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A Practical Guide to Quantum Machine Learning and Quantum Optimization Cover

A Practical Guide to Quantum Machine Learning and Quantum Optimization

Hands-on Approach to Modern Quantum Algorithms

Publisher:Packt Publishing Limited
By: ,   and    
Paid access
|May 2024
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Description

Work with fully explained algorithms and ready-to-use 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 real-world problems using practical examples of methods

Book Description

This book provides deep coverage of modern quantum algorithms that can be used to solve real-world problems. You’ll be introduced to quantum computing using a hands-on 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 D-Wave’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

  1. Foundations of Quantum Computing
  2. The Tools of the Trade in Quantum Computing
  3. Working with Quadratic Unconstrained Binary Optimization Problems
  4. Adiabatic Quantum Computing and Quantum Annealing
  5. QAOA: Quantum Approximate Optimization Algorithm
  6. GAS: Grover Adaptative Search
  7. VQE: Variational Quantum Solver
  8. What is Quantum Machine Learning?
  9. Quantum Support Vector Machines
  10. Quantum Neural Networks
  11. The Best of Both Worlds: Hybrid Architectures
  12. Quantum Generative Adversarial Networks
  13. Afterword: The Future of Quantum Computing
  14. Complex Numbers
  15. Basic Linear Algebra
  16. Computational Complexity
  17. Installing the Tools
  18. Production Notes
https://github.com/PacktPublishing/A-Practical-Guide-to-Quantum-Machine-Learning-and-Quantum-Optimization
PDF ISBN: 978-1-80461-830-1
Publisher: Packt Publishing Limited
Copyright owner: © 2023 Packt Publishing Limited
Publication date: 2024
Language: English
Pages: 680
Related subjects:
Computer sciences,
Fundamentals of computer sciences
BISAC subjects:
COM000000,
COM051360,
COM059000
Thema subjects:
UB,
UMX,
UY,
TJF

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