Concise Guide To Quantum Machine Learning

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Concise Guide to Quantum Machine Learning

Author : Davide Pastorello
Publisher : Springer Nature
ISBN 13 : 9811968977
Total Pages : 144 pages
Book Rating : 4.8/5 (119 download)

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Book Synopsis Concise Guide to Quantum Machine Learning by : Davide Pastorello

Download or read book Concise Guide to Quantum Machine Learning written by Davide Pastorello and published by Springer Nature. This book was released on 2022-12-16 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a brief but effective introduction to quantum machine learning (QML). QML is not merely a translation of classical machine learning techniques into the language of quantum computing, but rather a new approach to data representation and processing. Accordingly, the content is not divided into a “classical part” that describes standard machine learning schemes and a “quantum part” that addresses their quantum counterparts. Instead, to immerse the reader in the quantum realm from the outset, the book starts from fundamental notions of quantum mechanics and quantum computing. Avoiding unnecessary details, it presents the concepts and mathematical tools that are essential for the required quantum formalism. In turn, it reviews those quantum algorithms most relevant to machine learning. Later chapters highlight the latest advances in this field and discuss the most promising directions for future research. To gain the most from this book, a basic grasp of statistics and linear algebra is sufficient; no previous experience with quantum computing or machine learning is needed. The book is aimed at researchers and students with no background in quantum physics and is also suitable for physicists looking to enter the field of QML.

A Practical Guide to Quantum Machine Learning and Quantum Optimization

Author : Elias F. Combarro
Publisher : Packt Publishing Ltd
ISBN 13 : 1804618306
Total Pages : 680 pages
Book Rating : 4.8/5 (46 download)

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Book Synopsis A Practical Guide to Quantum Machine Learning and Quantum Optimization by : Elias F. Combarro

Download or read book A Practical Guide to Quantum Machine Learning and Quantum Optimization written by Elias F. Combarro and published by Packt Publishing Ltd. This book was released on 2023-03-31 with total page 680 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 Key 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 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 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 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.

Quantum Machine Learning

Author : Peter Wittek
Publisher : Academic Press
ISBN 13 : 0128010991
Total Pages : 176 pages
Book Rating : 4.1/5 (28 download)

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Book Synopsis Quantum Machine Learning by : Peter Wittek

Download or read book Quantum Machine Learning written by Peter Wittek and published by Academic Press. This book was released on 2014-09-10 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantum Machine Learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. Paring down the complexity of the disciplines involved, it focuses on providing a synthesis that explains the most important machine learning algorithms in a quantum framework. Theoretical advances in quantum computing are hard to follow for computer scientists, and sometimes even for researchers involved in the field. The lack of a step-by-step guide hampers the broader understanding of this emergent interdisciplinary body of research. Quantum Machine Learning sets the scene for a deeper understanding of the subject for readers of different backgrounds. The author has carefully constructed a clear comparison of classical learning algorithms and their quantum counterparts, thus making differences in computational complexity and learning performance apparent. This book synthesizes of a broad array of research into a manageable and concise presentation, with practical examples and applications. Bridges the gap between abstract developments in quantum computing with the applied research on machine learning Provides the theoretical minimum of machine learning, quantum mechanics, and quantum computing Gives step-by-step guidance to a broader understanding of this emergent interdisciplinary body of research

Concise Guide to Quantum Computing

Author : Sergei Kurgalin
Publisher :
ISBN 13 : 9783030650537
Total Pages : pages
Book Rating : 4.6/5 (55 download)

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Book Synopsis Concise Guide to Quantum Computing by : Sergei Kurgalin

Download or read book Concise Guide to Quantum Computing written by Sergei Kurgalin and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook is intended for practical, laboratory sessions associated with the course of quantum computing and quantum algorithms, as well as for self-study. It contains basic theoretical concepts and methods for solving basic types of problems and gives an overview of basic qubit operations, entangled states, quantum circuits, implementing functions, quantum Fourier transform, phase estimation, etc. The book serves as a basis for the application of new information technologies in education and corporate technical training: theoretical material and examples of practical problems, as well as exercises with, in most cases, detailed solutions, have relation to information technologies. A large number of detailed examples serve to better develop professional competencies in computer science. Dr. Sergei Kurgalin is Professor and Head of the Department of Digital Technologies at Voronezh State University, Russia. Dr. Sergei Borzunov is Associate Professor at the same institution.

Machine Learning with Quantum Computers

Author : Maria Schuld
Publisher : Springer Nature
ISBN 13 : 3030830985
Total Pages : 321 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Machine Learning with Quantum Computers by : Maria Schuld

Download or read book Machine Learning with Quantum Computers written by Maria Schuld and published by Springer Nature. This book was released on 2021-10-17 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers an introduction into quantum machine learning research, covering approaches that range from "near-term" to fault-tolerant quantum machine learning algorithms, and from theoretical to practical techniques that help us understand how quantum computers can learn from data. Among the topics discussed are parameterized quantum circuits, hybrid optimization, data encoding, quantum feature maps and kernel methods, quantum learning theory, as well as quantum neural networks. The book aims at an audience of computer scientists and physicists at the graduate level onwards. The second edition extends the material beyond supervised learning and puts a special focus on the developments in near-term quantum machine learning seen over the past few years.

Quantum Machine Learning

Author : Siddhartha Bhattacharyya
Publisher : Walter de Gruyter GmbH & Co KG
ISBN 13 : 3110670720
Total Pages : 144 pages
Book Rating : 4.1/5 (16 download)

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Book Synopsis Quantum Machine Learning by : Siddhartha Bhattacharyya

Download or read book Quantum Machine Learning written by Siddhartha Bhattacharyya and published by Walter de Gruyter GmbH & Co KG. This book was released on 2020-06-08 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantum-enhanced machine learning refers to quantum algorithms that solve tasks in machine learning, thereby improving a classical machine learning method. Such algorithms typically require one to encode the given classical dataset into a quantum computer, so as to make it accessible for quantum information processing. After this, quantum information processing routines can be applied and the result of the quantum computation is read out by measuring the quantum system. While many proposals of quantum machine learning algorithms are still purely theoretical and require a full-scale universal quantum computer to be tested, others have been implemented on small-scale or special purpose quantum devices.

Quantum Machine Learning and Optimisation in Finance

Author : Antoine Jacquier
Publisher : Packt Publishing Ltd
ISBN 13 : 1801817871
Total Pages : 443 pages
Book Rating : 4.8/5 (18 download)

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Book Synopsis Quantum Machine Learning and Optimisation in Finance by : Antoine Jacquier

Download or read book Quantum Machine Learning and Optimisation in Finance written by Antoine Jacquier and published by Packt Publishing Ltd. This book was released on 2022-10-31 with total page 443 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn the principles of quantum machine learning and how to apply them While focus is on financial use cases, all the methods and techniques are transferable to other fields Purchase of Print or Kindle includes a free eBook in PDF Key Features Discover how to solve optimisation problems on quantum computers that can provide a speedup edge over classical methods Use methods of analogue and digital quantum computing to build powerful generative models Create the latest algorithms that work on Noisy Intermediate-Scale Quantum (NISQ) computers Book Description With recent advances in quantum computing technology, we finally reached the era of Noisy Intermediate-Scale Quantum (NISQ) computing. NISQ-era quantum computers are powerful enough to test quantum computing algorithms and solve hard real-world problems faster than classical hardware. Speedup is so important in financial applications, ranging from analysing huge amounts of customer data to high frequency trading. This is where quantum computing can give you the edge. Quantum Machine Learning and Optimisation in Finance shows you how to create hybrid quantum-classical machine learning and optimisation models that can harness the power of NISQ hardware. This book will take you through the real-world productive applications of quantum computing. The book explores the main quantum computing algorithms implementable on existing NISQ devices and highlights a range of financial applications that can benefit from this new quantum computing paradigm. This book will help you be one of the first in the finance industry to use quantum machine learning models to solve classically hard real-world problems. We may have moved past the point of quantum computing supremacy, but our quest for establishing quantum computing advantage has just begun! What you will learn Train parameterised quantum circuits as generative models that excel on NISQ hardware Solve hard optimisation problems Apply quantum boosting to financial applications Learn how the variational quantum eigensolver and the quantum approximate optimisation algorithms work Analyse the latest algorithms from quantum kernels to quantum semidefinite programming Apply quantum neural networks to credit approvals Who this book is for This book is for Quants and developers, data scientists, researchers, and students in quantitative finance. Although the focus is on financial use cases, all the methods and techniques are transferable to other areas.

A Practical Guide to Quantum Machine Learning and Quantum Optimisation

Author : Elias F. Combarro
Publisher : Packt Publishing
ISBN 13 : 9781804613832
Total Pages : 0 pages
Book Rating : 4.6/5 (138 download)

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Book Synopsis A Practical Guide to Quantum Machine Learning and Quantum Optimisation by : Elias F. Combarro

Download or read book A Practical Guide to Quantum Machine Learning and Quantum Optimisation written by Elias F. Combarro and published by Packt Publishing. This book was released on 2023-03-31 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 Key Features: Get a solid grasp of the principles behind quantum algorithms and optimization with minimal mathematical prerequisites Learn the process of implementing the algorithms on simulators and actual quantum computers Solve real-world problems using practical examples of methods Book 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 computing Gain a solid understanding of modern quantum algorithms Understand how to formulate optimization problems with QUBO Solve optimization problems with quantum annealing, QAOA, GAS, and VQE Find out how to create quantum machine learning models Explore how quantum support vector machines and quantum neural networks work using Qiskit and PennyLane Discover how to implement hybrid architectures using Qiskit and PennyLane and its PyTorch interface Who 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.

Convolutional Neural Networks in Visual Computing

Author : Ragav Venkatesan
Publisher : CRC Press
ISBN 13 : 1498770401
Total Pages : 168 pages
Book Rating : 4.4/5 (987 download)

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Book Synopsis Convolutional Neural Networks in Visual Computing by : Ragav Venkatesan

Download or read book Convolutional Neural Networks in Visual Computing written by Ragav Venkatesan and published by CRC Press. This book was released on 2017-10-23 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the fundamentals in designing and deploying techniques using deep architectures. It is intended to serve as a beginner's guide to engineers or students who want to have a quick start on learning and/or building deep learning systems. This book provides a good theoretical and practical understanding and a complete toolkit of basic information and knowledge required to understand and build convolutional neural networks (CNN) from scratch. The book focuses explicitly on convolutional neural networks, filtering out other material that co-occur in many deep learning books on CNN topics.

Quantum Machine Learning: An Applied Approach

Author : Santanu Ganguly
Publisher : Apress
ISBN 13 : 9781484270974
Total Pages : 551 pages
Book Rating : 4.2/5 (79 download)

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Book Synopsis Quantum Machine Learning: An Applied Approach by : Santanu Ganguly

Download or read book Quantum Machine Learning: An Applied Approach written by Santanu Ganguly and published by Apress. This book was released on 2021-08-11 with total page 551 pages. Available in PDF, EPUB and Kindle. Book excerpt: Know how to adapt quantum computing and machine learning algorithms. This book takes you on a journey into hands-on quantum machine learning (QML) through various options available in industry and research. The first three chapters offer insights into the combination of the science of quantum mechanics and the techniques of machine learning, where concepts of classical information technology meet the power of physics. Subsequent chapters follow a systematic deep dive into various quantum machine learning algorithms, quantum optimization, applications of advanced QML algorithms (quantum k-means, quantum k-medians, quantum neural networks, etc.), qubit state preparation for specific QML algorithms, inference, polynomial Hamiltonian simulation, and more, finishing with advanced and up-to-date research areas such as quantum walks, QML via Tensor Networks, and QBoost. Hands-on exercises from open source libraries regularly used today in industry and research are included, such as Qiskit, Rigetti's Forest, D-Wave's dOcean, Google's Cirq and brand new TensorFlow Quantum, and Xanadu's PennyLane, accompanied by guided implementation instructions. Wherever applicable, the book also shares various options of accessing quantum computing and machine learning ecosystems as may be relevant to specific algorithms. The book offers a hands-on approach to the field of QML using updated libraries and algorithms in this emerging field. You will benefit from the concrete examples and understanding of tools and concepts for building intelligent systems boosted by the quantum computing ecosystem. This work leverages the author’s active research in the field and is accompanied by a constantly updated website for the book which provides all of the code examples. What You will Learn Understand and explore quantum computing and quantum machine learning, and their application in science and industry Explore various data training models utilizing quantum machine learning algorithms and Python libraries Get hands-on and familiar with applied quantum computing, including freely available cloud-based access Be familiar with techniques for training and scaling quantum neural networks Gain insight into the application of practical code examples without needing to acquire excessive machine learning theory or take a quantum mechanics deep dive Who This Book Is For Data scientists, machine learning professionals, and researchers

Quantum Computing and Artificial Intelligence

Author : Pethuru Raj
Publisher : Walter de Gruyter GmbH & Co KG
ISBN 13 : 3110791404
Total Pages : 308 pages
Book Rating : 4.1/5 (17 download)

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Book Synopsis Quantum Computing and Artificial Intelligence by : Pethuru Raj

Download or read book Quantum Computing and Artificial Intelligence written by Pethuru Raj and published by Walter de Gruyter GmbH & Co KG. This book was released on 2023-08-21 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Supervised Learning with Quantum Computers

Author : Maria Schuld
Publisher : Springer
ISBN 13 : 3319964240
Total Pages : 293 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Supervised Learning with Quantum Computers by : Maria Schuld

Download or read book Supervised Learning with Quantum Computers written by Maria Schuld and published by Springer. This book was released on 2018-08-30 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantum machine learning investigates how quantum computers can be used for data-driven prediction and decision making. The books summarises and conceptualises ideas of this relatively young discipline for an audience of computer scientists and physicists from a graduate level upwards. It aims at providing a starting point for those new to the field, showcasing a toy example of a quantum machine learning algorithm and providing a detailed introduction of the two parent disciplines. For more advanced readers, the book discusses topics such as data encoding into quantum states, quantum algorithms and routines for inference and optimisation, as well as the construction and analysis of genuine ``quantum learning models''. A special focus lies on supervised learning, and applications for near-term quantum devices.

Quantum Machine Learning

Author : Claudio Conti
Publisher : Springer Nature
ISBN 13 : 3031442261
Total Pages : 393 pages
Book Rating : 4.0/5 (314 download)

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Book Synopsis Quantum Machine Learning by : Claudio Conti

Download or read book Quantum Machine Learning written by Claudio Conti and published by Springer Nature. This book was released on 2024-01-28 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a new way of thinking about quantum mechanics and machine learning by merging the two. Quantum mechanics and machine learning may seem theoretically disparate, but their link becomes clear through the density matrix operator which can be readily approximated by neural network models, permitting a formulation of quantum physics in which physical observables can be computed via neural networks. As well as demonstrating the natural affinity of quantum physics and machine learning, this viewpoint opens rich possibilities in terms of computation, efficient hardware, and scalability. One can also obtain trainable models to optimize applications and fine-tune theories, such as approximation of the ground state in many body systems, and boosting quantum circuits’ performance. The book begins with the introduction of programming tools and basic concepts of machine learning, with necessary background material from quantum mechanics and quantum information also provided. This enables the basic building blocks, neural network models for vacuum states, to be introduced. The highlights that follow include: non-classical state representations, with squeezers and beam splitters used to implement the primary layers for quantum computing; boson sampling with neural network models; an overview of available quantum computing platforms, their models, and their programming; and neural network models as a variational ansatz for many-body Hamiltonian ground states with applications to Ising machines and solitons. The book emphasizes coding, with many open source examples in Python and TensorFlow, while MATLAB and Mathematica routines clarify and validate proofs. This book is essential reading for graduate students and researchers who want to develop both the requisite physics and coding knowledge to understand the rich interplay of quantum mechanics and machine learning.

Hands-On Quantum Machine Learning With Python

Author : Frank Zickert
Publisher : Independently Published
ISBN 13 :
Total Pages : 440 pages
Book Rating : 4.5/5 (165 download)

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Book Synopsis Hands-On Quantum Machine Learning With Python by : Frank Zickert

Download or read book Hands-On Quantum Machine Learning With Python written by Frank Zickert and published by Independently Published. This book was released on 2021-06-19 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: You're interested in quantum computing and machine learning. But you don't know how to get started? Let me help! Whether you just get started with quantum computing and machine learning or you're already a senior machine learning engineer, Hands-On Quantum Machine Learning With Python is your comprehensive guide to get started with Quantum Machine Learning - the use of quantum computing for the computation of machine learning algorithms. Quantum computing promises to solve problems intractable with current computing technologies. But is it fundamentally different and asks us to change the way we think. Hands-On Quantum Machine Learning With Python strives to be the perfect balance between theory taught in a textbook and the actual hands-on knowledge you'll need to implement real-world solutions. Inside this book, you will learn the basics of quantum computing and machine learning in a practical and applied manner.

Quantum Machine Learning With Python

Author : Santanu Pattanayak
Publisher : Apress
ISBN 13 : 9781484265215
Total Pages : 295 pages
Book Rating : 4.2/5 (652 download)

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Book Synopsis Quantum Machine Learning With Python by : Santanu Pattanayak

Download or read book Quantum Machine Learning With Python written by Santanu Pattanayak and published by Apress. This book was released on 2021-03-29 with total page 295 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quickly scale up to Quantum computing and Quantum machine learning foundations and related mathematics and expose them to different use cases that can be solved through Quantum based algorithms.This book explains Quantum Computing, which leverages the Quantum mechanical properties sub-atomic particles. It also examines Quantum machine learning, which can help solve some of the most challenging problems in forecasting, financial modeling, genomics, cybersecurity, supply chain logistics, cryptography among others. You'll start by reviewing the fundamental concepts of Quantum Computing, such as Dirac Notations, Qubits, and Bell state, followed by postulates and mathematical foundations of Quantum Computing. Once the foundation base is set, you'll delve deep into Quantum based algorithms including Quantum Fourier transform, phase estimation, and HHL (Harrow-Hassidim-Lloyd) among others. You'll then be introduced to Quantum machine learning and Quantum deep learning-based algorithms, along with advanced topics of Quantum adiabatic processes and Quantum based optimization. Throughout the book, there are Python implementations of different Quantum machine learning and Quantum computing algorithms using the Qiskit toolkit from IBM and Cirq from Google Research. What You'll Learn Understand Quantum computing and Quantum machine learning Explore varied domains and the scenarios where Quantum machine learning solutions can be applied Develop expertise in algorithm development in varied Quantum computing frameworks Review the major challenges of building large scale Quantum computers and applying its various techniques Who This Book Is For Machine Learning enthusiasts and engineers who want to quickly scale up to Quantum Machine Learning

Reservoir Computing

Author : Kohei Nakajima
Publisher : Springer
ISBN 13 : 9789811316869
Total Pages : 458 pages
Book Rating : 4.3/5 (168 download)

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Book Synopsis Reservoir Computing by : Kohei Nakajima

Download or read book Reservoir Computing written by Kohei Nakajima and published by Springer. This book was released on 2021-08-06 with total page 458 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the first comprehensive book about reservoir computing (RC). RC is a powerful and broadly applicable computational framework based on recurrent neural networks. Its advantages lie in small training data set requirements, fast training, inherent memory and high flexibility for various hardware implementations. It originated from computational neuroscience and machine learning but has, in recent years, spread dramatically, and has been introduced into a wide variety of fields, including complex systems science, physics, material science, biological science, quantum machine learning, optical communication systems, and robotics. Reviewing the current state of the art and providing a concise guide to the field, this book introduces readers to its basic concepts, theory, techniques, physical implementations and applications. The book is sub-structured into two major parts: theory and physical implementations. Both parts consist of a compilation of chapters, authored by leading experts in their respective fields. The first part is devoted to theoretical developments of RC, extending the framework from the conventional recurrent neural network context to a more general dynamical systems context. With this broadened perspective, RC is not restricted to the area of machine learning but is being connected to a much wider class of systems. The second part of the book focuses on the utilization of physical dynamical systems as reservoirs, a framework referred to as physical reservoir computing. A variety of physical systems and substrates have already been suggested and used for the implementation of reservoir computing. Among these physical systems which cover a wide range of spatial and temporal scales, are mechanical and optical systems, nanomaterials, spintronics, and quantum many body systems. This book offers a valuable resource for researchers (Ph.D. students and experts alike) and practitioners working in the field of machine learning, artificial intelligence, robotics, neuromorphic computing, complex systems, and physics.

Concise Guide to Computation Theory

Author : Akira Maruoka
Publisher : Springer Science & Business Media
ISBN 13 : 0857295357
Total Pages : 281 pages
Book Rating : 4.8/5 (572 download)

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Book Synopsis Concise Guide to Computation Theory by : Akira Maruoka

Download or read book Concise Guide to Computation Theory written by Akira Maruoka and published by Springer Science & Business Media. This book was released on 2011-04-29 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook presents a thorough foundation to the theory of computation. Combining intuitive descriptions and illustrations with rigorous arguments and detailed proofs for key topics, the logically structured discussion guides the reader through the core concepts of automata and languages, computability, and complexity of computation. Topics and features: presents a detailed introduction to the theory of computation, complete with concise explanations of the mathematical prerequisites; provides end-of-chapter problems with solutions, in addition to chapter-opening summaries and numerous examples and definitions throughout the text; draws upon the author’s extensive teaching experience and broad research interests; discusses finite automata, context-free languages, and pushdown automata; examines the concept, universality and limitations of the Turing machine; investigates computational complexity based on Turing machines and Boolean circuits, as well as the notion of NP-completeness.