Evolution and Applications of Quantum Computing

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.

• Preface xvii1 Introduction to Quantum Computing 1V. Padmavathi, C. N. Sujatha, V. Sitharamulu, K. Sudheer Reddy and A. Mallikarjuna Reddy1.1 Quantum Computation 21.2 Importance of Quantum Mechanics 21.3 Security Options in Quantum Mechanics 21.4 Quantum States and Qubits 31.5 Quantum Mechanics Interpretation 41.6 Quantum Mechanics Implementation 41.6.1 Photon Polarization Representation 41.7 Quantum Computation 61.7.1 Quantum Gates 71.8 Comparison of Quantum and Classical Computation 111.9 Quantum Cryptography 121.10 Qkd 121.11 Conclusion 12References 132 Fundamentals of Quantum Computing and Significance of Innovation 15Swapna Mudrakola, Uma Maheswari V., Krishna Keerthi Chennam and MVV Prasad Kantidpudi2.1 Quantum Reckoning Mechanism 162.2 Significance of Quantum Computing 162.3 Security Opportunities in Quantum Computing 162.4 Quantum States of Qubit 172.5 Quantum Computing Analysis 172.6 Quantum Computing Development Mechanism 182.7 Representation of Photon Polarization 182.8 Theory of Quantum Computing 202.9 Quantum Logical Gates 212.9.1 I-Qubit GATE 212.9.2 Hadamard-GATE 222.9.3 NOT_GATE_QUANTUM or Pauli_X-GATE 222.9.3.1 Pauli_Y-GATE 232.9.3.2 Pauli_Z-GATE 232.9.3.3 Pauli_S-Gate 232.9.4 Two-Qubit GATE 242.9.5 Controlled NOT(C-NOT) 242.9.6 The Two-Qubits are Swapped Using SWAP_GATE 242.9.7 C-Z-GATE (Controlled Z-GATE) 242.9.8 C-P-GATE (Controlled-Phase-GATE) 252.9.9 Three-Qubit Quantum GATE 252.9.9.1 GATE: Toffoli Gate 252.9.10 F-C-S GATE (Fredkin Controlled Swap-GATE) 262.10 Quantum Computation and Classical Computation Comparison 272.11 Quantum Cryptography 272.12 Quantum Key Distribution – QKD 272.13 Conclusion 28References 283 Analysis of Design Quantum Multiplexer Using CSWAP and Controlled-R Gates 31Virat Tara, Navneet Sharma, Pravindra Kumar and Kumar Gautam3.1 Introduction 323.2 Mathematical Background of Quantum Circuits 343.2.1 Hadamard Gate 343.2.2 CSWAP Gates 353.2.3 Controlled-R Gates 363.3 Methodology of Designing Quantum Multiplexer (QMUX) 363.3.1 QMUX Using CSWAP Gates 363.3.1.1 Generalization 373.3.2 QMUX Using Controlled-R Gates 373.4 Analysis and Synthesis of Proposed Methodology 393.5 Complexity and Cost of Quantum Circuits 413.6 Conclusion 42References 424 Artificial Intelligence and Machine Learning Algorithms in Quantum Computing Domain 45Syed Abdul Moeed, P. Niranjan and G. Ashmitha4.1 Introduction 464.1.1 Quantum Computing Convolutional Neural Network 514.2 Literature Survey 524.3 Quantum Algorithms Characteristics Used in Machine Learning Problems 584.3.1 Minimizing Quantum Algorithm 584.3.2 K-NN Algorithm 584.3.3 K-Means Algorithm 604.4 Tree Tensor Networking 614.5 TNN Implementation on IBM Quantum Processor 624.6 Neurotomography 624.7 Conclusion and Future Scope 63References 645 Building a Virtual Reality-Based Framework for the Education of Autistic Kids 67Kanak Pandit, Aditya Mogare, Achal Shah, Prachi Thete and Megharani Patil5.1 Introduction 685.2 Literature Review 715.3 Proposed Work 745.3.1 Methodology 745.3.2 Work Flow of Neural Style Transfer 755.3.3 A-Frame 755.3.3.1 Setting Up the Virtual World and Adding Components 755.3.3.2 Adding Interactivity Through Raycasting 765.3.3.3 Animating the Components 775.3.4 Neural Style Transfer 785.3.4.1 Choosing the Content and Styling Image 795.3.4.2 Image Preprocessing and Generation of a Random Image 795.3.4.3 Model Design and Extraction of Content and Style 815.3.4.4 Loss Calculation 815.3.4.5 Model Optimization 845.4 Evaluation Metrics 865.5 Results 895.5.1 A-Frame 895.5.2 Neural Style Transfer 905.6 Conclusion 90References 916 Detection of Phishing URLs Using Machine Learning and Deep Learning Models Implementing a URL Feature Extractor 93Abishek Mahesh, Prithvi Seshadri, Shruti Mishra and Sandeep Kumar Satapathy6.1 Introduction 946.2 Related Work 946.3 Proposed Model 956.3.1 URL Feature Extractor 956.3.2 Dataset 1036.3.3 Methodologies 1046.3.3.1 AdaBoost Classifier 1056.3.3.2 Gradient Boosting Classifier 1056.3.3.3 K-Nearest Neighbors 1056.3.3.4 Logistic Regression 1066.3.3.5 Artificial Neural Networks 1066.3.3.6 Support Vector Machines (SVM) 1076.3.3.7 Naïve Bayes Classifier 1076.4 Results 1096.5 Conclusions 109References 1097 Detection of Malicious Emails and URLs Using Text Mining 111Heetakshi Fating, Aditya Narawade, Sandeep Kumar Satapathy and Shruti Mishra7.1 Introduction 1127.2 Related Works 1127.3 Dataset Description 1147.4 Proposed Architecture 1157.5 Methodology 1167.5.1 Methodology for the URL Dataset 1167.5.2 Methodology for the Email Dataset 1187.5.2.1 Overcoming the Overfitting Problem 1187.5.2.2 Tokenization 1197.5.2.3 Applying Machine Learning Algorithms 1197.5.3 Detecting Presence of Malicious URLs in Otherwise Non-Malicious Emails 1197.5.3.1 Preparation of Dataset 1197.5.3.2 Creation of Features 1207.5.3.3 Applying Machine Learning Algorithms 1207.6 Results 1207.6.1 URL Dataset 1207.6.2 Email Dataset 1217.6.3 Final Dataset 1217.7 Conclusion 122References 1228 Quantum Data Traffic Analysis for Intrusion Detection System 125Anshul Harish Khatri, Vaibhav Gadag, Simrat Singh, Sandeep Kumar Satapathy and Shruti Mishra8.1 Introduction 1268.2 Literature Overview 1278.3 Methodology 1298.3.1 Autoviz 1298.3.2 Dataset 1328.3.3 Proposed Models 1328.3.3.1 Decision Tree 1358.3.3.2 Random Forest Classifier Algorithm 1368.3.3.3 AdaBoost Classifier 1368.3.3.4 Ridge Classifier 1378.3.3.5 Logistic Regression 1378.3.3.6 SVM-Linear Kernel 1388.3.3.7 Naive Bayes 1388.3.3.8 Quadratic Discriminant Analysis 1398.4 Results 1408.5 Conclusion 141References 1429 Quantum Computing in Netnomy: A Networking Paradigm in e-Pharmaceutical Setting 145Sarthak Dash, Sugyanta Priyadarshini, Sachi Nandan Mohanty, Sukanya Priyadarshini and Nisrutha Dulla9.1 Introduction 1469.2 Discussion 1489.2.1 Exploring Market Functioning via Quantum Network Economy 1489.2.1.1 Internal Networking Marketing 1499.2.1.2 Layered Marketing 1499.2.1.3 Role of Marketing in Pharma Network Organizations 1509.2.1.4 Role of Marketing in Vertical Networking Organizations 1529.2.1.5 Generic e-Commerce Entity Model in Pharmaceutical Industry 1539.2.2 Analyzing the Usability of Quantum Netnomics in Attending Economic Development 1549.2.2.1 Theory of 4Ps in Pharma Marketing mix 1559.2.2.2 Buying Behavior of the e-Consumers 1569.2.2.3 Maintaining of Privacy and Security via Quantum Technology in e-Structure 1579.2.2.4 Interface Influencing Sales 1579.3 Results 1589.4 Conclusion 159References 15910 Machine Learning Approach in the Indian Service Industry: A Case Study on Indian Banks 163Pragati Priyadarshinee10.1 Introduction 16310.2 Literature Survey 16410.3 Experimental Results 17010.4 Conclusion 172References 17211 Accelerating Drug Discovery with Quantum Computing 175Mahesh V. and Shimil Shijo11.1 Introduction 17511.2 Working Nature of Quantum Computers 17611.3 Use Cases of Quantum Computing in Drug Discovery 17811.4 Target Drug Identification and Validation 17911.5 Drug Discovery Using Quantum Computers is Expected to Start by 2030 17911.6 Conclusion 180References 18112 Problems and Demanding Situations in Traditional Cryptography: An Insistence for Quantum Computing to Secure Private Information 183D. DShivaprasad, Mohamed Sirajudeen Yoosuf, P. Selvaramalakshmi, Manoj A. Patil and Dasari Promod Kumar12.1 Introduction to Cryptography 18412.1.1 Confidentiality 18412.1.2 Authentication 18512.1.3 Integrity 18512.1.4 Non-Repudiation 18612.2 Different Types of Cryptography 18612.2.1 One-Way Processing 18612.2.1.1 Hash Function (One-Way Processing) 18612.2.2 Two-Way Processing 18712.2.2.1 Symmetric Cryptography 18812.2.2.2 Asymmetric Cryptography 18912.2.3 Algorithms Types 19012.2.3.1 Stream Cipher 19012.2.3.2 Block Cipher 19112.2.4 Modes of Algorithm 19212.2.4.1 Cipher Feedback Mode 19212.2.4.2 Output Feedback Mode 19212.2.4.3 Cipher Block Chaining Mode 19212.2.4.4 Electronic Code Book 19212.3 Common Attacks 19312.3.1 Passive Attacks 19312.3.1.1 Traffic Analysis 19312.3.1.2 Eavesdropping 19412.3.1.3 Foot Printing 19512.3.1.4 War Driving 19512.3.1.5 Spying 19512.3.2 Active Attacks 19612.3.2.1 Denial of Service 19612.3.2.2 Distributed Denial of Service (DDOS) 19712.3.2.3 Message Modification 19712.3.2.4 Masquerade 19712.3.2.5 Trojans 19812.3.2.6 Replay Attacks 19912.3.3 Programming Weapons for the Attackers 19912.3.3.1 Dormant Phase 20012.3.3.2 Propagation Phase 20012.3.3.3 Triggering Phase 20112.3.3.4 Execution Phase 20112.4 Recent Cyber Attacks 20112.5 Drawbacks of Traditional Cryptography 20312.5.1 Cost and Time Delay 20312.5.2 Disclosure of Mathematical Computation 20312.5.3 Unsalted Hashing 20412.5.4 Attacks 20412.6 Need of Quantum Cryptography 20412.6.1 Quantum Mechanics 20412.7 Evolution of Quantum Cryptography 20512.8 Conclusion and Future Work 205References 20513 Identification of Bacterial Diseases in Plants Using Re-Trained Transfer Learning in Quantum Computing Environment 207Sri Silpa Padmanabhuni, B. Srikanth Reddy, A. Mallikarjuna Reddy and K. Sudheer Reddy13.1 Introduction 20813.2 Literature Review 21813.3 Proposed Methodology 22013.3.1 SVM Classifier 22213.3.2 Random Forest to Classify the Rice Leaf 22313.3.2.1 Image Pre-Processing 22313.3.2.2 Feature Extraction 22313.3.2.3 Classification 22413.4 Experiment Results 226Conclusion 230References 23014 Quantum Cryptography 233Salma Fauzia14.1 Fundamentals of Cryptography 23414.2 Principle of Quantum Cryptography 23714.2.1 Quantum vs. Conventional Cryptography 23714.3 Quantum Key Distribution Protocols 23814.3.1 Overview and BB84 Protocol 23814.3.2 The B92 Protocol 24014.3.3 E91 Protocol 24114.3.4 SARG04 Protocol 24314.4 Impact of the Sifting and Distillation Steps on the Key Size 24314.5 Cryptanalysis 24614.6 Quantum Key Distribution in the Real World 247References 24815 Security Issues in Vehicular Ad Hoc Networks and Quantum Computing 249B. Veera Jyothi, L. Suresh Kumar and B. Surya Samantha15.1 Introduction 25015.2 Overview of VANET Security 25015.2.1 Security of VANET 25015.2.2 Attacks are Classified 25115.3 Architectural and Systematic Security Methods 25215.3.1 Solutions for Cryptography 25215.3.2 Framework for Trust Groups 25215.3.3 User Privacy Security System Based on ID 25315.4 Suggestions on Particular Security Challenges 25415.4.1 Content Delivery Integrity Metrics 25415.4.2 Position Detection 25415.4.3 Protective Techniques 25515.5 Quantum Computing in Vehicular Networks 25715.5.1 Securing Automotive Ecosystems: A Challenge 25715.5.2 Generation of Quantum Random Numbers (QRNG) 25815.6 Quantum Key Transmission (QKD) 25815.7 Quantum Internet – A Future Vision 25915.7.1 Quantum Internet Applications 25915.7.2 Application Usage-Based Categorization 26015.8 Conclusions 262References 26316 Quantum Cryptography with an Emphasis on the Security Analysis of QKD Protocols 265Radhika Kavuri, Santhosh Voruganti, Sheena Mohammed, Sucharitha Inapanuri and B. Harish Goud16.1 Introduction 26616.2 Basic Terminology and Concepts of Quantum Cryptography 26716.2.1 Quantum Cryptography and Quantum Key Distribution 26716.2.2 Quantum Computing and Quantum Mechanics 26716.2.3 Post-Quantum Cryptography 26716.2.4 Quantum Entanglement 26716.2.5 Heisenberg’s Uncertainty Principle 26816.2.6 Qubits 26816.2.7 Polarization 26916.2.8 Traditional Cryptography vs. Quantum Cryptography 26916.3 Trends in Quantum Cryptography 27016.3.1 Global Quantum Key Distribution Links 27116.3.2 Research Statistics on Quantum Cryptography 27316.4 An Overview of QKD Protocols 27416.4.1 Introduction to the Prepare-and-Measure Protocols 27516.4.2 The BB84 Protocol 27516.4.3 B92 Protocol 27816.4.4 Six State Protocol (SSP) 27816.4.5 SARG04 Protocol 27916.4.6 Introduction to the Entanglement-Based Protocols 28016.4.7 The E91 Protocol 28016.4.8 The BBM92 Protocol 28016.5 Security Concerns in QKD 28216.6 Future Research Foresights 28416.6.1 Increase in Bit Rate 28416.6.2 Longer Distance Coverage 28416.6.3 Long Distance Quantum Repeaters 28516.6.4 Device Independent Quantum Cryptography 28516.6.5 Development of Tools for Simulation and Measurements 28516.6.6 Global Quantum Communication Network 28516.6.7 Integrated Photonic Spaced QKD 28516.6.8 Quantum Teleportation 286References 28617 Deep Learning-Based Quantum System for Human Activity Recognition 289Shoba Rani Salvadi, Narsimhulu Pallati and Madhuri T.17.1 Introduction 29017.2 Related Works 29217.3 Proposed Scheme 29317.3.1 Datasets Description 29417.3.2 Pre-Processing 29417.3.3 Feature Extraction 29517.3.4 Preliminaries 29517.3.4.1 Quantum Computing 29617.3.4.2 Convolutional Neural Networks 29617.3.5 Proposed ORQC-CNN Model 29617.3.5.1 Quantum Convolutional Layer 29717.3.5.2 Convolutional Layer 29917.3.5.3 Max-Pooling Layer 29917.3.5.4 Fully Connected Layer 29917.3.6 Parameter Selection Using Artificial Gorilla Troops Optimization Algorithm (AGTO) 30017.3.6.1 Exploration Phase 30117.3.6.2 Exploitation Phase 30217.3.6.3 Follow the Silverback 30317.3.6.4 Competition for Adult Females 30317.3.7 Computational Difficulty 30417.4 Results and Discussion 30417.4.1 Performance Measure 30517.4.2 Performance Analysis of Dataset 1 30617.4.3 Performance Analysis of Dataset 2 30717.4.4 Comparison 30817.5 Conclusion 309References 30918 Quantum Intelligent Systems and Deep Learning 313Bhagaban Swain and Debasis Gountia18.1 Introduction 31318.2 Quantum Support Vector Machine 31518.3 Quantum Principal Component Analysis 31818.4 Quantum Neural Network 31918.5 Variational Quantum Classifier 32118.6 Conclusion 323References 323Index 327