Quantum Neural Computation

Author : Vladimir G. Ivancevic
Publisher : Springer Science & Business Media
ISBN 13 : 9048133505
Total Pages : 938 pages
Book Rating : 4.05/5 ( download)

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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.

Author : John A. Hertz
Publisher : CRC Press
ISBN 13 : 0429968213
Total Pages : 352 pages
Book Rating : 4.11/5 ( download)

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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.

Author : Melanie Swan
Publisher : World Scientific
ISBN 13 : 1800610637
Total Pages : 551 pages
Book Rating : 4.37/5 ( download)

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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.

Author : Alexandr A. Ezhov
Publisher :
ISBN 13 :
Total Pages : 248 pages
Book Rating : 4.48/5 ( download)

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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.

Author : Nikola Kasabov
Publisher : Physica
ISBN 13 : 3790818569
Total Pages : 411 pages
Book Rating : 4.67/5 ( download)

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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.

Author : Maria Schuld
Publisher : Springer Nature
ISBN 13 : 3030830985
Total Pages : 321 pages
Book Rating : 4.84/5 ( download)

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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.

Author : Maria Schuld
Publisher : Springer
ISBN 13 : 3319964240
Total Pages : 293 pages
Book Rating : 4.49/5 ( download)

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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.

Author : Philip D. Wasserman
Publisher : Van Nostrand Reinhold Company
ISBN 13 :
Total Pages : 280 pages
Book Rating : 4.01/5 ( download)

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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.

Author : Pijush Samui
Publisher : Academic Press
ISBN 13 : 0128113197
Total Pages : 658 pages
Book Rating : 4.96/5 ( download)

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Download or read book Handbook of Neural Computation written by Pijush Samui and published by Academic Press. This book was released on 2017-07-18 with total page 658 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Neural Computation explores neural computation applications, ranging from conventional fields of mechanical and civil engineering, to electronics, electrical engineering and computer science. This book covers the numerous applications of artificial and deep neural networks and their uses in learning machines, including image and speech recognition, natural language processing and risk analysis. Edited by renowned authorities in this field, this work is comprised of articles from reputable industry and academic scholars and experts from around the world. Each contributor presents a specific research issue with its recent and future trends. As the demand rises in the engineering and medical industries for neural networks and other machine learning methods to solve different types of operations, such as data prediction, classification of images, analysis of big data, and intelligent decision-making, this book provides readers with the latest, cutting-edge research in one comprehensive text. Features high-quality research articles on multivariate adaptive regression splines, the minimax probability machine, and more Discusses machine learning techniques, including classification, clustering, regression, web mining, information retrieval and natural language processing Covers supervised, unsupervised, reinforced, ensemble, and nature-inspired learning methods

Author : Peter Wittek
Publisher : Academic Press
ISBN 13 : 0128010991
Total Pages : 176 pages
Book Rating : 4.90/5 ( download)

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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