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.
publication date
2023-03-31
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
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
approach
The book will be written following a style of presentation that iterates over three different layers. For each new concept or algorithm, an intuitive exposition will be given first. Then, the protocol or method will be explained again, this time with more mathematical detail. Finally, it will be illustrated with code examples that will be ready to use in both quantum simulators and actual quantum computers. The exposition will be self-contained and incremental, always building on previously explained concepts. Concepts introduced in the first chapters will be used again and again in the rest of the book, each time adding a little more depth and with new practical applications every time.
audience
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.
short description
This book introduces the main quantum algorithms that are currently used in optimization and machine learning. The approach is hands-on, with examples that can be run on simulators and actual quantum computers. The algorithms are explained in full detail, without sacrificing rigor, but, at the same time, keeping mathematical prerequisites to a minimum.
subtitle
Hands-on Approach to Modern Quantum Algorithms
keywords
Mathematics for machine learning; quantum computer; python machine learning; machine learning python; pytorch book; quantum book; quantum computing books
Product ISBN
9781804613832
