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

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

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Work with fully explained algorithms and ready-to-use examples that can be run on quantum simulators and actual quantum computers with this comprehensive guideKey FeaturesGet a solid grasp of the principles behind quantum algorithms and optimization with minimal mathematical prerequisitesLearn the process of implementing the algorithms on simulators and actual quantum computersSolve real-world problems using practical examples of methodsBook DescriptionThis book provides deep coverage of modern quantum algorithms that can be used to solve real-world problems. Youll be introduced to quantum computing using a hands-on approach with minimal prerequisites.Youll 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 thats ready to be run on quantum simulators and actual quantum computers. Youll also learn how to utilize programming frameworks such as IBMs Qiskit, Xanadus PennyLane, and D-Waves 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 learnReview the basics of quantum computingGain a solid understanding of modern quantum algorithmsUnderstand how to formulate optimization problems with QUBOSolve optimization problems with quantum annealing, QAOA, GAS, and VQEFind out how to create quantum machine learning modelsExplore how quantum support vector machines and quantum neural networks work using Qiskit and PennyLaneDiscover how to implement hybrid architectures using Qiskit and PennyLane and its PyTorch interfaceWho this book is forThis 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.

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Hands-on Approach to Modern Quantum Algorithms BIRMINGHAMMUMBAI - photo 1
Hands-on Approach to Modern Quantum Algorithms
BIRMINGHAMMUMBAI Copyright 2023 Packt Publishing All rights reserved - photo 2

BIRMINGHAMMUMBAI

Copyright 2023 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the , nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

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First published: March 2023

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ISBN 978-1-80461-383-2

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Contributors
About the authors

Elas F. Combarroholds degrees from the University of Oviedo (Spain) in both mathematics (1997, award for second highest grades in the country) and computer science (2002, award for highest grades in the country). After carrying out several stays as a visiting researcher at the Novosibirsk State University (Russia), he obtained a PhD in mathematics (Oviedo, 2001) with a dissertation on the properties of some computable predicates under the supervision of Prof. Andrey Morozov and Prof. Consuelo Martnez.

Since 2009, Elas F. Combarro has been a tenured associate professor at the Computer Science Department of the University of Oviedo. He has published more than 50 research papers in international journals on topics such as quantum computing, computability theory, machine learning, fuzzy measures, and computational algebra. His current research focuses on the application of quantum computing to algebraic, optimization, and machine-learning problems.

In 2020 and 2022, he was a Cooperation Associate at CERN openlab. Currently, he is Spains representative on the Advisory Board of the CERN Quantum Technology Initiative.

To Adela, Paula and Sergio. You are my reason to live.

Samuel Gonzlez-Castilloholds degrees from the University of Oviedo (Spain) in both mathematics and physics (2021). He is currently a mathematics research student at Maynooth University, where he works as a graduate teaching assistant.

He completed his physics bachelor thesis under the supervision of Prof. Elas F.

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