QC101 Quantum Computing and Introduction to Quantum Machine Learning

QC101 Quantum Computing and Introduction to Quantum Machine Learning

by Kumaresan Ramanathan
Released February 2021
Publisher(s): Packt Publishing
ISBN: 9781838989934

Read it now on the O’Reilly learning platform with a 10-day free trial.

O’Reilly members get unlimited access to books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.

Start your free trial

Video description

Quantum computing is a cutting-edge computing paradigm that utilizes the principles of quantum mechanics to perform computation. Unlike classical computers that rely on bits (0s and 1s) for data representation and processing, quantum computers use quantum bits or qubits. These qubits can exist in multiple states simultaneously, a property known as superposition.

The course content spans a comprehensive journey through the world of quantum computing and its applications. Covering topics from the fundamentals of high school-level math and physics to quantum cryptography, quantum gates, and quantum algorithms, the course offers a structured path for us to grasp the intricacies of this revolutionary field. With hands-on quantum programming using Microsoft Q# and IBM Qiskit, we will construct quantum circuits and delve into practical applications such as quantum cryptography and ML. Tackle complex unsolvable problems, fundamentally changing the computation landscape.

Upon completion, we will possess a strong foundation in quantum computing, including knowledge of quantum physics, mathematical concepts, practical quantum programming, cryptography, quantum gates, algorithms, and quantum machine learning. This knowledge equips us to apply quantum computing to real-world challenges and be at the forefront of this cutting-edge technology.

What You Will Learn

  • Acquire a solid understanding of quantum physics principles
  • Learn to handle complex numbers, linear algebra, probability, statistics
  • Simulate and run quantum programs using Microsoft Q# and IBM Qiskit
  • Explore quantum cryptography and the secure key-sharing BB84 protocol
  • Dive into quantum machine learning to apply advanced algorithms
  • Apply quantum computing and quantum algorithms such as Shor’s algorithm

Audience

The target audience for the course includes software professionals, technical managers, machine learning and AI professionals, and individuals with a keen interest in quantum computing. We will require a background in high school-level math and physics and an appreciation for these subjects. The course caters to those who want to delve into quantum computing, understand its practical applications, and potentially integrate quantum technology into their work or research. A strong foundation in high-school-level math and physics, along with an enthusiasm for these subjects is desirable.

About The Author

Kumaresan Ramanathan: Kumaresan Ramanathan is the Principal Architect at Coroman Systems. He is passionate about making technology easy to understand. He has taught students at the University of Massachusetts and guided software professionals at Cadence Design Systems, icons, Empirix, Relona, and Johnson & Johnson. His goal is to help you earn more than $200,000 annually as a software professional. He focuses on teaching AI and Quantum Computing because these are the highest-paid skills in the industry. His courses help beginners who have a basic understanding of high school math and coding.

In addition to teaching technical skills, Kumaresan also helps you build leadership ability. His courses discuss trade-offs between various technical choices and help you take wise decisions. As an expert software professional, you will be able to recommend solutions, suggest implementation choices, and guide software design.

He has an electrical engineering degree from IIT and a masters degree in computer science from the University of Massachusetts. He has managed software teams and helped startups launch products in international markets. He has lived most of his professional life in the Boston area. He enjoys reading science fiction and economic theory.

Table of contents

  1. Chapter 1 : Introduction
    1. Introduction
    2. How Is Quantum Computing Different?
  2. Chapter 2 : Quantum Physics Through Photon Polarization
    1. Quantum Physics Through Photon Polarization 1
    2. Quantum Physics Through Photon Polarization 2
    3. Quantum Physics Through Photon Polarization 3
    4. Quantum Physics Through Photon Polarization 4
    5. Quantum Physics Through Photon Polarization 5
    6. Quantum Physics Through Photon Polarization 6
    7. Quantum Physics Through Photon Polarization 7
    8. Quantum Physics Through Photon Polarization 8
    9. Quantum Physics Through Photon Polarization 9
    10. Quantum Physics Through Photon Polarization 10
    11. Quantum Physics Through Photon Polarization 11
    12. Quantum Physics Through Photon Polarization 12
    13. Quantum Physics Through Photon Polarization 13
    14. Quantum Physics Through Photon Polarization 14
  3. Chapter 3 : Math Foundation: Complex Numbers, Probability, Linear Algebra, and Logic
    1. Boolean Algebra
    2. Boolean Variables and Operators
    3. Truth Tables
    4. Logic Gates
    5. Logic Circuits
    6. AND Gate
    7. OR Gate
    8. NOT Gate
    9. Multiple Input Gates
    10. Equivalent Circuits 1
    11. Equivalent Circuits 2
    12. Universal Gate NAND
    13. Exclusive OR
    14. XOR for Assignment
    15. XOR of Bit Sequences 1
    16. XOR of Bit Sequences 2
    17. Introduction to Cryptography
    18. Cryptography with XOR
    19. Shared Secret
    20. Importance of Randomness
    21. Breaking the Code
    22. Introduction to Probability
    23. Probability of a Boolean Expression
    24. Mutually Exclusive Events
    25. Independent Events
    26. Manipulating Probabilities with Algebra
    27. P (Mutually Exclusive Events)
    28. P (Independent Events)
    29. Complete Set of MutEx Events
    30. P (A OR B)
    31. Examples
    32. Examples
    33. P (Bit Values)
    34. Analysis with Venn Diagrams
    35. Venn Diagram: P (A AND B)
    36. Venn Diagram: P (A OR B)
    37. Venn Diagram: P (NOT A)
    38. Examples
    39. Examples
    40. Conditional Probability
    41. Examples
    42. Introduction to Statistics
    43. Random Variables
    44. Mapping Random Variables
    45. Mean, Average, Expected Values
    46. Example
    47. Example
    48. Beyond Mean
    49. Standard Deviation
    50. Examples
    51. Combinations of Random Variables
    52. Correlation
    53. Analysis of Correlation
    54. Introduction to Complex Numbers
    55. Imaginary i
    56. Addition
    57. Subtraction
    58. Multiplication by a Real Number
    59. Division by a Real Number
    60. Complex Multiplication
    61. Examples
    62. Complex Conjugates
    63. Squared Magnitude
    64. Complex Division
    65. Examples
    66. Euler's Formula
    67. Polar Form
    68. Examples
    69. Fractional Powers
    70. Complex Cube Roots of 1
    71. Square Root of i
    72. 2D Coordinates
    73. Matrices
    74. Matrix Dimensions
    75. Matrix Addition
    76. Matrix Subtraction
    77. Scalar Multiplication
    78. Matrix Multiplication
    79. Examples
    80. Examples
    81. 3x3 Example
    82. Exercises
    83. More Multiplications
    84. When Is Multiplication Possible?
    85. Example
    86. Not Commutative
    87. Associative and Distributive
    88. Dimension of Result
    89. Odd-Shaped Matrices
    90. Examples
    91. Outer Product
    92. Exercise
    93. Inner Product
    94. Exercises
    95. Identity Matrix
    96. Matrix Inverse
    97. Transpose
    98. Transpose Examples
    99. Transpose of Product
    100. Complex Conjugate of Matrices
    101. Adjoint
    102. Unitary
    103. Hermitian
    104. Hermitian and Unitary
    105. Why Hermitian or Unitary?
    106. Vectors and Transformations
    107. Rotation in 2D
    108. Special Directions
    109. Eigenvectors and Eigenvalues
    110. More Eigenvectors
  4. Chapter 4 : Quantum Cryptography
    1. Photons
    2. Photon Polarization
    3. Experiments with Photon Polarization
    4. No-Cloning Theorem
    5. Encoding with XOR
    6. Encryption with Single-Use Shared-Secrets
    7. Encoding Data in Photon Polarization
    8. Making the Protocol Secure
    9. Exchanging Polarization Angles
    10. Why Is the BB84 protocol secure?
    11. Analysis
  5. Chapter 5 : Developing a Math Model for Quantum Physics
    1. Modeling Physics with Math
    2. Subtractive Probabilities Through Complex Numbers
    3. Modeling Superposition Through Matrices
    4. Overview of Math Model
  6. Chapter 6 : Quantum Physics of Spin States
    1. Introduction to Spin States
    2. Basis
    3. Column Matrix Representation of Quantum State
    4. State Vector
    5. Experiments with Spin 1
    6. Experiments with Spin 2
    7. Experiments with Spin 3
  7. Chapter 7 : Modeling Quantum Spin States with Math
    1. Analysis of Experiments 1
    2. Analysis of Experiments 2
    3. Analysis of Experiments 3
    4. Dirac Bra-Ket Notation 1
    5. Dirac Bra-Ket Notation 2
    6. More Experiment Analysis 1
    7. More Experiment Analysis 2
    8. On Random Behavior
  8. Chapter 8 : Reversible and Irreversible State Transformations
    1. Irreversible Transformations: Measurement
    2. Reversible State Transformations
  9. Chapter 9 : Multi-Qubit Systems
    1. Analyzing Multi-Qubit Systems
  10. Chapter 10 : Entanglement
    1. Entanglement
  11. Chapter 11 : Understanding Superposition and Entanglement with Quantum Simulators
    1. Installing Java and Running the Simulators
    2. Launching the Superposition Simulator
    3. Classical Photon
    4. Quantum Photon
    5. No Cloning
    6. No Cloning
    7. Measurement Is Irreversible
    8. Deterministic Versus Probabilistic
    9. Running the Simulator
    10. Superposition 1
    11. Superposition 2
    12. Measurement and Superposition
    13. Two Photon Systems
    14. Entanglement
    15. Simulating Entanglement 1
    16. Simulating Entanglement 2
    17. Simulating Entanglement 3
    18. Simulating Entanglement 4
    19. Independent Photons
    20. Effect of Measurement
  12. Chapter 12 : Quantum Computing Model
    1. Quantum Circuits
    2. Fanout
    3. Uncomputing
    4. Reversible Gates
    5. Quantum NOT
    6. Other Single-Qubit Gates
    7. CNOT Gate
    8. CCNOT: Toffoli Gate
    9. Universal Gate
    10. Fredkin Gate
    11. Effects of Superposition and Entanglement on Quantum Gates
  13. Chapter 13 : Quantum Programming with Microsoft Q#
    1. Installing Q#
    2. Q# Simulation Architecture
    3. Q# Controller
    4. Q# Execution Model
    5. Measuring Superposition States
    6. Overview of 4-Qubit Simulation Framework
    7. Set Operation
    8. Iterative Measurement
    9. Verifying Output after Initialization - 1
    10. Verifying Output after Initialization - 2
    11. NOT Operation
    12. Superposition
    13. SWAP
    14. CNOT
    15. Significance of Superposition and Entanglement
    16. Effect of Superposition on Quantum Gates
    17. Toffoli Gate: General Configuration
    18. Toffoli Configured as NOT
    19. Toffoli Configured as AND
    20. Toffoli Configured as Fanout
  14. Chapter 14 : IBM Quantum Experience
    1. IBM Quantum Experience
  15. Chapter 15 : Quantum Programming and Algorithms with IBM Qiskit
    1. What Is Qiskit?
    2. Installing Python and Qiskit
    3. Interactive Python
    4. Jupyter Notebooks
    5. Spyder Python IDE
    6. Variables and Assignment
    7. Data Types
    8. Operators
    9. Type Conversion
    10. Strings
    11. Lists
    12. Dictionaries
    13. Loops
    14. Decisions
    15. Functions
    16. Object-Oriented Programming
    17. Exceptions
    18. Modules
    19. Quantum Circuits 1
    20. Quantum Circuits 2
    21. Quantum Circuits 3
    22. Quantum Circuits 4
    23. Quantum Circuits 5
    24. Running a Circuit
    25. Circuit Matrix
    26. Implementing BB84 Cryptography
    27. Shor's Algorithm
  16. Chapter 16 : Machine Learning Foundation
    1. Introduction to Machine Learning
    2. What Is AI?
    3. Structure of ML Systems
    4. Learning with Models
    5. Speed Up Learning
    6. Underfit and Overfit
    7. Classification
    8. Sigmoid Models
    9. Regularization 1
    10. Regularization 2
    11. Machine Learning Libraries
    12. Machine Learning Coding
    13. Multi-Layer Network 1
    14. Multi-Layer Network 2
    15. Convolution 1
    16. Convolution 2
    17. Convolution 3
    18. Recurrent
  17. Chapter 17 : Quantum Machine Learning with Qiskit
    1. Quantum Machine Learning with KNN
    2. KNN Problem Description
    3. Code for Classical KNN
    4. Code for Quantum KNN
    5. Math for Classical KNN
    6. Math Prerequisites for Quantum KNN
    7. Math for Quantum KNN
    8. Connecting Math and Code for Classical KNN
    9. Connecting Math and Code for Quantum KNN
    10. Introduction to Classification
    11. Support Vector Machines - Separation
    12. Support Vector Machines - Overfitting
    13. Support Vector Machines - Soft Margins
    14. Support Vector Machines - Higher Dimensions and Kernels
    15. Support Vector Machines - Multiple Classes
    16. Quantum Support Vector Machines
    17. Significance of Quantum Machine Learning

Product information

  • Title: QC101 Quantum Computing and Introduction to Quantum Machine Learning
  • Author(s): Kumaresan Ramanathan
  • Release date: February 2021
  • Publisher(s): Packt Publishing
  • ISBN: 9781838989934