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

Elías F. Combarro Fernández-Combarro Álvarez

Samuel González Castillo

$52.99

4.9 (14 Ratings)

Paperback Mar 2023 680 pages 1st Edition

Elías F. Combarro Fernández-Combarro Álvarez

Samuel González Castillo

$52.99

4.9 (14 Ratings)

Paperback Mar 2023 680 pages 1st Edition

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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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Frequently bought together

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Total $ 144.97

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N/A Feb 13, 2024

Libro che unisce i modelli di ottimizzazione (con una buona base teorica) al Quantum Computing. Valido.

Feefo Verified review

Gauri Jul 05, 2023

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

Good Jan 20, 2024

I didnt study yet , i have to study

Kushal Jun 09, 2023

A Practical Guide to Quantum Machine Learning and Quantum Optimization offers a comprehensive and accessible exploration of modern quantum algorithms, providing readers with a hands-on approach to mastering these cutting-edge techniques. This invaluable resource combines theory and practical examples, equipping both beginners and seasoned professionals with the tools and knowledge to harness the power of quantum computing in the fields of machine learning and optimization.

Ferdous Khan PhD Apr 17, 2023

I attended a series of lectures titled “A Practical Introduction to Quantum Computing: From Qubits to Quantum Machine Learning and Beyond”, taught online from CERN in the summer of 2020 by Elías F. Combarro, and it changed my life in so many ways. Initially, there were 1500+ of us logged in from all over the world, and that must have been a record turnout for any online class. By the end of the classes, most of us had done enough exercises to learn the basics of quantum computing (QC), up to and including Quantum Generative Adversarial Networks (QGANs).And what a treat it was, mainly because Professor Combarro was such a passionate teacher who deeply cared about the subject matter (it showed!), and the students/researchers. There was feedback from us from all over the world, and it sharpened our understanding of the subject matter, i.e., quantum computing. Some of us stayed in touch with Professor Combarro as quantum computing was rapidly evolving. Having remembered the pleasure and benefits of learning from the online class, I had suggested that Professor Combarro write a book on the subject. Beyond that, I can take no further credit, other than occasional inquiries. Credit must go to many, as you will read in Elias’ acknowledgement, foremost among them is the co-author Samuel González-Castillo, and the reviewers, Francisco Orts and Guillermo Botella, and the Editorial review team.This book is about quantum algorithms, and it is a pedagogical treasure. There are many enhancements in this that go beyond the online class! The difference (with the online class) is that the authors decided not to include basic methods such as quantum teleportation, quantum key distribution with the BB84 protocol, or canonical algorithms like the ones by Deutsch and Jozsa, and by Shor, for instance. It is worth recalling that traditionally, one starts studying quantum computing by going through ‘protocols with just a few qubits, and then learning about Deutsch-Jozsa’s, Simon’s, and Bernstein-Vazirani’s algorithms, climbing all the way up to Shor’s and Grover’s methods.’ Some of these methods are, however, discussed elsewhere although not in this book.In contrast, this book is a ‘unified, detailed, and practice-oriented explanation of many algorithms that are central to modern quantum computing and that are difficult to find together in a sole source. This includes a lot of methods that have been developed to solve optimization problems with quantum computers, and most algorithms (especially the ones based on variational circuits) from the field of quantum machine learning.’ This book addresses both of these areas.Parts 2 and 3 are mostly self-contained and independent of each other. ‘They can be used for self-study of quantum optimization and quantum machine learning, or to teach two short, independent courses on these topics or one full course on modern quantum algorithms. The strongest dependencies between chapters are shown in Figure 1, so you can know which chapters you may skip without losing track of the explanation.’ How convenient is this from the pedagogical point of view!I really like the ‘Important notes’, ‘To learn more’, and the Exercises (The answers are provided at the end in Assessments.) The Important notes are essential. The exercises all the more so. The authors assume that you have had no previous experience with quantum computing at all (it is assumed that you have a working knowledge of complex numbers and linear algebra).The other addition that I like is that of the programming framework, PennyLane. In the online class, we used IBM Qiskit, and D-Wave’s Ocean. The authors seem to recommend PennyLane a great deal, perhaps a notch higher. ‘PennyLane is one of the best frameworks out there when it comes to inter-operability.’Finally, as in the online class, there is lots of code that one can run on simulators, and on actual quantum computers, just as we did. It is only after running these codes (in IBM Qiskit) for Shor, Deutsch-Zosja and Grover’s algorithms that I was able to understand what quantum computing was about at some fundamental level. I hope users of this book will take advantage of these codes and gain new insights just as we did in 2020.In the coming years, guided by this book, I am quite certain it would be easier to follow the ‘developments of new quantum methods, the increase of the applicability of quantum techniques, and the deepening of our understanding of the properties of quantum algorithms.’

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

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