Description: A Practical Guide to Quantum Machine Learning and Quantum Optimization by Alberto Di Meglio, Elías F. Combarro, Samuel González-Castillo Estimated delivery 3-12 business days Format Paperback Condition Brand New Description 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. Author Biography Elías F. Combarro holds 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 some research stays at the Novosibirsk State University (Russia), he obtained a Ph.D. in Mathematics (Oviedo, 2001) with a dissertation on the properties of some computable predicates under the supervision of Prof. Andrey Morozov and Prof. Consuelo Martínez.Since 2009, Elías F. Combarro has been an 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 Computability Theory, Machine Learning, Fuzzy Measures and Computational Algebra. His current research focuses on the application Quantum Computing to algebraic, optimisation and machine learning problems.From July 2020 to January 2021, he was a Cooperation Associate at CERN openlab. Currently, he is the Spain representative in the Advisory Board of CERN Quantum Technology Initiative, a member of the Advisory Board of SheQuantum and one of the founders of the QSpain, a quantum computing think tank based in Spain. Samuel González-Castillo holds degrees from the University of Oviedo (Spain) in both Mathematics and Physics (2021). He is currently a mathematics research student at the National University of Ireland, Maynooth, where he works as a graduate teaching assistant.He completed his physics bachelor thesis under the supervision of Prof. Elías F. Combarro and Prof. Ignacio F. RÚa (University of Oviedo), and Dr. Sofia Vallecorsa (CERN). In it, he worked alongside other researchers from ETH ZÜrich on the application of Quantum Machine Learning to classification problems in High Energy Physis. In 2021, he was a summer student at CERN developing a benchmarking framework for quantum simulators. He has contributed to several conferences on quantum computing. Details ISBN 1804613835 ISBN-13 9781804613832 Title A Practical Guide to Quantum Machine Learning and Quantum Optimization Author Alberto Di Meglio, Elías F. Combarro, Samuel González-Castillo Format Paperback Year 2023 Pages 680 Publisher Packt Publishing Limited GE_Item_ID:141533951; About Us Grand Eagle Retail is the ideal place for all your shopping needs! With fast shipping, low prices, friendly service and over 1,000,000 in stock items - you're bound to find what you want, at a price you'll love! Shipping & Delivery Times Shipping is FREE to any address in USA. Please view eBay estimated delivery times at the top of the listing. 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Price: 66.96 USD
Location: Fairfield, Ohio
End Time: 2024-10-12T06:02:52.000Z
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Restocking Fee: No
Return shipping will be paid by: Buyer
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ISBN-13: 9781804613832
Book Title: A Practical Guide to Quantum Machine Learning and Quantum Optimiz
Number of Pages: 680 Pages
Language: English
Publication Name: Practical Guide to Quantum Machine Learning and Quantum Optimisation : Hands-On Approach to Modern Quantum Algorithms
Publisher: Packt Publishing, The Limited
Subject: General
Publication Year: 2023
Type: Textbook
Author: Elias F. Combarro, Samuel Gonzalez-Castillo
Item Length: 92.5 in
Subject Area: Mathematics, Computers
Item Width: 75 in
Format: Trade Paperback