Book description
Work with fully explained algorithms and readytouse examples that can be run on quantum simulators and actual quantum computers with this comprehensive guide
Key Features
 Get a solid grasp of the principles behind quantum algorithms and optimization with minimal mathematical prerequisites
 Learn the process of implementing the algorithms on simulators and actual quantum computers
 Solve realworld problems using practical examples of methods
Book Description
This book provides deep coverage of modern quantum algorithms that can be used to solve realworld problems. You’ll be introduced to quantum computing using a handson approach with minimal prerequisites.
You’ll discover many algorithms, tools, and methods to model optimization problems with the QUBO and Ising formalisms, and you will find out how to solve optimization problems with quantum annealing, QAOA, Grover Adaptive Search (GAS), and VQE. This book also shows you how to train quantum machine learning models, such as quantum support vector machines, quantum neural networks, and quantum generative adversarial networks. The book takes a straightforward path to help you learn about quantum algorithms, illustrating them with code that’s ready to be run on quantum simulators and actual quantum computers. You’ll also learn how to utilize programming frameworks such as IBM’s Qiskit, Xanadu’s PennyLane, and DWave’s Leap.
Through reading this book, you will not only build a solid foundation of the fundamentals of quantum computing, but you will also become familiar with a wide variety of modern quantum algorithms. Moreover, this book will give you the programming skills that will enable you to start applying quantum methods to solve practical problems right away.
What you will learn
 Review the basics of quantum computing
 Gain a solid understanding of modern quantum algorithms
 Understand how to formulate optimization problems with QUBO
 Solve optimization problems with quantum annealing, QAOA, GAS, and VQE
 Find out how to create quantum machine learning models
 Explore how quantum support vector machines and quantum neural networks work using Qiskit and PennyLane
 Discover how to implement hybrid architectures using Qiskit and PennyLane and its PyTorch interface
Who this book is for
This book is for professionals from a wide variety of backgrounds, including computer scientists and programmers, engineers, physicists, chemists, and mathematicians. Basic knowledge of linear algebra and some programming skills (for instance, in Python) are assumed, although all mathematical prerequisites will be covered in the appendices.
Table of contents
 Handson Approach to Modern Quantum Algorithms
 Contributors
 Foreword
 Acknowledgements
 Table of Contents
 Preface
 Part I: I, for One, Welcome our New Quantum Overlords
 Chapter 1: Foundations of Quantum Computing
 Chapter 2: The Tools of the Trade in Quantum Computing
 Part II: When Time is Gold: Tools for Quantum Optimization
 Chapter 3: Working with Quadratic Unconstrained Binary Optimization Problems
 Chapter 4: Adiabatic Quantum Computing and Quantum Annealing
 Chapter 5: QAOA: Quantum Approximate Optimization Algorithm
 Chapter 6: GAS: Grover Adaptive Search
 Chapter 7: VQE: Variational Quantum Eigensolver
 Part III: A Match Made in Heaven: Quantum Machine Learning
 Chapter 8: What Is Quantum Machine Learning?
 Chapter 9: Quantum Support Vector Machines
 Chapter 10: Quantum Neural Networks
 Chapter 11: The Best of Both Worlds: Hybrid Architectures
 Chapter 12: Quantum Generative Adversarial Networks
 Part IV: Afterword and Appendices
 Chapter 13: Afterword: The Future of Quantum Computing
 Appendix A: Complex Numbers
 Appendix B: Basic Linear Algebra
 Appendix C: Computational Complexity
 Appendix D: Installing the Tools
 Appendix E: Production Notes

Assessments
 Chapter 1, Foundations of Quantum Computing
 Chapter 2, The Tools of the Trade in Quantum Computing
 Chapter 3, Working with Quadratic Unconstrained Binary Optimization Problems
 Chapter 4, Adiabatic Quantum Computing and Quantum Annealing
 Chapter 5, QAOA: Quantum Approximate Optimization Algorithm
 Chapter 6, GAS: Grover Adaptative Search
 Chapter 7, VQE: Variational Quantum Eigensolver
 Chapter 8, What is Quantum machine Learning?
 Chapter 9, Quantum Support Vector Machines
 Chapter 10, Quantum Neural Networks
 Chapter 11, The Best of Both Worlds: Hybrid Architectures
 Chapter 12, Quantum Generative Adversarial Networks
 Bibliography
 Index
 Other Books You May Enjoy
Product information
 Title: A Practical Guide to Quantum Machine Learning and Quantum Optimization
 Author(s):
 Release date: March 2023
 Publisher(s): Packt Publishing
 ISBN: 9781804613832
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