Introduction to Quantum Computing & Machine Learning Technologies: 1, #1

By M. Sreedevi, S. R. Jena and Vani Rajasekar

Quantum computing is a sophisticated approach to making parallel calculations, using the physics that governs subatomic particles to replace the more simplistic transistors in today's computers. Therefore it holds the promise to solve some of our planet's biggest challenges - in the areas of environment, agriculture, health, energy, climate, materials science, and others we haven't encountered yet. For some of these problems, classical computing is increasingly challenged as the size of the system grows. When designed to scale, quantum systems will presumably have some capabilities that exceed our most powerful supercomputers. As the global community of quantum researchers, scientists, engineers, and business leaders continue to collaborate to advance the quantum ecosystem, we expect to see quantum impact accelerate across every industry. Like the first digital computers, quantum computers offer the possibility of technology exponentially more powerful than current systems. They stand to change companies, entire industries, and the world by solving problems that seem impossible today. A recent report by Gartner states that by 2023, 20% of organizations will be budgeting for quantum computing projects. As this new technology develops, organizations will face a shortage of quantum computing experts.

 

The time to learn about quantum computing is now. Discover the business and technical implications of this new frontier in computing and how you can apply quantum computing to your organization is a greater challenge.

 

Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. It is undeniably one of the most influential and powerful technologies in today's world. More importantly, we are far from seeing its full potential. There's no doubt, it will continue to be making headlines for the foreseeable future.

 

Machine learning is a tool for turning information into knowledge. In the past 50 years, there has been an explosion of data. This mass of data is useless unless we analyse it and find the patterns hidden within. Machine learning techniques are used to automatically find the valuable underlying patterns within complex data that we would otherwise struggle to discover. The hidden patterns and knowledge about a problem can be used to predict future events and perform all kinds of complex decision making.

 

• Language: English
• Publisher: Soumya Ranjan Jena
• Release date: Jul 21, 2022
• ISBN: 9798201626808

Book preview

Introduction to Quantum Computing &

Machine Learning Technologies

M. Sreedevi

S. R. Jena Vani Rajasekar

PREFACE

Quantum computing is a sophisticated approach to making parallel calculations, using the physics that governs subatomic particles to replace the more simplistic transistors in today’s computers. Therefore it holds the promise to solve some of our planet's biggest challenges - in the areas of environment, agriculture, health, energy, climate, materials science, and others we haven't encountered yet. For some of these problems, classical computing is increasingly challenged as the size of the system grows. When designed to scale, quantum systems will presumably have some capabilities that exceed our most powerful supercomputers. As the global community of quantum researchers, scientists, engineers, and business leaders continue to collaborate to advance the quantum ecosystem, we expect to see quantum impact accelerate across every industry. Like the first digital computers, quantum computers offer the possibility of technology exponentially more powerful than current systems. They stand to change companies, entire industries, and the world by solving problems that seem impossible today. A recent report by Gartner states that by 2023, 20% of organizations will be budgeting for quantum computing projects. As this new technology develops, organizations will face a shortage of quantum computing experts.

––––––––

The time to learn about quantum computing is now. Discover the business and technical implications of this new frontier in computing and how you can apply quantum computing to your organization is a greater challenge.

Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. It is undeniably one of the most influential and powerful technologies in today’s world. More importantly, we are far from seeing its full potential. There’s no doubt, it will continue to be making headlines for the foreseeable future.

Machine learning is a tool for turning information into knowledge. In the past 50 years, there has been an explosion of data. This mass of data is useless unless we analyse it and find the patterns hidden within. Machine learning techniques are used to automatically find the valuable underlying patterns within complex data that we would otherwise struggle to discover. The hidden patterns and knowledge about a problem can be used to predict future events and perform all kinds of complex decision making.

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- Authors

About The Authors

Dr. M. Sreedevi is working as a Professor and HOD in Department of Computer Science and Technology at Madanapalle Institute of Technology and Science (UGC-Autonomous), Madanapalle, Andhra Pradesh, India. She has completed PhD in Network Security from S.V.U.C.E, S. V. University, Tirupati, M.Tech in IT from Punjabi University, Patiala and B.Tech in ECE from S.V.U.C.E, S. V. University, Tirupati. She has published more than 30 research papers in international journals and conferences and organized various international workshops and conferences. Her research interests include Cryptography and Network Security, Blockchain and Machine Learning technologies.

Mr. Soumya Ranjan Jena is currently working as an Assistant Professor in Department of Computer Science and Technology at Madanapalle Institute of Technology and Science (UGC-Autonomous), Madanapalle, Andhra Pradesh, India. He has completed M.Tech in IT from Utkal University, Bhubaneswar, Odisha, B.Tech in CSE from BPUT, Odisha and CCNA from CTTC. He has published 3 books, 12 Scopus indexed research papers, 23 WoS indexed research papers, and 5 patents. His research interests include Cloud and Distributed Computing, IoT, Green Computing etc.

Dr. Vani Rajasekar is currently working as Assistant Professor in Department of Computer Science and Engineering at Kongu Engineering College, Erode, Tamil Nadu, India. She has completed PhD in Information and Communication Engineering from Anna University, Tamil Nadu, M.Tech in Information and Cyber Warfare from Kongu Engineering College, Erode, Tamil Nadu, India and B.Tech in Information Technology from Kongu Engineering College, Erode, Tamil Nadu, India. She has published more than 40 research papers and book chapters published in various international journals and conferences which are indexed by Scopus, Web of Science and SCI. Her research interests include Cryptography, Network Security, Biometrics and Wireless Networks.

CONTENTS

Module 1: Quantum Computing

CHAPTER 1 QUANTUM CRYPTOGRAPHY 1-6

1.1  Introduction 1

1.2  Quantum Computing 1

1.3  QUbit 2

1.4  Techniques needed for Quantum Computing———————————2

1.5  Fundamentals of Quantum Cryptography————————————-2

1.6  Principles of Quantum Cryptography——————————————3

1.7  Two-Minute Drill—————————————————————-5

1.8  Key Terms 5

1.9  Review Questions 6

1.10  References 6

CHAPTER 2 QUANTUM KEY DISTRIBUTION 7-13

2.1  Introduction 7

2.2  QKD Techniques—————————————————-7

2.3  Quantum Key Exchange———————————————-8

2.4  Various QKD Networks 9

2.5  Security Attacks on QKD 10

2.6  Security Proofs on QKD 11

2.7  Counterfactual QKD 12

2.8  Two-Minute Drill 12

2.9  Key Terms 12

2.10  Review Questions 13

2.11  References 13

CHAPTER 3 CHALLENGES AND OPPORTUNITIES IN QUANTUM COMPUTING SYSTEMS  14-20

3.1  Introduction 14

3.2  Cloud Based Quantum Systems 15

3.3  QISKIT 15

3.4  Various Applications in Quantum near Term Systems——————-18

3.5  Two-Minute Drill 19

3.6  Key Words 19

3.7  Questions on Self-Assessment 19

3.8  References    20

CHAPTER 4 QUANTUM ERROR CORRECTION TECHNIQUES 21-25

4.1  Introduction 21

4.2  Quantum Entanglement 22

4.3  Post- Quantum Cryptography 22

4.4  Quantum Attacks 24

4.5  Two-Minute Drill 24

4.6  Key Words 25

4.7  Review Questions 25

4.8  References 25

Appendix A: Quantum Computing Job Oriented Short Type Questions and With Answers 26

Appendix B: Quantum Computing MCQs with Answers 30

Module 2: Machine Learning Technologies

CHAPTER 5 INTRODUCTION TO MACHINE LEARNING 36-40

5.1  What is Machine Learning? 36

5.2  Uses of Machine Learning   39 5.3 Selection of Machine Learning Model  39

5.4  Two-Minute Drill 40

5.5  Key Words 40

5.6  Review Questions 40

5.7  References 40

CHAPTER 6 SUPERVISED MACHINE LEARNING 41-47

6.1 Introduction   42 6.2 Process Involved in Supervised Learning  42

6.3  Regression 43

6.4  Polynomial Regression 45

6.5  Two-Minute Drill 46

6.6  Key Words 46

6.7  Review Questions 47

6.8  References 47

CHAPTER 7 CLASSIFICATION 48-53

7.1  Introduction 48

7.2  Types and Evaluation of Classification Algorithm———————————48

7.3  K-Nearest Neighbor (K-NN) Algorithm 49

7.4  Naive Bayes Classifier Algorithm 50

7.5  Two-Minute Drill 52

7.6  Key Words 52

7.7  Review Questions    52

7.8  References 53

CHAPTER 8 UNSUPERVISED MACHINE LEARNING 54-58

8.1  Introduction 54

8.2  Types of Unsupervised Learning 54

8.3  K-Means Clustering 55

8.4  Hierarchical Clustering 56

8.5  Association Rule Mining 57

8.6  Two-Minute Drill 58

8.7  Key Words 58

8.8  Review Questions 58

8.9  References    58

CHAPTER 9 REINFORCEMENT LEARNING 60-64

9.1  Introduction 60

9.2  Various Approaches Used In Reinforcement Learning—————————-61

9.3  Types of Reinforcement Learning 61

9.4  Markov Decision Process 62

9.5  Two-Minute Drill 63

9.6  Key Words 63

9.7  Review Questions 64

9.8  References 64

Appendix 3: Machine Learning Job Oriented Short Type Questions and With Answers 65

Appendix 4: Machine Learning MCQs with Answers 70

Quantum Computing Module

CHAPTER 1 QUANTUM CRYPTOGRAPHY

1.1  INTRODUCTION

Classical cryptography is primarily focused on mathematical algorithms, which are based on the assumption that it is simple to replicate relatively diverse prime numbers but incredibly difficult to perform prime factorization to identify the primes. These primes are needed for transmission encrypting and decrypting, which means that in order to eavesdrop on a transmission to determine the prime factorization. Classical cryptography's security depends on the unverifiable mathematical premise that finding the prime factors of a large integer is inefficient. Mathematicians were not