A Practical Guide to Quantum Machine Learning and Quantum Optimisation: Hands-on Approach to Modern Quantum Algorithms (Paperback)

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.

這本全面指南將帶領您使用完整解釋的演算法和現成的範例，與量子模擬器和實際量子電腦一起工作。

主要特點：
- 在具有最少數學先備知識的情況下，深入了解量子演算法和優化原則。
- 學習在模擬器和實際量子電腦上實現演算法的過程。
- 使用實際方法的實際範例解決現實世界的問題。

書籍描述：
本書深入介紹了現代量子演算法，可用於解決現實世界的問題。您將以實踐為基礎的方法介紹量子計算，並具有最少的先備知識。

您將了解許多演算法、工具和方法，以QUBO和Ising形式建模優化問題，並學習如何使用量子退火、QAOA、Grover自適應搜索（GAS）和VQE解決優化問題。本書還向您展示如何訓練量子機器學習模型，例如量子支持向量機、量子神經網絡和量子生成對抗網絡。本書以直觀的方式幫助您學習量子演算法，並通過可在量子模擬器和實際量子電腦上運行的程式碼進行示範。您還將學習如何使用IBM的Qiskit、Xanadu的PennyLane和D-Wave的Leap等編程框架。

通過閱讀本書，您不僅將建立堅實的量子計算基礎，還將熟悉各種現代量子演算法。此外，本書還將提供使您能夠立即應用量子方法解決實際問題的編程技能。

您將學到：
- 複習量子計算的基礎知識。
- 獲得對現代量子演算法的深入理解。
- 了解如何使用QUBO公式制定優化問題。
- 使用量子退火、QAOA、GAS和VQE解決優化問題。
- 了解如何創建量子機器學習模型。
- 使用Qiskit和PennyLane瞭解量子支持向量機和量子神經網絡的工作原理。
- 發現如何使用Qiskit和PennyLane以及其PyTorch接口實現混合架構。

本書適合的讀者：
本書適合各種背景的專業人士，包括計算機科學家和程序員、工程師、物理學家、化學家和數學家。假設具備線性代數的基本知識和一些編程技能（例如Python），但附錄中將涵蓋所有數學先備知識。