Evolution and Applications of Quantum Computing

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.

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.

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.

Sachi Nandan Mohanty, PhD, received his doctorate from IIT Kharagpur, India in 2015 and did his post-doc work at IIT Kanpur.  His research areas include data mining, big data analysis, cognitive science, fuzzy decision-making, brain-computer interface, cognition, and computational intelligence. He has received 3 Best Paper Awards and the Best Thesis award first prize by the Computer Society of India in the year 2015. He has published more than hundred research paper in international journals and has been elected as Fellow of the Institute of Engineers and Senior member of IEEE Computer Society Hyderabad chapter.

RAJANIKANTH ALUVALU, PhD is an associate professor in the Department of IT, CBIT, Hyderabad. He is a senior Member IEEE. 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 area includes digital forensics and cybersecurity. She has more than 10 years of teaching experience. 

Preface xvii

1 Introduction to Quantum Computing 1
V. Padmavathi, C. N. Sujatha, V. Sitharamulu, K. Sudheer Reddy and A. Mallikarjuna Reddy

1.1 Quantum Computation 2

1.2 Importance of Quantum Mechanics 2

1.3 Security Options in Quantum Mechanics 2

1.4 Quantum States and Qubits 3

1.5 Quantum Mechanics Interpretation 4

1.6 Quantum Mechanics Implementation 4

1.6.1 Photon Polarization Representation 4

1.7 Quantum Computation 6

1.7.1 Quantum Gates 7

1.8 Comparison of Quantum and Classical Computation 11

1.9 Quantum Cryptography 12

1.10 Qkd 12

1.11 Conclusion 12

References 13

2 Fundamentals of Quantum Computing and Significance of Innovation 15
Swapna Mudrakola, Uma Maheswari V., Krishna Keerthi Chennam and MVV Prasad Kantidpudi

2.1 Quantum Reckoning Mechanism 16

2.2 Significance of Quantum Computing 16

2.3 Security Opportunities in Quantum Computing 16

2.4 Quantum States of Qubit 17

2.5 Quantum Computing Analysis 17

2.6 Quantum Computing Development Mechanism 18

2.7 Representation of Photon Polarization 18

2.8 Theory of Quantum Computing 20

2.9 Quantum Logical Gates 21

2.9.1 I-Qubit GATE 21

2.9.2 Hadamard-GATE 22

2.9.3 NOT_GATE_QUANTUM or Pauli_X-GATE 22

2.9.3.1 Pauli_Y-GATE 23

2.9.3.2 Pauli_Z-GATE 23

2.9.3.3 Pauli_S-Gate 23

2.9.4 Two-Qubit GATE 24

2.9.5 Controlled NOT(C-NOT) 24

2.9.6 The Two-Qubits are Swapped Using SWAP_GATE 24

2.9.7 C-Z-GATE (Controlled Z-GATE) 24

2.9.8 C-P-GATE (Controlled-Phase-GATE) 25

2.9.9 Three-Qubit Quantum GATE 25

2.9.9.1 GATE: Toffoli Gate 25

2.9.10 F-C-S GATE (Fredkin Controlled Swap-GATE) 26

2.10 Quantum Computation and Classical Computation Comparison 27

2.11 Quantum Cryptography 27

2.12 Quantum Key Distribution – QKD 27

2.13 Conclusion 28

References 28

3 Analysis of Design Quantum Multiplexer Using CSWAP and Controlled-R Gates 31
Virat Tara, Navneet Sharma, Pravindra Kumar and Kumar Gautam

3.1 Introduction 32

3.2 Mathematical Background of Quantum Circuits 34

3.2.1 Hadamard Gate 34

3.2.2 CSWAP Gates 35

3.2.3 Controlled-R Gates 36

3.3 Methodology of Designing Quantum Multiplexer (QMUX) 36

3.3.1 QMUX Using CSWAP Gates 36

3.3.1.1 Generalization 37

3.3.2 QMUX Using Controlled-R Gates 37

3.4 Analysis and Synthesis of Proposed Methodology 39

3.5 Complexity and Cost of Quantum Circuits 41

3.6 Conclusion 42

References 42

4 Artificial Intelligence and Machine Learning Algorithms in Quantum Computing Domain 45
Syed Abdul Moeed, P. Niranjan and G. Ashmitha

4.1 Introduction 46

4.1.1 Quantum Computing Convolutional Neural Network 51

4.2 Literature Survey 52

4.3 Quantum Algorithms Characteristics Used in Machine Learning Problems 58

4.3.1 Minimizing Quantum Algorithm 58

4.3.2 K-NN Algorithm 58

4.3.3 K-Means Algorithm 60

4.4 Tree Tensor Networking 61

4.5 TNN Implementation on IBM Quantum Processor 62

4.6 Neurotomography 62

4.7 Conclusion and Future Scope 63

References 64

5 Building a Virtual Reality-Based Framework for the Education of Autistic Kids 67
Kanak Pandit, Aditya Mogare, Achal Shah, Prachi Thete and Megharani Patil

5.1 Introduction 68

5.2 Literature Review 71

5.3 Proposed Work 74

5.3.1 Methodology 74

5.3.2 Work Flow of Neural Style Transfer 75

5.3.3 A-Frame 75

5.3.3.1 Setting Up the Virtual World and Adding Components 75

5.3.3.2 Adding Interactivity Through Raycasting 76

5.3.3.3 Animating the Components 77

5.3.4 Neural Style Transfer 78

5.3.4.1 Choosing the Content and Styling Image 79

5.3.4.2 Image Preprocessing and Generation of a Random Image 79

5.3.4.3 Model Design and Extraction of Content and Style 81

5.3.4.4 Loss Calculation 81

5.3.4.5 Model Optimization 84

5.4 Evaluation Metrics 86

5.5 Results 89

5.5.1 A-Frame 89

5.5.2 Neural Style Transfer 90

5.6 Conclusion 90

References 91

6 Detection of Phishing URLs Using Machine Learning and Deep Learning Models Implementing a URL Feature Extractor 93
Abishek Mahesh, Prithvi Seshadri, Shruti Mishra and Sandeep Kumar Satapathy

6.1 Introduction 94

6.2 Related Work 94

6.3 Proposed Model 95

6.3.1 URL Feature Extractor 95

6.3.2 Dataset 103

6.3.3 Methodologies 104

6.3.3.1 AdaBoost Classifier 105

6.3.3.2 Gradient Boosting Classifier 105

6.3.3.3 K-Nearest Neighbors 105

6.3.3.4 Logistic Regression 106

6.3.3.5 Artificial Neural Networks 106

6.3.3.6 Support Vector Machines (SVM) 107

6.3.3.7 Naïve Bayes Classifier 107

6.4 Results 109

6.5 Conclusions 109

References 109

7 Detection of Malicious Emails and URLs Using Text Mining 111
Heetakshi Fating, Aditya Narawade, Sandeep Kumar Satapathy and Shruti Mishra

7.1 Introduction 112

7.2 Related Works 112

7.3 Dataset Description 114

7.4 Proposed Architecture 115

7.5 Methodology 116

7.5.1 Methodology for the URL Dataset 116

7.5.2 Methodology for the Email Dataset 118

7.5.2.1 Overcoming the Overfitting Problem 118

7.5.2.2 Tokenization 119

7.5.2.3 Applying Machine Learning Algorithms 119

7.5.3 Detecting Presence of Malicious URLs in Otherwise Non-Malicious Emails 119

7.5.3.1 Preparation of Dataset 119

7.5.3.2 Creation of Features 120

7.5.3.3 Applying Machine Learning Algorithms 120

7.6 Results 120

7.6.1 URL Dataset 120

7.6.2 Email Dataset 121

7.6.3 Final Dataset 121

7.7 Conclusion 122

References 122

8 Quantum Data Traffic Analysis for Intrusion Detection System 125
Anshul Harish Khatri, Vaibhav Gadag, Simrat Singh, Sandeep Kumar Satapathy and Shruti Mishra

8.1 Introduction 126

8.2 Literature Overview 127

8.3 Methodology 129

8.3.1 Autoviz 129

8.3.2 Dataset 132

8.3.3 Proposed Models 132

8.3.3.1 Decision Tree 135

8.3.3.2 Random Forest Classifier Algorithm 136

8.3.3.3 AdaBoost Classifier 136

8.3.3.4 Ridge Classifier 137

8.3.3.5 Logistic Regression 137

8.3.3.6 SVM-Linear Kernel 138

8.3.3.7 Naive Bayes 138

8.3.3.8 Quadratic Discriminant Analysis 139

8.4 Results 140

8.5 Conclusion 141

References 142

9 Quantum Computing in Netnomy: A Networking Paradigm in e-Pharmaceutical Setting 145
Sarthak Dash, Sugyanta Priyadarshini, Sachi Nandan Mohanty, Sukanya Priyadarshini and Nisrutha Dulla

9.1 Introduction 146

9.2 Discussion 148

9.2.1 Exploring Market Functioning via Quantum Network Economy 148

9.2.1.1 Internal Networking Marketing 149

9.2.1.2 Layered Marketing 149

9.2.1.3 Role of Marketing in Pharma Network Organizations 150

9.2.1.4 Role of Marketing in Vertical Networking Organizations 152

9.2.1.5 Generic e-Commerce Entity Model in Pharmaceutical Industry 153

9.2.2 Analyzing the Usability of Quantum Netnomics in Attending Economic Development 154

9.2.2.1 Theory of 4Ps in Pharma Marketing mix 155

9.2.2.2 Buying Behavior of the e-Consumers 156

9.2.2.3 Maintaining of Privacy and Security via Quantum Technology in e-Structure 157

9.2.2.4 Interface Influencing Sales 157

9.3 Results 158

9.4 Conclusion 159

References 159

10 Machine Learning Approach in the Indian Service Industry: A Case Study on Indian Banks 163
Pragati Priyadarshinee

10.1 Introduction 163

10.2 Literature Survey 164

10.3 Experimental Results 170

10.4 Conclusion 172

References 172

11 Accelerating Drug Discovery with Quantum Computing 175
Mahesh V. and Shimil Shijo

11.1 Introduction 175

11.2 Working Nature of Quantum Computers 176

11.3 Use Cases of Quantum Computing in Drug Discovery 178

11.4 Target Drug Identification and Validation 179

11.5 Drug Discovery Using Quantum Computers is Expected to Start by 2030 179

11.6 Conclusion 180

References 181

12 Problems and Demanding Situations in Traditional Cryptography: An Insistence for Quantum Computing to Secure Private Information 183
D. DShivaprasad, Mohamed Sirajudeen Yoosuf, P. Selvaramalakshmi, Manoj A. Patil and Dasari Promod Kumar

12.1 Introduction to Cryptography 184

12.1.1 Confidentiality 184

12.1.2 Authentication 185

12.1.3 Integrity 185

12.1.4 Non-Repudiation 186

12.2 Different Types of Cryptography 186

12.2.1 One-Way Processing 186

12.2.1.1 Hash Function (One-Way Processing) 186

12.2.2 Two-Way Processing 187

12.2.2.1 Symmetric Cryptography 188

12.2.2.2 Asymmetric Cryptography 189

12.2.3 Algorithms Types 190

12.2.3.1 Stream Cipher 190

12.2.3.2 Block Cipher 191

12.2.4 Modes of Algorithm 192

12.2.4.1 Cipher Feedback Mode 192

12.2.4.2 Output Feedback Mode 192

12.2.4.3 Cipher Block Chaining Mode 192

12.2.4.4 Electronic Code Book 192

12.3 Common Attacks 193

12.3.1 Passive Attacks 193

12.3.1.1 Traffic Analysis 193

12.3.1.2 Eavesdropping 194

12.3.1.3 Foot Printing 195

12.3.1.4 War Driving 195

12.3.1.5 Spying 195

12.3.2 Active Attacks 196

12.3.2.1 Denial of Service 196

12.3.2.2 Distributed Denial of Service (DDOS) 197

12.3.2.3 Message Modification 197

12.3.2.4 Masquerade 197

12.3.2.5 Trojans 198

12.3.2.6 Replay Attacks 199

12.3.3 Programming Weapons for the Attackers 199

12.3.3.1 Dormant Phase 200

12.3.3.2 Propagation Phase 200

12.3.3.3 Triggering Phase 201

12.3.3.4 Execution Phase 201

12.4 Recent Cyber Attacks 201

12.5 Drawbacks of Traditional Cryptography 203

12.5.1 Cost and Time Delay 203

12.5.2 Disclosure of Mathematical Computation 203

12.5.3 Unsalted Hashing 204

12.5.4 Attacks 204

12.6 Need of Quantum Cryptography 204

12.6.1 Quantum Mechanics 204

12.7 Evolution of Quantum Cryptography 205

12.8 Conclusion and Future Work 205

References 205

13 Identification of Bacterial Diseases in Plants Using Re-Trained Transfer Learning in Quantum Computing Environment 207
Sri Silpa Padmanabhuni, B. Srikanth Reddy, A. Mallikarjuna Reddy and K. Sudheer Reddy

13.1 Introduction 208

13.2 Literature Review 218

13.3 Proposed Methodology 220

13.3.1 SVM Classifier 222

13.3.2 Random Forest to Classify the Rice Leaf 223

13.3.2.1 Image Pre-Processing 223

13.3.2.2 Feature Extraction 223

13.3.2.3 Classification 224

13.4 Experiment Results 226

Conclusion 230

References 230

14 Quantum Cryptography 233
Salma Fauzia

14.1 Fundamentals of Cryptography 234

14.2 Principle of Quantum Cryptography 237

14.2.1 Quantum vs. Conventional Cryptography 237

14.3 Quantum Key Distribution Protocols 238

14.3.1 Overview and BB84 Protocol 238

14.3.2 The B92 Protocol 240

14.3.3 E91 Protocol 241

14.3.4 SARG04 Protocol 243

14.4 Impact of the Sifting and Distillation Steps on the Key Size 243

14.5 Cryptanalysis 246

14.6 Quantum Key Distribution in the Real World 247

References 248

15 Security Issues in Vehicular Ad Hoc Networks and Quantum Computing 249
B. Veera Jyothi, L. Suresh Kumar and B. Surya Samantha

15.1 Introduction 250

15.2 Overview of VANET Security 250

15.2.1 Security of VANET 250

15.2.2 Attacks are Classified 251

15.3 Architectural and Systematic Security Methods 252

15.3.1 Solutions for Cryptography 252

15.3.2 Framework for Trust Groups 252

15.3.3 User Privacy Security System Based on ID 253

15.4 Suggestions on Particular Security Challenges 254

15.4.1 Content Delivery Integrity Metrics 254

15.4.2 Position Detection 254

15.4.3 Protective Techniques 255

15.5 Quantum Computing in Vehicular Networks 257

15.5.1 Securing Automotive Ecosystems: A Challenge 257

15.5.2 Generation of Quantum Random Numbers (QRNG) 258

15.6 Quantum Key Transmission (QKD) 258

15.7 Quantum Internet – A Future Vision 259

15.7.1 Quantum Internet Applications 259

15.7.2 Application Usage-Based Categorization 260

15.8 Conclusions 262

References 263

16 Quantum Cryptography with an Emphasis on the Security Analysis of QKD Protocols 265
Radhika Kavuri, Santhosh Voruganti, Sheena Mohammed, Sucharitha Inapanuri and B. Harish Goud

16.1 Introduction 266

16.2 Basic Terminology and Concepts of Quantum Cryptography 267

16.2.1 Quantum Cryptography and Quantum Key Distribution 267

16.2.2 Quantum Computing and Quantum Mechanics 267

16.2.3 Post-Quantum Cryptography 267

16.2.4 Quantum Entanglement 267

16.2.5 Heisenberg’s Uncertainty Principle 268

16.2.6 Qubits 268

16.2.7 Polarization 269

16.2.8 Traditional Cryptography vs. Quantum Cryptography 269

16.3 Trends in Quantum Cryptography 270

16.3.1 Global Quantum Key Distribution Links 271

16.3.2 Research Statistics on Quantum Cryptography 273

16.4 An Overview of QKD Protocols 274

16.4.1 Introduction to the Prepare-and-Measure Protocols 275

16.4.2 The BB84 Protocol 275

16.4.3 B92 Protocol 278

16.4.4 Six State Protocol (SSP) 278

16.4.5 SARG04 Protocol 279

16.4.6 Introduction to the Entanglement-Based Protocols 280

16.4.7 The E91 Protocol 280

16.4.8 The BBM92 Protocol 280

16.5 Security Concerns in QKD 282

16.6 Future Research Foresights 284

16.6.1 Increase in Bit Rate 284

16.6.2 Longer Distance Coverage 284

16.6.3 Long Distance Quantum Repeaters 285

16.6.4 Device Independent Quantum Cryptography 285

16.6.5 Development of Tools for Simulation and Measurements 285

16.6.6 Global Quantum Communication Network 285

16.6.7 Integrated Photonic Spaced QKD 285

16.6.8 Quantum Teleportation 286

References 286

17 Deep Learning-Based Quantum System for Human Activity Recognition 289
Shoba Rani Salvadi, Narsimhulu Pallati and Madhuri T.

17.1 Introduction 290

17.2 Related Works 292

17.3 Proposed Scheme 293

17.3.1 Datasets Description 294

17.3.2 Pre-Processing 294

17.3.3 Feature Extraction 295

17.3.4 Preliminaries 295

17.3.4.1 Quantum Computing 296

17.3.4.2 Convolutional Neural Networks 296

17.3.5 Proposed ORQC-CNN Model 296

17.3.5.1 Quantum Convolutional Layer 297

17.3.5.2 Convolutional Layer 299

17.3.5.3 Max-Pooling Layer 299

17.3.5.4 Fully Connected Layer 299

17.3.6 Parameter Selection Using Artificial Gorilla Troops Optimization Algorithm (AGTO) 300

17.3.6.1 Exploration Phase 301

17.3.6.2 Exploitation Phase 302

17.3.6.3 Follow the Silverback 303

17.3.6.4 Competition for Adult Females 303

17.3.7 Computational Difficulty 304

17.4 Results and Discussion 304

17.4.1 Performance Measure 305

17.4.2 Performance Analysis of Dataset 1 306

17.4.3 Performance Analysis of Dataset 2 307

17.4.4 Comparison 308

17.5 Conclusion 309

References 309

18 Quantum Intelligent Systems and Deep Learning 313
Bhagaban Swain and Debasis Gountia

18.1 Introduction 313

18.2 Quantum Support Vector Machine 315

18.3 Quantum Principal Component Analysis 318

18.4 Quantum Neural Network 319

18.5 Variational Quantum Classifier 321

18.6 Conclusion 323

References 323

Index 327

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