Quantum Machine Learning with Python: Using Cirq from Google Research and IBM Qiskit

by Santanu Pattanayak

Overview

Quickly scale up to Quantum computing and Quantum machine learning foundations and related mathematics and expose them to different use cases that can be solved through Quantum based algorithms.This book explains Quantum Computing, which leverages the Quantum mechanical properties sub-atomic particles. It also examines Quantum machine learning, which can help solve some of the most challenging problems in forecasting, financial modeling, genomics, cybersecurity, supply chain logistics, cryptography among others.

You'll start by reviewing the fundamental concepts of Quantum Computing, such as Dirac Notations, Qubits, and Bell state, followed by postulates and mathematical foundations of Quantum Computing. Once the foundation base is set, you'll delve deep into Quantum based algorithms including Quantum Fourier transform, phase estimation, and HHL (Harrow-Hassidim-Lloyd) among others.

You'll then be introduced to Quantum machine learning and Quantum deep learning-based algorithms, along with advanced topics of Quantum adiabatic processes and Quantum based optimization. Throughout the book, there are Python implementations of different Quantum machine learning and Quantum computing algorithms using the Qiskit toolkit from IBM and Cirq from Google Research.

What You'll Learn

• Understand Quantum computing and Quantum machine learning
• Explore varied domains and the scenarios where Quantum machine learning solutions can be applied
• Develop expertise in algorithm development in varied Quantum computing frameworks
• Review the major challenges of building large scale Quantum computers and applying its various techniques

Who This Book Is For

Machine Learning enthusiasts and engineers who want to quickly scale up to Quantum Machine Learning

Product Details

ISBN-13:	9781484265215
Publisher:	Apress
Publication date:	03/13/2021
Edition description:	1st ed.
Pages:	361
Product dimensions:	7.01(w) x 10.00(h) x (d)

About the Author

Santanu Pattanayak works as a staff machine learning specialist at Qualcomm Corp R&D and is an author of the book “Pro Deep Learning with TensorFlow” published by Apress. He has around 12 years of work experience and has worked at GE, Capgemini, and IBM before joining Qualcomm. He graduated with a degree in electrical engineering from Jadavpur University, Kolkata and is an avid math enthusiast. Santanu has a master’s degree in data science from Indian Institute of Technology (IIT), Hyderabad. He also participates in Kaggle competitions in his spare time where he ranks in top 500. Currently, he resides in Bangalore with his wife.

Table of Contents

Quantum Machine Learning With Python

Chapter 1: Introduction to Quantum Mechanics and Quantum Computing

Chapter Goal: Introduce the concept of Quantum mechanics and Quantum computing to the readers

No of pages 50-60

Sub-Topics

1. Introduction to Quantum computing

2. Quantum bit and its realization

3. Quantum superposition and Quantum entanglement

4. Bloch Sphere representation of Qubit

5. Stern Gerlach Experiment

6. Bell State

7. Dirac Notations

8. Single Qubit Gates

9. Multiple Qubit Gates

10. Quantum No Cloning Theorem

11. Measurement in different basis

12. Quantum Teleportation

13. Quantum parallelism with Deuth Jozsa

14. Reversibility of quantum computing

Chapter 2: Mathematical Foundations and Postulates of Quantum Computing

Chapter Goal: Lays the mathematical foundation along with the postulates of Quantum computing

No of pages 50-60

Sub -Topics

1. Topics from Linear algebra

2. Pauli Operators

3. Linear Operators and their properties

4. Hermitian Operators

5. Normal Operators

6. Unitary Operators

7. Spectral Decomposition

8. Linear Operators on Tensor Product of Vectors

9. Exponential Operator

10. Commutator Anti commutator Operator

11. Postulates of Quantum Mechanics

12. Measurement Operators

13. Heisenberg Uncertainty Principle

14. Density Operators and Mixed States

15. Solovay-Kitaev Theorem and Universality of Quantum gates

Chapter 3: Introduction to Quantum Algorithms

Chapter Goal: Introduce to the readers Quantum algorithms to express the Quantum computing supremacy over classical computation

No of pages: 70-80

Sub - Topics:

1. Introduction to Cirq and Qiskit

2. Bell State creation and measurement in Cirq and qiskit

3. Quantum teleportation Implementation

4. Quantum Random Number generator

5. Deutsch Jozsa Implementation

8. Hadamard Sampling

6. Bernstein Vajirani Algorithm Implementation

7. Bell’s Inequality Implementation

8. Simon’s Algorithm of secret string search Implementation

9 Grover’s Algorithm Implementation

10. Algorithmic complexity in Quantum and Classical computing paradigm

Chapter 4: Quantum Fourier Transform Related Algorithms

Goal: Introduce to the readers Quantum Fourier related algorithms

No of pages: 60-70

Sub - Topics:

1. Fourier Series

2. Fourier Transform

3. Discrete Fourier Transform

4. Quantum Fourier Transform(QFT)

5. QFT implementation

6. Hadamard Transform as Fourier Transform

7. Quantum Phase Estimation(QPE)

8. Quantum Phase Estimation Implementation

9. Error Analysis in Quantum Phase Estimation

10. Shor’s Period Finding Algorithm and Factoring

11. Period Finding Implementation

12. Prime Factoring and Implementation

PART 2

Chapter 5: Introduction to Quantum Machine Learning

Goal: Introduce to the readers Quantum machine learning paradigm

No of pages: 60-70

Sub - Topics:

1. Harrow, Hassidim and Lloyd Algorithm (HHL) for solving Linear Equation

2. HHL algorithm Implementation

3. Quantum Linear Regression and Implementation

4. Quantum SWAP Test for dot product Computation

5. Quantum SWAP Test Implementation

6. Quantum Amplitude Scaling

7. Quantum Euclidean Distance Computation

8. Quantum Euclidean Distance Implementation

9. Quantum K means

10. Quantum K means Implementation

11. Quantum Random Access Memory(QRAM)

12. Quantum Principle Component Analysis

13. Quantum Support Vector Machines

14. Quantum Least Square Support Vector Machines(LS -SVM)

15. Least Square SVM Implementation

Chapter 6: Quantum Deep Learning and Quantum Optimization Based Algorithms

Goal: Introduce to the readers Quantum deep learning algorithms and Quantum Optimization Based Algorithms

No of pages: 40-50

Sub - Topics:

1. Quantum Neural network and Implementation

2. Quantum Convolutional Neural Network and Implementation

3. Variational Quantum Eigen solvers(VQE)

4. Graph Coloring Problem using VQE

5. Travelling Salesman problem using VQE

Chapter 7: Quantum Adiabatic Processes and Quantum based Optimization.

Editorial Reviews

The rigor, the mathematical detail, and the inclusion of proofs are very important contributions … . the book is well written and easy to read. Concepts, ideas, and algorithms are very well illustrated with simple examples but then also explained in exquisite mathematical detail, followed by concise yet nicely explained codification in Cirq or Qiskit.” (Santiago Escobar, Computing Reviews, October 28, 2021)

From the Publisher