
10.2 Literature Survey
10.3 Experimental Results
10.4 Conclusion
References
11. Accelerating Drug Discovery with Quantum Computing
Mahesh V. and Shimil Shijo
11.1 Introduction
11.2 Working Nature of Quantum Computers
11.3 Use Cases of Quantum Computing in Drug Discovery
11.4 Target Drug Identification and Validation
11.5 Drug Discovery Using Quantum Computers is Expected to Start by 2030
11.6 Conclusion
References
12. Problems and Demanding Situations in Traditional Cryptography: An Insistence for Quantum Computing to Secure Private Information
D. DShivaprasad, Mohamed Sirajudeen Yoosuf, P. Selvaramalakshmi, Manoj A. Patil
and Dasari Promod Kumar
12.1 Introduction to Cryptography
12.1.1 Confidentiality
12.1.2 Authentication
12.1.3 Integrity
12.1.4 Non-Repudiation
12.2 Different Types of Cryptography
12.2.1 One-Way Processing
12.2.1.1 Hash Function (One-Way Processing)
12.2.2 Two-Way Processing
12.2.2.1 Symmetric Cryptography
12.2.2.2 Asymmetric Cryptography
12.2.3 Algorithms Types
12.2.3.1 Stream Cipher
12.2.3.2 Block Cipher
12.2.4 Modes of Algorithm
12.2.4.1 Cipher Feedback Mode
12.2.4.2 Output Feedback Mode
12.2.4.3 Cipher Block Chaining Mode
12.2.4.4 Electronic Code Book
12.3 Common Attacks
12.3.1 Passive Attacks
12.3.1.1 Traffic Analysis
12.3.1.2 Eavesdropping
12.3.1.3 Foot Printing
12.3.1.4 War Driving
12.3.1.5 Spying
12.3.2 Active Attacks
12.3.2.1 Denial of Service
12.3.2.2 Distributed Denial of Service (DDOS)
12.3.2.3 Message Modification
12.3.2.4 Masquerade
12.3.2.5 Trojans
12.3.2.6 Replay Attacks
12.3.3 Programming Weapons for the Attackers
12.3.3.1 Dormant Phase
12.3.3.2 Propagation Phase
12.3.3.3 Triggering Phase
12.3.3.4 Execution Phase
12.4 Recent Cyber Attacks
12.5 Drawbacks of Traditional Cryptography
12.5.1 Cost and Time Delay
12.5.2 Disclosure of Mathematical Computation
12.5.3 Unsalted Hashing
12.5.4 Attacks
12.6 Need of Quantum Cryptography
12.6.1 Quantum Mechanics
12.7 Evolution of Quantum Cryptography
12.8 Conclusion and Future Work
References
13. Identification of Bacterial Diseases in Plants Using Re-Trained Transfer Learning in Quantum Computing Environment
Sri Silpa Padmanabhuni, B. Srikanth Reddy, A. Mallikarjuna Reddy and K. Sudheer Reddy
13.1 Introduction
13.2 Literature Review
13.3 Proposed Methodology
13.3.1 SVM Classifier
13.3.2 Random Forest to Classify the Rice Leaf
13.3.2.1 Image Pre-Processing
13.3.2.2 Feature Extraction
13.3.2.3 Classification
13.4 Experiment Results
Conclusion
References
14. Quantum Cryptography
Salma Fauzia
14.1 Fundamentals of Cryptography
14.2 Principle of Quantum Cryptography
14.2.1 Quantum vs. Conventional Cryptography
14.3 Quantum Key Distribution Protocols
14.3.1 Overview and BB84 Protocol
14.3.2 The B92 Protocol
14.3.3 E91 Protocol
14.3.4 SARG04 Protocol
14.4 Impact of the Sifting and Distillation Steps on the Key Size
14.5 Cryptanalysis
14.6 Quantum Key Distribution in the Real World
References
15. Security Issues in Vehicular Ad Hoc Networks and Quantum Computing
B. Veera Jyothi, L. Suresh Kumar and B. Surya Samantha
15.1 Introduction
15.2 Overview of VANET Security
15.2.1 Security of VANET
15.2.2 Attacks are Classified
15.3 Architectural and Systematic Security Methods
15.3.1 Solutions for Cryptography
15.3.2 Framework for Trust Groups
15.3.3 User Privacy Security System Based on ID
15.4 Suggestions on Particular Security Challenges
15.4.1 Content Delivery Integrity Metrics
15.4.2 Position Detection
15.4.3 Protective Techniques
15.5 Quantum Computing in Vehicular Networks
15.5.1 Securing Automotive Ecosystems: A Challenge
15.5.2 Generation of Quantum Random Numbers (QRNG)
15.6 Quantum Key Transmission (QKD)
15.7 Quantum Internet – A Future Vision
15.7.1 Quantum Internet Applications
15.7.2 Application Usage-Based Categorization
15.8 Conclusions
References
16. Quantum Cryptography with an Emphasis on the Security Analysis of QKD Protocols
Radhika Kavuri, Santhosh Voruganti, Sheena Mohammed, Sucharitha Inapanuri and B. Harish Goud
16.1 Introduction
16.2 Basic Terminology and Concepts of Quantum Cryptography
16.2.1 Quantum Cryptography and Quantum Key Distribution
16.2.2 Quantum Computing and Quantum Mechanics
16.2.3 Post-Quantum Cryptography
16.2.4 Quantum Entanglement
16.2.5 Heisenberg’s Uncertainty Principle
16.2.6 Qubits
16.2.7 Polarization
16.2.8 Traditional Cryptography vs. Quantum Cryptography
16.3 Trends in Quantum Cryptography
16.3.1 Global Quantum Key Distribution Links
16.3.2 Research Statistics on Quantum Cryptography
16.4 An Overview of QKD Protocols
16.4.1 Introduction to the Prepare-and-Measure Protocols
16.4.2 The BB84 Protocol
16.4.3 B92 Protocol
16.4.4 Six State Protocol (SSP)
16.4.5 SARG04 Protocol
16.4.6 Introduction to the Entanglement-Based Protocols
16.4.7 The E91 Protocol
16.4.8 The BBM92 Protocol
16.5 Security Concerns in QKD
16.6 Future Research Foresights
16.6.1 Increase in Bit Rate
16.6.2 Longer Distance Coverage
16.6.3 Long Distance Quantum Repeaters
16.6.4 Device Independent Quantum Cryptography
16.6.5 Development of Tools for Simulation and Measurements
16.6.6 Global Quantum Communication Network
16.6.7 Integrated Photonic Spaced QKD
16.6.8 Quantum Teleportation
References
17. Deep Learning-Based Quantum System for Human Activity Recognition
Shoba Rani Salvadi, Narsimhulu Pallati and Madhuri T.
17.1 Introduction
17.2 Related Works
17.3 Proposed Scheme
17.3.1 Datasets Description
17.3.2 Pre-Processing
17.3.3 Feature Extraction
17.3.4 Preliminaries
17.3.4.1 Quantum Computing
17.3.4.2 Convolutional Neural Networks
17.3.5 Proposed ORQC-CNN Model
17.3.5.1 Quantum Convolutional Layer
17.3.5.2 Convolutional Layer
17.3.5.3 Max-Pooling Layer
17.3.5.4 Fully Connected Layer
17.3.6 Parameter Selection Using Artificial Gorilla Troops Optimization Algorithm (AGTO)
17.3.6.1 Exploration Phase
17.3.6.2 Exploitation Phase
17.3.6.3 Follow the Silverback
17.3.6.4 Competition for Adult Females
17.3.7 Computational Difficulty
17.4 Results and Discussion
17.4.1 Performance Measure
17.4.2 Performance Analysis of Dataset 1
17.4.3 Performance Analysis of Dataset 2
17.4.4 Comparison
17.5 Conclusion
References
18. Quantum Intelligent Systems and Deep Learning
Bhagaban Swain and Debasis Gountia
18.1 Introduction
18.2 Quantum Support Vector Machine
18.3 Quantum Principal Component Analysis
18.4 Quantum Neural Network
18.5 Variational Quantum Classifier
18.6 Conclusion
References
Index
Back to Top
Preface
1. Introduction to Quantum Computing
V. Padmavathi, C. N. Sujatha, V. Sitharamulu, K. Sudheer Reddy and A. Mallikarjuna Reddy
1.1 Quantum Computation
1.2 Importance of Quantum Mechanics
1.3 Security Options in Quantum Mechanics
1.4 Quantum States and Qubits
1.5 Quantum Mechanics Interpretation
1.6 Quantum Mechanics Implementation
1.6.1 Photon Polarization Representation
1.7 Quantum Computation
1.7.1 Quantum Gates
1.8 Comparison of Quantum and Classical Computation
1.9 Quantum Cryptography
1.10 QKD
1.11 Conclusion
References
2. Fundamentals of Quantum Computing and Significance of Innovation
Swapna Mudrakola, Uma Maheswari V., Krishna Keerthi Chennam and MVV Prasad Kantidpudi
2.1 Quantum Reckoning Mechanism
2.2 Significance of Quantum Computing
2.3 Security Opportunities in Quantum Computing
2.4 Quantum States of Qubit
2.5 Quantum Computing Analysis
2.6 Quantum Computing Development Mechanism
2.7 Representation of Photon Polarization
2.8 Theory of Quantum Computing
2.9 Quantum Logical Gates
2.9.1 I-Qubit GATE
2.9.2 Hadamard-GATE
2.9.3 NOT_GATE_QUANTUM or Pauli_X-GATE
2.9.3.1 Pauli_Y-GATE
2.9.3.2 Pauli_Z-GATE
2.9.3.3 Pauli_S-Gate
2.9.4 Two-Qubit GATE
2.9.5 Controlled NOT(C-NOT)
2.9.6 The Two-Qubits are Swapped Using SWAP_GATE
2.9.7 C-Z-GATE (Controlled Z-GATE)
2.9.8 C-P-GATE (Controlled-Phase-GATE)
2.9.9 Three-Qubit Quantum GATE
2.9.9.1 GATE: Toffoli Gate
2.9.10 F-C-S GATE (Fredkin Controlled Swap-GATE)
2.10 Quantum Computation and Classical Computation Comparison
2.11 Quantum Cryptography
2.12 Quantum Key Distribution – QKD
2.13 Conclusion
References
3. Analysis of Design Quantum Multiplexer Using CSWAP and Controlled-R Gates
Virat Tara, Navneet Sharma, Pravindra Kumar and Kumar Gautam
3.1 Introduction
3.2 Mathematical Background of Quantum Circuits
3.2.1 Hadamard Gate
3.2.2 CSWAP Gates
3.2.3 Controlled-R Gates
3.3 Methodology of Designing Quantum Multiplexer (QMUX)
3.3.1 QMUX Using CSWAP Gates
3.3.1.1 Generalization
3.3.2 QMUX Using Controlled-R Gates
3.4 Analysis and Synthesis of Proposed Methodology
3.5 Complexity and Cost of Quantum Circuits
3.6 Conclusion
References
4. Artificial Intelligence and Machine Learning Algorithms in Quantum Computing Domain
Syed Abdul Moeed, P. Niranjan and G. Ashmitha
4.1 Introduction
4.1.1 Quantum Computing Convolutional Neural Network
4.2 Literature Survey
4.3 Quantum Algorithms Characteristics Used in Machine Learning Problems
4.3.1 Minimizing Quantum Algorithm
4.3.2 K-NN Algorithm
4.3.3 K-Means Algorithm
4.4 Tree Tensor Networking
4.5 TNN Implementation on IBM Quantum Processor
4.6 Neurotomography
4.7 Conclusion and Future Scope
References
5. Building a Virtual Reality-Based Framework for the Education of Autistic Kids
Kanak Pandit, Aditya Mogare, Achal Shah, Prachi Thete and Megharani Patil
5.1 Introduction
5.2 Literature Review
5.3 Proposed Work
5.3.1 Methodology
5.3.2 Work Flow of Neural Style Transfer
5.3.3 A-Frame
5.3.3.1 Setting Up the Virtual World and Adding Components
5.3.3.2 Adding Interactivity Through Raycasting
5.3.3.3 Animating the Components
5.3.4 Neural Style Transfer
5.3.4.1 Choosing the Content and Styling Image
5.3.4.2 Image Preprocessing and Generation of a Random Image
5.3.4.3 Model Design and Extraction of Content and Style
5.3.4.4 Loss Calculation
5.3.4.5 Model Optimization
5.4 Evaluation Metrics
5.5 Results
5.5.1 A-Frame
5.5.2 Neural Style Transfer
5.6 Conclusion
References
6. Detection of Phishing URLs Using Machine Learning and Deep Learning Models Implementing a URL Feature Extractor
Abishek Mahesh, Prithvi Seshadri, Shruti Mishra and Sandeep Kumar Satapathy
6.1 Introduction
6.2 Related Work
6.3 Proposed Model
6.3.1 URL Feature Extractor
6.3.2 Dataset
6.3.3 Methodologies
6.3.3.1 AdaBoost Classifier
6.3.3.2 Gradient Boosting Classifier
6.3.3.3 K-Nearest Neighbors
6.3.3.4 Logistic Regression
6.3.3.5 Artificial Neural Networks
6.3.3.6 Support Vector Machines (SVM)
6.3.3.7 Naïve Bayes Classifier
6.4 Results
6.5 Conclusions
References
7. Detection of Malicious Emails and URLs Using Text Mining
Heetakshi Fating, Aditya Narawade, Sandeep Kumar Satapathy and Shruti Mishra
7.1 Introduction
7.2 Related Works
7.3 Dataset Description
7.4 Proposed Architecture
7.5 Methodology
7.5.1 Methodology for the URL Dataset
7.5.2 Methodology for the Email Dataset
7.5.2.1 Overcoming the Overfitting Problem
7.5.2.2 Tokenization
7.5.2.3 Applying Machine Learning Algorithms
7.5.3 Detecting Presence of Malicious URLs in Otherwise Non-Malicious Emails
7.5.3.1 Preparation of Dataset
7.5.3.2 Creation of Features
7.5.3.3 Applying Machine Learning Algorithms
7.6 Results
7.6.1 URL Dataset
7.6.2 Email Dataset
7.6.3 Final Dataset
7.7 Conclusion
References
8. Quantum Data Traffic Analysis for Intrusion Detection System
Anshul Harish Khatri, Vaibhav Gadag, Simrat Singh, Sandeep Kumar Satapathy and Shruti Mishra
8.1 Introduction
8.2 Literature Overview
8.3 Methodology
8.3.1 Autoviz
8.3.2 Dataset
8.3.3 Proposed Models
8.3.3.1 Decision Tree
8.3.3.2 Random Forest Classifier Algorithm
8.3.3.3 AdaBoost Classifier
8.3.3.4 Ridge Classifier
8.3.3.5 Logistic Regression
8.3.3.6 SVM-Linear Kernel
8.3.3.7 Naive Bayes
8.3.3.8 Quadratic Discriminant Analysis
8.4 Results
8.5 Conclusion
References
9. Quantum Computing in Netnomy: A Networking Paradigm in e-Pharmaceutical Setting
Sarthak Dash, Sugyanta Priyadarshini, Sachi Nandan Mohanty, Sukanya Priyadarshini and Nisrutha Dulla
9.1 Introduction
9.2 Discussion
9.2.1 Exploring Market Functioning via Quantum Network Economy
9.2.1.1 Internal Networking Marketing
9.2.1.2 Layered Marketing
9.2.1.3 Role of Marketing in Pharma Network Organizations
9.2.1.4 Role of Marketing in Vertical Networking Organizations
9.2.1.5 Generic e-Commerce Entity Model in Pharmaceutical Industry
9.2.2 Analyzing the Usability of Quantum Netnomics in Attending Economic Development
9.2.2.1 Theory of 4Ps in Pharma Marketing Mix
9.2.2.2 Buying Behavior of the e-Consumers
9.2.2.3 Maintaining of Privacy and Security via Quantum Technology in e-Structure
9.2.2.4 Interface Influencing Sales
9.3 Results
9.4 Conclusion
References
10. Machine Learning Approach in the Indian Service Industry: A Case Study on Indian Banks
Pragati Priyadarshinee
10.1 Introduction
10.2 Literature Survey
10.3 Experimental Results
10.4 Conclusion
References
11. Accelerating Drug Discovery with Quantum Computing
Mahesh V. and Shimil Shijo
11.1 Introduction
11.2 Working Nature of Quantum Computers
11.3 Use Cases of Quantum Computing in Drug Discovery
11.4 Target Drug Identification and Validation
11.5 Drug Discovery Using Quantum Computers is Expected to Start by 2030
11.6 Conclusion
References
12. Problems and Demanding Situations in Traditional Cryptography: An Insistence for Quantum Computing to Secure Private Information
D. DShivaprasad, Mohamed Sirajudeen Yoosuf, P. Selvaramalakshmi, Manoj A. Patil
and Dasari Promod Kumar
12.1 Introduction to Cryptography
12.1.1 Confidentiality
12.1.2 Authentication
12.1.3 Integrity
12.1.4 Non-Repudiation
12.2 Different Types of Cryptography
12.2.1 One-Way Processing
12.2.1.1 Hash Function (One-Way Processing)
12.2.2 Two-Way Processing
12.2.2.1 Symmetric Cryptography
12.2.2.2 Asymmetric Cryptography
12.2.3 Algorithms Types
12.2.3.1 Stream Cipher
12.2.3.2 Block Cipher
12.2.4 Modes of Algorithm
12.2.4.1 Cipher Feedback Mode
12.2.4.2 Output Feedback Mode
12.2.4.3 Cipher Block Chaining Mode
12.2.4.4 Electronic Code Book
12.3 Common Attacks
12.3.1 Passive Attacks
12.3.1.1 Traffic Analysis
12.3.1.2 Eavesdropping
12.3.1.3 Foot Printing
12.3.1.4 War Driving
12.3.1.5 Spying
12.3.2 Active Attacks
12.3.2.1 Denial of Service
12.3.2.2 Distributed Denial of Service (DDOS)
12.3.2.3 Message Modification
12.3.2.4 Masquerade
12.3.2.5 Trojans
12.3.2.6 Replay Attacks
12.3.3 Programming Weapons for the Attackers
12.3.3.1 Dormant Phase
12.3.3.2 Propagation Phase
12.3.3.3 Triggering Phase
12.3.3.4 Execution Phase
12.4 Recent Cyber Attacks
12.5 Drawbacks of Traditional Cryptography
12.5.1 Cost and Time Delay
12.5.2 Disclosure of Mathematical Computation
12.5.3 Unsalted Hashing
12.5.4 Attacks
12.6 Need of Quantum Cryptography
12.6.1 Quantum Mechanics
12.7 Evolution of Quantum Cryptography
12.8 Conclusion and Future Work
References
13. Identification of Bacterial Diseases in Plants Using Re-Trained Transfer Learning in Quantum Computing Environment
Sri Silpa Padmanabhuni, B. Srikanth Reddy, A. Mallikarjuna Reddy and K. Sudheer Reddy
13.1 Introduction
13.2 Literature Review
13.3 Proposed Methodology
13.3.1 SVM Classifier
13.3.2 Random Forest to Classify the Rice Leaf
13.3.2.1 Image Pre-Processing
13.3.2.2 Feature Extraction
13.3.2.3 Classification
13.4 Experiment Results
Conclusion
References
14. Quantum Cryptography
Salma Fauzia
14.1 Fundamentals of Cryptography
14.2 Principle of Quantum Cryptography
14.2.1 Quantum vs. Conventional Cryptography
14.3 Quantum Key Distribution Protocols
14.3.1 Overview and BB84 Protocol
14.3.2 The B92 Protocol
14.3.3 E91 Protocol
14.3.4 SARG04 Protocol
14.4 Impact of the Sifting and Distillation Steps on the Key Size
14.5 Cryptanalysis
14.6 Quantum Key Distribution in the Real World
References
15. Security Issues in Vehicular Ad Hoc Networks and Quantum Computing
B. Veera Jyothi, L. Suresh Kumar and B. Surya Samantha
15.1 Introduction
15.2 Overview of VANET Security
15.2.1 Security of VANET
15.2.2 Attacks are Classified
15.3 Architectural and Systematic Security Methods
15.3.1 Solutions for Cryptography
15.3.2 Framework for Trust Groups
15.3.3 User Privacy Security System Based on ID
15.4 Suggestions on Particular Security Challenges
15.4.1 Content Delivery Integrity Metrics
15.4.2 Position Detection
15.4.3 Protective Techniques
15.5 Quantum Computing in Vehicular Networks
15.5.1 Securing Automotive Ecosystems: A Challenge
15.5.2 Generation of Quantum Random Numbers (QRNG)
15.6 Quantum Key Transmission (QKD)
15.7 Quantum Internet – A Future Vision
15.7.1 Quantum Internet Applications
15.7.2 Application Usage-Based Categorization
15.8 Conclusions
References
16. Quantum Cryptography with an Emphasis on the Security Analysis of QKD Protocols
Radhika Kavuri, Santhosh Voruganti, Sheena Mohammed, Sucharitha Inapanuri and B. Harish Goud
16.1 Introduction
16.2 Basic Terminology and Concepts of Quantum Cryptography
16.2.1 Quantum Cryptography and Quantum Key Distribution
16.2.2 Quantum Computing and Quantum Mechanics
16.2.3 Post-Quantum Cryptography
16.2.4 Quantum Entanglement
16.2.5 Heisenberg’s Uncertainty Principle
16.2.6 Qubits
16.2.7 Polarization
16.2.8 Traditional Cryptography vs. Quantum Cryptography
16.3 Trends in Quantum Cryptography
16.3.1 Global Quantum Key Distribution Links
16.3.2 Research Statistics on Quantum Cryptography
16.4 An Overview of QKD Protocols
16.4.1 Introduction to the Prepare-and-Measure Protocols
16.4.2 The BB84 Protocol
16.4.3 B92 Protocol
16.4.4 Six State Protocol (SSP)
16.4.5 SARG04 Protocol
16.4.6 Introduction to the Entanglement-Based Protocols
16.4.7 The E91 Protocol
16.4.8 The BBM92 Protocol
16.5 Security Concerns in QKD
16.6 Future Research Foresights
16.6.1 Increase in Bit Rate
16.6.2 Longer Distance Coverage
16.6.3 Long Distance Quantum Repeaters
16.6.4 Device Independent Quantum Cryptography
16.6.5 Development of Tools for Simulation and Measurements
16.6.6 Global Quantum Communication Network
16.6.7 Integrated Photonic Spaced QKD
16.6.8 Quantum Teleportation
References
17. Deep Learning-Based Quantum System for Human Activity Recognition
Shoba Rani Salvadi, Narsimhulu Pallati and Madhuri T.
17.1 Introduction
17.2 Related Works
17.3 Proposed Scheme
17.3.1 Datasets Description
17.3.2 Pre-Processing
17.3.3 Feature Extraction
17.3.4 Preliminaries
17.3.4.1 Quantum Computing
17.3.4.2 Convolutional Neural Networks
17.3.5 Proposed ORQC-CNN Model
17.3.5.1 Quantum Convolutional Layer
17.3.5.2 Convolutional Layer
17.3.5.3 Max-Pooling Layer
17.3.5.4 Fully Connected Layer
17.3.6 Parameter Selection Using Artificial Gorilla Troops Optimization Algorithm (AGTO)
17.3.6.1 Exploration Phase
17.3.6.2 Exploitation Phase
17.3.6.3 Follow the Silverback
17.3.6.4 Competition for Adult Females
17.3.7 Computational Difficulty
17.4 Results and Discussion
17.4.1 Performance Measure
17.4.2 Performance Analysis of Dataset 1
17.4.3 Performance Analysis of Dataset 2
17.4.4 Comparison
17.5 Conclusion
References
18. Quantum Intelligent Systems and Deep Learning
Bhagaban Swain and Debasis Gountia
18.1 Introduction
18.2 Quantum Support Vector Machine
18.3 Quantum Principal Component Analysis
18.4 Quantum Neural Network
18.5 Variational Quantum Classifier
18.6 Conclusion
References
Index
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