Quantum Neural Computation

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Quantum Neural Computation

Author : Vladimir G. Ivancevic
Publisher : Springer Science & Business Media
ISBN 13 : 9048133505
Total Pages : 938 pages
Book Rating : 4.0/5 (481 download)

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Book Synopsis Quantum Neural Computation by : Vladimir G. Ivancevic

Download or read book Quantum Neural Computation written by Vladimir G. Ivancevic and published by Springer Science & Business Media. This book was released on 2010-01-18 with total page 938 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantum Neural Computation is a graduate–level monographic textbook. It presents a comprehensive introduction, both non-technical and technical, into modern quantum neural computation, the science behind the fiction movie Stealth. Classical computing systems perform classical computations (i.e., Boolean operations, such as AND, OR, NOT gates) using devices that can be described classically (e.g., MOSFETs). On the other hand, quantum computing systems perform classical computations using quantum devices (quantum dots), that is devices that can be described only using quantum mechanics. Any information transfer between such computing systems involves a state measurement. This book describes this information transfer at the edge of classical and quantum chaos and turbulence, where mysterious quantum-mechanical linearity meets even more mysterious brain’s nonlinear complexity, in order to perform a super–high–speed and error–free computations. This monograph describes a crossroad between quantum field theory, brain science and computational intelligence.

Introduction To The Theory Of Neural Computation

Author : John A. Hertz
Publisher : CRC Press
ISBN 13 : 0429968213
Total Pages : 352 pages
Book Rating : 4.4/5 (299 download)

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Book Synopsis Introduction To The Theory Of Neural Computation by : John A. Hertz

Download or read book Introduction To The Theory Of Neural Computation written by John A. Hertz and published by CRC Press. This book was released on 2018-03-08 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive introduction to the neural network models currently under intensive study for computational applications. It also provides coverage of neural network applications in a variety of problems of both theoretical and practical interest.

Quantum Computing for the Brain

Author : Melanie Swan
Publisher : Wspc (Europe)
ISBN 13 : 9781800610613
Total Pages : 512 pages
Book Rating : 4.6/5 (16 download)

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Book Synopsis Quantum Computing for the Brain by : Melanie Swan

Download or read book Quantum Computing for the Brain written by Melanie Swan and published by Wspc (Europe). This book was released on 2022 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantum Computing for the Brain argues that the brain is the killer application for quantum computing. No other system is as complex, as multidimensional in time and space, as dynamic, as less well-understood, as of peak interest, and as in need of three-dimensional modeling as it functions in real-life, as the brain. Quantum computing has emerged as a platform suited to contemporary data processing needs, surpassing classical computing and supercomputing. This book shows how quantum computing's increased capacity to model classical data with quantum states and the ability to run more complex permutations of problems can be employed in neuroscience applications such as neural signaling and synaptic integration. State-of-the-art methods are discussed such as quantum machine learning, tensor networks, Born machines, quantum kernel learning, wavelet transforms, Rydberg atom arrays, ion traps, boson sampling, graph-theoretic models, quantum optical machine learning, neuromorphic architectures, spiking neural networks, quantum teleportation, and quantum walks. Quantum Computing for the Brain is a comprehensive one-stop resource for an improved understanding of the converging research frontiers of foundational physics, information theory, and neuroscience in the context of quantum computing.

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.

Introduction to Quantum Neural Technologies

Author : Alexandr A. Ezhov
Publisher :
ISBN 13 :
Total Pages : 248 pages
Book Rating : 4.:/5 (318 download)

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Book Synopsis Introduction to Quantum Neural Technologies by : Alexandr A. Ezhov

Download or read book Introduction to Quantum Neural Technologies written by Alexandr A. Ezhov and published by . This book was released on 2003 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a first introductory book in Quantum Neural Technology -- a new and promising area of informatics. Combination of the ideas from quantum computing and neural computing raises the possibility of dramatically decreasing the complexity of neural systems by replacing networks of classical neurons with a single quantum neuron. In the first two chapters, the fundamentals of neural technologies and of quantum computing are presented. In the third chapter, it is outlined how the problems typical for classical neural technology can be solved by using quantum neural technology. This book is very useful to students, teachers, researchers, and engineers, who are working in informatics or just interested in being briefly aware of it.

Advanced Methods in Neural Computing

Author : Philip D. Wasserman
Publisher : Van Nostrand Reinhold Company
ISBN 13 :
Total Pages : 280 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Advanced Methods in Neural Computing by : Philip D. Wasserman

Download or read book Advanced Methods in Neural Computing written by Philip D. Wasserman and published by Van Nostrand Reinhold Company. This book was released on 1993 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the engineer's guide to artificial neural networks, the advanced computing innovation which is posed to sweep into the world of business and industry. The author presents the basic principles and advanced concepts by means of high-performance paradigms which function effectively in real-world situations.

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 Computing For The Brain

Author : Melanie Swan
Publisher : World Scientific
ISBN 13 : 1800610637
Total Pages : 551 pages
Book Rating : 4.8/5 (6 download)

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Book Synopsis Quantum Computing For The Brain by : Melanie Swan

Download or read book Quantum Computing For The Brain written by Melanie Swan and published by World Scientific. This book was released on 2022-05-30 with total page 551 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantum Computing for the Brain argues that the brain is the killer application for quantum computing. No other system is as complex, as multidimensional in time and space, as dynamic, as less well-understood, as of peak interest, and as in need of three-dimensional modeling as it functions in real-life, as the brain.Quantum computing has emerged as a platform suited to contemporary data processing needs, surpassing classical computing and supercomputing. This book shows how quantum computing's increased capacity to model classical data with quantum states and the ability to run more complex permutations of problems can be employed in neuroscience applications such as neural signaling and synaptic integration. State-of-the-art methods are discussed such as quantum machine learning, tensor networks, Born machines, quantum kernel learning, wavelet transforms, Rydberg atom arrays, ion traps, boson sampling, graph-theoretic models, quantum optical machine learning, neuromorphic architectures, spiking neural networks, quantum teleportation, and quantum walks.Quantum Computing for the Brain is a comprehensive one-stop resource for an improved understanding of the converging research frontiers of foundational physics, information theory, and neuroscience in the context of quantum computing.

Future Directions for Intelligent Systems and Information Sciences

Author : Nikola Kasabov
Publisher : Physica
ISBN 13 : 3790818569
Total Pages : 411 pages
Book Rating : 4.7/5 (98 download)

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Book Synopsis Future Directions for Intelligent Systems and Information Sciences by : Nikola Kasabov

Download or read book Future Directions for Intelligent Systems and Information Sciences written by Nikola Kasabov and published by Physica. This book was released on 2013-11-11 with total page 411 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited volume comprises invited chapters that cover five areas of the current and the future development of intelligent systems and information sciences. Half of the chapters were presented as invited talks at the Workshop "Future Directions for Intelligent Systems and Information Sciences" held in Dunedin, New Zealand, 22-23 November 1999 after the International Conference on Neuro-Information Processing (lCONIPI ANZIISI ANNES '99) held in Perth, Australia. In order to make this volume useful for researchers and academics in the broad area of information sciences I invited prominent researchers to submit materials and present their view about future paradigms, future trends and directions. Part I contains chapters on adaptive, evolving, learning systems. These are systems that learn in a life-long, on-line mode and in a changing environment. The first chapter, written by the editor, presents briefly the paradigm of Evolving Connectionist Systems (ECOS) and some of their applications. The chapter by Sung-Bae Cho presents the paradigms of artificial life and evolutionary programming in the context of several applications (mobile robots, adaptive agents of the WWW). The following three chapters written by R.Duro, J.Santos and J.A.Becerra (chapter 3), GCoghill . (chapter 4), Y.Maeda (chapter 5) introduce new techniques for building adaptive, learning robots.

An Introduction to Neural Computing

Author : Igor Aleksander
Publisher : Van Nostrand Reinhold Company
ISBN 13 :
Total Pages : 276 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis An Introduction to Neural Computing by : Igor Aleksander

Download or read book An Introduction to Neural Computing written by Igor Aleksander and published by Van Nostrand Reinhold Company. This book was released on 1990 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second edition of this text has been updated and includes material on new developments including neurocontrol, pattern analysis and dynamic systems. The book should be useful for undergraduate students of neural networks.

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

Handbook of Neural Computing Applications

Author : Alianna J. Maren
Publisher : Academic Press
ISBN 13 : 148326484X
Total Pages : 472 pages
Book Rating : 4.4/5 (832 download)

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Book Synopsis Handbook of Neural Computing Applications by : Alianna J. Maren

Download or read book Handbook of Neural Computing Applications written by Alianna J. Maren and published by Academic Press. This book was released on 2014-05-10 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Neural Computing Applications is a collection of articles that deals with neural networks. Some papers review the biology of neural networks, their type and function (structure, dynamics, and learning) and compare a back-propagating perceptron with a Boltzmann machine, or a Hopfield network with a Brain-State-in-a-Box network. Other papers deal with specific neural network types, and also on selecting, configuring, and implementing neural networks. Other papers address specific applications including neurocontrol for the benefit of control engineers and for neural networks researchers. Other applications involve signal processing, spatio-temporal pattern recognition, medical diagnoses, fault diagnoses, robotics, business, data communications, data compression, and adaptive man-machine systems. One paper describes data compression and dimensionality reduction methods that have characteristics, such as high compression ratios to facilitate data storage, strong discrimination of novel data from baseline, rapid operation for software and hardware, as well as the ability to recognized loss of data during compression or reconstruction. The collection can prove helpful for programmers, computer engineers, computer technicians, and computer instructors dealing with many aspects of computers related to programming, hardware interface, networking, engineering or design.

Quantum Neural Computation and Associative Memory

Author : Tamer Salman
Publisher :
ISBN 13 :
Total Pages : 78 pages
Book Rating : 4.:/5 (87 download)

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Book Synopsis Quantum Neural Computation and Associative Memory by : Tamer Salman

Download or read book Quantum Neural Computation and Associative Memory written by Tamer Salman and published by . This book was released on 2012 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Handbook of Neural Computation

Author : E Fiesler
Publisher : CRC Press
ISBN 13 : 9780750303125
Total Pages : 436 pages
Book Rating : 4.3/5 (31 download)

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Book Synopsis Handbook of Neural Computation by : E Fiesler

Download or read book Handbook of Neural Computation written by E Fiesler and published by CRC Press. This book was released on 1996-01-01 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Handbook of Neural Computation is a practical, hands-on guide to the design and implementation of neural networks used by scientists and engineers to tackle difficult and/or time-consuming problems. The handbook bridges an information pathway between scientists and engineers in different disciplines who apply neural networks to similar problems. It is unmatched in the breadth of its coverage and is certain to become the standard reference resource for the neural network community.

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

Author : Pethuru Raj
Publisher : Walter de Gruyter GmbH & Co KG
ISBN 13 : 3111342271
Total Pages : 336 pages
Book Rating : 4.1/5 (113 download)

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Book Synopsis Quantum Machine Learning by : Pethuru Raj

Download or read book Quantum Machine Learning written by Pethuru Raj and published by Walter de Gruyter GmbH & Co KG. This book was released on 2024-08-05 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantum computing has shown a potential to tackle specific types of problems, especially those involving a daunting number of variables, at an exponentially faster rate compared to classical computers. This volume focuses on quantum variants of machine learning algorithms, such as quantum neural networks, quantum reinforcement learning, quantum principal component analysis, quantum support vectors, quantum Boltzmann machines, and many more.

Quantum Neural Networks

Author : Nam H. Nguyen
Publisher :
ISBN 13 :
Total Pages : 225 pages
Book Rating : 4.:/5 (117 download)

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Book Synopsis Quantum Neural Networks by : Nam H. Nguyen

Download or read book Quantum Neural Networks written by Nam H. Nguyen and published by . This book was released on 2020 with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantum computing is becoming a reality, at least on a small scale. However, designing a good quantum algorithm is still a challenging task. This has been a huge major bottleneck in quantum computation for years. In this work, we will show that it is possible to take a detour from the conventional programming approach by incorporating machine learning techniques, speci cally neural networks, to train a quantum system such that the desired algorithm is \learned," thus obviating the program design obstacle. Our work here merges quantum computing and neural networks to form what we call \Quantum Neural Networks" (QNNs). Another serious issue one needs to overcome when doing anything quantum is the problem of \noise and decoherence". A well-known technique to overcome this issue is using error correcting code. However, error correction schemes require an enormous amount of additional ancilla qubits, which is not feasible for the current state-of-the-art quantum computing devices or any near-term devices for that matter. We show in this work that QNNs are robust to noise and decoherence, provide error suppression quantum algorithms. Furthermore, not only are our QNN models robust to noise and decoherehce, we show that they also possess an inherent speed-up, in term of being able to learned a task much faster, over various classical neural networks, at least on the set of problems we benchmarked them on. Afterward, we show that although our QNN model is designed to run on a fundamental level of a quantum system, we can also decompose it into a sequence of gates and implement it on current quantum hardware devices. We did this for a non-trivial problem known as the \entanglement witness" calculation. We then propose a couple of di erent hybrid quantum neural network architectures, networks with both quantum and classical information processing. We hope that this might increase the capability over previous QNN models in terms of the complexity of the problems it might be able to solve.