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Taschenbuch. Etat : Neu. Neuware - This book presents the research into and application of machine learning in quantum computation, known as quantum machine learning (QML). It presents a comparison of quantum machine learning, classical machine learning, and traditional programming, along with the usage of quantum computing, toward improving traditional machine learning algorithms through case studies.In summary, the book: - Covers the core and fundamental aspects of statistics, quantum learning, and quantum machines. - Discusses the basics of machine learning, regression, supervised and unsupervised machine learning algorithms, and artificial neural networks. - Elaborates upon quantum machine learning models, quantum machine learning approaches and quantum classification, and boosting. - Introduces quantum evaluation models, deep quantum learning, ensembles, and QBoost. - Presents case studies to demonstrate the efficiency of quantum mechanics in industrial aspects. This reference text is primarily written for scholars and researchers working in the fields of computer science and engineering, information technology, electrical engineering, and electronics and communication engineering.…

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Taschenbuch. Etat : Neu. A Practical Guide to Quantum Machine Learning and Quantum Optimisation | Hands-on Approach to Modern Quantum Algorithms | Elías F. Combarro (u. a.) | Taschenbuch | Englisch | 2023 | Packt Publishing | EAN 9781804613832 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. …

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 Features:Get 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 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.What You Will Learn:Review 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 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.…

Buch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book presents the research into and application of machine learning in quantum computation, known as quantum machine learning (QML). It presents a comparison of quantum machine learning, classical machine learning, and traditional programming, along with the usage of quantum computing, toward improving traditional machine learning algorithms through case studies.In summary, the book:Covers the core and fundamental aspects of statistics, quantum learning, and quantum machines.Discusses the basics of machine learning, regression, supervised and unsupervised machine learning algorithms, and artificial neural networks.Elaborates upon quantum machine learning models, quantum machine learning approaches and quantum classification, and boosting.Introduces quantum evaluation models, deep quantum learning, ensembles, and QBoost.Presents case studies to demonstrate the efficiency of quantum mechanics in industrial aspects.This reference text is primarily written for scholars and researchers working in the fields of computer science and engineering, information technology, electrical engineering, and electronics and communication engineering.…