A Practical Guide to Quantum Machine Learning and Quantum Optimization

Machine Learning utilizing modern quantum algorithms and writing codes that can work with actual quantum computers.

Key Features

• Gain a solid understanding of the principles behind quantum algorithms with minimal mathematics
• Understand the process of implementing and running the algorithms on simulators and actual quantum computers
• This book gives practical examples of methods that can be used to solve real-world quantum problems

Book Description

This book provides a deep coverage of modern quantum algorithms for real-world problems, including machine learning and optimization. It will introduce the reader to quantum computing with a hands-on approach: reducing to the very minimum the mathematical and physical concepts needed to understand the material. Its content will cover many protocols and methods, from basic ones like quantum key distribution and quantum teleportation, to more advanced ones like Shor's algorithm, quantum optimisation and quantum neural networks. The book will present algorithms in a clear and straightforward way, illustrating them with code that is ready to be run on quantum simulators and actual quantum computers. The programming languages that will be used in the book include IBM's Qiskit, Xanadu's Pennylane and D-Wave's Leap. How to run these programs on actual quantum computers will also be covered in the text.

As learning outcomes, readers will obtain a solid grasp on the fundamentals of quantum computing, a deep understanding of wide variety of quantum algorithms, and programming skills that will allow them to start applying quantum methods to practical problems.

What you will learn

• Gain a solid understanding of modern quantum algorithms
• Learn and understand new advances in quantum algorithms
• Learn what is the QUBO, QAOA, and HOBO model
• Construct quantum circuits used to solve optimization problems
• Understand how quantum neural networks work using Qiskit and Pennylane
• Implement hybrid architectures using Qiskit/Pennylane and its PyTorch interface

Who This Book Is For

The book is aimed at readers with a wide variety of backgrounds, including computer scientists and programmers, engineers, physicists, chemists, and mathematicians. Only 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. Quantum Adiabatic Computing and Quantum Annealing
5. QAOA: Quantum approximate optimisation 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. Appendices