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Alberto Di
A Practical Guide to Quantum Machine Learning and Quantum Optimization
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666862A Practical Guide to Quantum Machine Learning and Quantum Optimizationhttps://www.gandhi.com.mx/a-practical-guide-to-quantum-machine-learning-and-quantum-optimisation/phttps://gandhi.vtexassets.com/arquivos/ids/280223/1dff7172-b58f-43cb-98bb-ed33b80c4860.jpg?v=638334080196070000739821MXNPackt PublishingInStock/Ebooks/664339A Practical Guide to Quantum Machine Learning and Quantum Optimization739821https://www.gandhi.com.mx/a-practical-guide-to-quantum-machine-learning-and-quantum-optimisation/phttps://gandhi.vtexassets.com/arquivos/ids/280223/1dff7172-b58f-43cb-98bb-ed33b80c4860.jpg?v=638334080196070000InStockMXN99999DIEbook20239781804618301_W3siaWQiOiI0ZTQzODljOS01NDNlLTQ0MzItOGI1Mi03M2RkOTM1MTJlNWQiLCJsaXN0UHJpY2UiOjgyMSwiZGlzY291bnQiOjgyLCJzZWxsaW5nUHJpY2UiOjczOSwiaW5jbHVkZXNUYXgiOnRydWUsInByaWNlVHlwZSI6Ildob2xlc2FsZSIsImN1cnJlbmN5IjoiTVhOIiwiZnJvbSI6IjIwMjQtMDQtMDhUMTY6MDA6MDBaIiwicmVnaW9uIjoiTVgiLCJpc1ByZW9yZGVyIjpmYWxzZX1d9781804618301_<p><strong>Machine Learning utilizing modern quantum algorithms and writing codes that can work with actual quantum computers.</strong></p><h4>Key Features</h4><ul><li>Gain a solid understanding of the principles behind quantum algorithms with minimal mathematics</li><li>Understand the process of implementing and running the algorithms on simulators and actual quantum computers</li><li>This book gives practical examples of methods that can be used to solve real-world quantum problems</li></ul><h4>Book Description</h4><p>This book provides a deep coverage of modern quantum algorithms for real-world problems, including machine learning and optimization. It will introduce the reader to quantum computing with a hands-on approach: reducing to the very minimum the mathematical and physical concepts needed to understand the material. Its content will cover many protocols and methods, from basic ones like quantum key distribution and quantum teleportation, to more advanced ones like Shors algorithm, quantum optimisation and quantum neural networks. The book will present algorithms in a clear and straightforward way, illustrating them with code that is ready to be run on quantum simulators and actual quantum computers. The programming languages that will be used in the book include IBMs Qiskit, Xanadus Pennylane and D-Waves Leap. How to run these programs on actual quantum computers will also be covered in the text.</p><p>As learning outcomes, readers will obtain a solid grasp on the fundamentals of quantum computing, a deep understanding of wide variety of quantum algorithms, and programming skills that will allow them to start applying quantum methods to practical problems.</p><h4>What you will learn</h4><ul><li>Gain a solid understanding of modern quantum algorithms</li><li>Learn and understand new advances in quantum algorithms</li><li>Learn what is the QUBO, QAOA, and HOBO model</li><li>Construct quantum circuits used to solve optimization problems</li><li>Understand how quantum neural networks work using Qiskit and Pennylane</li><li>Implement hybrid architectures using Qiskit/Pennylane and its PyTorch interface</li></ul><h4>Who This Book Is For</h4><p>The book is aimed at readers with a wide variety of backgrounds, including computer scientists and programmers, engineers, physicists, chemists, and mathematicians. Only 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.</p><h4>Table of Contents</h4><ol><li>Foundations of Quantum Computing</li><li>The Tools of the Trade in Quantum Computing</li><li>Working with Quadratic Unconstrained Binary Optimization Problems</li><li>Quantum Adiabatic Computing and Quantum Annealing</li><li>QAOA: Quantum approximate optimisation algorithm</li><li>GAS: Grover Adaptative Search</li><li>VQE: Variational quantum solver</li><li>What is Quantum Machine Learning?</li><li>Quantum support vector machines</li><li>Quantum neural networks</li><li>The best of both worlds: Hybrid architectures</li><li>Quantum generative adversarial networks</li><li>Afterword: the future of quantum computing</li><li>Appendices</li></ol>...(*_*)9781804618301_<p><strong>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</strong></p><h4>Key Features</h4><ul><li>Get a solid grasp of the principles behind quantum algorithms and optimization with minimal mathematical prerequisites</li><li>Learn the process of implementing the algorithms on simulators and actual quantum computers</li><li>Solve real-world problems using practical examples of methods</li></ul><h4>Book Description</h4><p>This 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.</p><p>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.</p><p>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.</p><h4>What you will learn</h4><ul><li>Review the basics of quantum computing</li><li>Gain a solid understanding of modern quantum algorithms</li><li>Understand how to formulate optimization problems with QUBO</li><li>Solve optimization problems with quantum annealing, QAOA, GAS, and VQE</li><li>Find out how to create quantum machine learning models</li><li>Explore how quantum support vector machines and quantum neural networks work using Qiskit and PennyLane</li><li>Discover how to implement hybrid architectures using Qiskit and PennyLane and its PyTorch interface</li></ul><h4>Who this book is for</h4><p>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.</p>...9781804618301_Packt Publishinglibro_electonico_b5c86ec2-3049-302f-b0ad-f0f01b5dd5ef_9781804618301;9781804618301_9781804618301Alberto DiInglésMéxicohttps://getbook.kobo.com/koboid-prod-public/packt-epub-3e5d3714-6858-4b87-88b8-d773d93337e5.epub2023-03-31T00:00:00+00:00Packt Publishing