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A Practical Guide to Quantum Machine Learning and Quantum Optimization
A Practical Guide to Quantum Machine Learning and Quantum Optimization

A Practical Guide to Quantum Machine Learning and Quantum Optimization: Hands-on Approach to Modern Quantum Algorithms

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Profile Icon Elías F. Combarro Fernández-Combarro Álvarez Profile Icon Samuel González Castillo
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$52.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8 (15 Ratings)
Paperback Mar 2023 680 pages 1st Edition
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Profile Icon Elías F. Combarro Fernández-Combarro Álvarez Profile Icon Samuel González Castillo
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$52.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8 (15 Ratings)
Paperback Mar 2023 680 pages 1st Edition
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A Practical Guide to Quantum Machine Learning and Quantum Optimization

Chapter 1
Foundations of Quantum Computing

The beginning is always today.
— Mary Shelley

You may have heard that the mathematics needed to understand quantum computing is arcane, mysterious and difficult…but we utterly disagree! In fact, in this chapter, we will introduce all the concepts that you will need in order to follow the quantum algorithms that we will be studying in the rest of the book. Actually, you may be surprised to see that we will only rely on some linear algebra and a bit of (extremely simple) trigonometry.

We shall start by giving a quick overview of what quantum computing is, what the current state of the art is, and what the main applications are expected to be. After that, we will introduce the model of quantum circuits. There are several computational models for quantum computing, but this is the most popular one and, moreover, it's the one that we will be using throughout most of the book. Then, we will describe in detail what qubits are, how we can operate on them by using quantum gates, and how we can retrieve results by performing measurements. We will start with the simplest possible case — just a humble qubit! Then, we will steadily build upon that until we learn how to work with as many qubits as we want.

This chapter will cover the following topics:

  • Quantum computing: the big picture

  • The basics of the quantum circuit model

  • Working with one qubit and the Bloch sphere

  • Working with two qubits and entanglement

  • Working with multiple qubits and universality

After reading this chapter, you will have acquired a solid understanding of the fundamentals of quantum computing and you will be more than ready to learn how practical quantum algorithms are developed.

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Key benefits

  • 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

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.

Who is this book 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.

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
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Publication date : Mar 31, 2023
Length: 680 pages
Edition : 1st
Language : English
ISBN-13 : 9781804613832
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Table of Contents

21 Chapters
Part I: I, for One, Welcome our New Quantum Overlords Chevron down icon Chevron up icon
Chapter 1: Foundations of Quantum Computing Chevron down icon Chevron up icon
Chapter 2: The Tools of the Trade in Quantum Computing Chevron down icon Chevron up icon
Part II: When Time is Gold: Tools for Quantum Optimization Chevron down icon Chevron up icon
Chapter 3: Working with Quadratic Unconstrained Binary Optimization Problems Chevron down icon Chevron up icon
Chapter 4: Adiabatic Quantum Computing and Quantum Annealing Chevron down icon Chevron up icon
Chapter 5: QAOA: Quantum Approximate Optimization Algorithm Chevron down icon Chevron up icon
Chapter 6: GAS: Grover Adaptive Search Chevron down icon Chevron up icon
Chapter 7: VQE: Variational Quantum Eigensolver Chevron down icon Chevron up icon
Part III: A Match Made in Heaven: Quantum Machine Learning Chevron down icon Chevron up icon
Chapter 8: What Is Quantum Machine Learning? Chevron down icon Chevron up icon
Chapter 9: Quantum Support Vector Machines Chevron down icon Chevron up icon
Chapter 10: Quantum Neural Networks Chevron down icon Chevron up icon
Chapter 11: The Best of Both Worlds: Hybrid Architectures Chevron down icon Chevron up icon
Chapter 12: Quantum Generative Adversarial Networks Chevron down icon Chevron up icon
Part IV: Afterword and Appendices Chevron down icon Chevron up icon
Chapter 13: Afterword: The Future of Quantum Computing Chevron down icon Chevron up icon
Assessments Chevron down icon Chevron up icon
Bibliography Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

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N/A Feb 13, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Libro che unisce i modelli di ottimizzazione (con una buona base teorica) al Quantum Computing. Valido.
Feefo Verified review Feefo
Tiny May 11, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Wow, just wow, a great book providing invaluable data. For those seeking any reference to convert quantum equations into code, test classically, and then run on a quantum computer, this is the book. “A Practical Guide to Quantum Machine Learning and Quantum Optimization” (Packt, 2023) by Elias Combarro and Samuel Gozalez-Castillo more than lives up to the promise. For each chapter, the book provides the quantum theory, and then the practical application, supplemented with in-step exercises to keep up with the material. If your math skills are a little weak, don’t worry, the full set of appendices provides quick references to get up to speed. The book appears in three relevant sections, an intro to quantum circuits, deeply analyzing practical quantum algorithms, and integrating quantum computing into machine learning tools. This is a must-read for anyone with even a partial interest in quantum computing and a must-have desk reference for those actively involved in this exciting field.The first section is relatively brief, and if you are familiar with quantum computing, you can probably skipthese two chapters. However, it provides an excellent history of quantum, an introduction to using quantum circuits, and the tools provided during the book. This section relates to Appendix D, describing how to install and use the various tools required. This starts the integration between the appendices as background and the details throughout the book. The section finishes by discussing the various IDE options for writing quantum code and where that code can actually be run across quantum computers. One missing element was it does not discuss the options to use AWS Braket which I mentioned in a previous review. Section two is the meat of the book and the real brain challenge. The authors look at five different quantum algorithms (Quadratic Unconstrained Binary Operators (QUBO), Adiabatic Quantum Computing, Quantum Approximate Optimization Algorithm (QAOA), Grover Adaptive Search (GAS), and the Variational Quantum Eigensolver). If those names left you a little behind, don’t worry. Each chapter starts with the explanation and theory behind the algorithm, use-cases, and then moves into the coding approach. Each chapter takes a detailed look at either Qiskit or Pennylane, depending on which works best, and then moves to describe that one thoroughly. The other option receives a brief explanation but is sufficient to start. An important reminder for this mid-section is the concept of NP problems where N is a number, and P is polynomial time. This presents NP as the class of problems for which exists a polynomial time verifier and one of the largest open quantum problems proving that P is not equal to NP. Moving on, NP-hard problems occur if every problem in NP is poly-time reducible. NP-complete occurs where every problem is present in NP and every problem is reducible to it. These definitions occur repeatedly throughout the book. One other important consideration is how quantum can help accelerate some problems, for example, in the GAS theorem, to find data satisfying specific conditions in unsorted data, a classical computer requires a minimum of N/2 operations while quantum computing requires √N operations, a massive decrease. The third section then converts those algorithms into practical ML applications. It begins with a review of how to establish ML processes and the changes to be expected. The biggest challenge is first converting classical data into quantum-relevant terms and then measuring them at the end. After working through Vector Support Machines and neural networks, the chapter on Generative Adversarial Networks(GAN) is great. The GAN process is where one sets an initial machine to review data and produce new data similar to what it has learned. The discriminator then uses the original dataset to determine if the new data is sufficient. Multiple competing algorithms, how could you ask for anything more? The one drawback to the book is it is primarily a reference rather than a true guide. It highlights all the required material to be able to function in a quantum computing space but does not show you what you should be looking for with your particular use case. This is a strength and a shortcoming. As a reference, this is key material to make sure the solutions you implement are appropriate or to help discriminate once you have a problem. If you don’t know what you want to do with quantum computing, this may not help you get there but that definitely does not undermine the book’s value. Overall, “A Practical Guide to Quantum Machine Learning and Quantum Optimization” (Packt, 2023) by Elias Combarro and Samuel Gozalez-Castillo is one of the best I’ve read recently on this challenging field. It presents all the required detail, and if not always easy to understand, the appendices rapidly bring you up to speed. I recommend the e-version as searching to connect the terms between chapters was frequently helpful as well. Each chapter also presents a set of exercises to make sure one can follow along accurately. The code samples are convenient, and always included with outputs, and jumping between different providers helps ensure you understand the process and not just the particular implementation. I recommend to anyone with even a passing interest in quantum computing and as a key desk reference for those who deal with these issues on a daily basis. A must-read.
Amazon Verified review Amazon
Aadi May 01, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
What sets this book apart is its emphasis on practical applications. The authors provide numerous examples and code snippets to demonstrate how quantum algorithms can be used to solve real-world problems. They also discuss the current limitations of quantum computing and offer guidance on how to overcome these limitations.The writing is clear and concise, making the material accessible to both beginners and experts. The book is well-organized, with each chapter building upon the previous one, and the authors do an excellent job summarizing the key takeaways at the end of each chapter.
Amazon Verified review Amazon
Gauri Jul 05, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book offers an in-depth understanding of concepts related to ML, Quantum Computing, and Optimization. As a prior graduate student myself, I found the information related to QC frameworks and quantum gates useful as it explains the working principles of different models related to these concepts.
Amazon Verified review Amazon
Good Jan 20, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I didnt study yet , i have to study
Amazon Verified review Amazon
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