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

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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. 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.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.Table of ContentsFoundations of Quantum ComputingThe Tools of the Trade in Quantum ComputingWorking with Quadratic Unconstrained Binary Optimization ProblemsAdiabatic Quantum Computing and Quantum AnnealingQAOA: Quantum Approximate Optimization AlgorithmGAS: Grover Adaptative SearchVQE: Variational Quantum SolverWhat is Quantum Machine Learning?Quantum Support Vector MachinesQuantum Neural NetworksThe Best of Both Worlds: Hybrid ArchitecturesQuantum Generative Adversarial NetworksAfterword: The Future of Quantum ComputingComplex NumbersBasic Linear AlgebraComputational ComplexityInstalling the ToolsProduction Notes

Publisher	Packt Publishing
Publication date	March 31, 2023
Edition	1st
Language	English
Print length	680 pages
ISBN-10	1804613835
ISBN-13	978-1804613832
Item Weight	2.53 pounds (1.15 kg)
Dimensions	7.5 x 1.6 x 9.25 inches (19.1 x 4.1 x 23.5 cm)

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

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Python Editorial Review

**** “A Practical Guide to Quantum Machine Learning and Quantum Optimization,” authored by Elías F. Combarro and Samuel González-Castillo, emerges as a significant contribution to the field of quantum computing literature, especially for those with an interest in algorithms and practical applications. Drawing from the success of a transformative online course, this book serves both novices and experts with a carefully structured pedagogical approach. Readers appreciate the authors' dedication to clarity and engagement. The book is commended for its systematic breakdown of complex topics, including an emphasis on practical algorithm implementation that can significantly aid comprehension. The unique feature of the book is its focus on modern quantum algorithms, particularly in quantum optimization and quantum machine learning, bridging theory with real-world coding through examples and exercises. Key functionalities include important study notes, supplementary resources for deeper learning, and a collection of exercises that enhance understanding. The integration of PennyLane as a programming framework stands out, granting users exposure to a flexible tool for quantum computing applications. Reviewers highlight that the coding exercises allow readers to experience quantum computing through hands-on practice, fostering a deeper understanding of fundamental concepts and their applications. The book's layout is designed for accessibility: sections can serve educational purposes independently or as a comprehensive course on advanced quantum algorithms. Many have found it indispensable as both a learning tool and a desk reference for ongoing work in the field. Readers recommend this book unequivocally for anyone looking to enhance their knowledge and skills in quantum computing and machine learning, indicating that it meets and exceeds expectations set during the initial online lectures. **

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Fordeler

Comprehensive coverage of quantum algorithms particularly in optimization and machine learning
Engaging and clear pedagogical style
Plenty of practical exercises, with answers provided
Use of the PennyLane programming framework, noted for inter-operability
Ideal for both self-study and guided courses
Appendices that aid in quick understanding of necessary mathematical concepts

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Basic concepts such as quantum teleportation and certain canonical algorithms may be excluded

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