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Quantum Neural Computation
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Author :Vladimir G. Ivancevic Publisher :Springer Science & Business Media ISBN 13 :9048133505 Total Pages :929 pages Book Rating :4.0/5 (481 download)
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 929 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.
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.
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.
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.
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.
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.
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.
Book Synopsis Handbook of Neural Computation by : Pijush Samui
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
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.
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
Book Synopsis Neural-Network Simulation of Strongly Correlated Quantum Systems by : Stefanie Czischek
Download or read book Neural-Network Simulation of Strongly Correlated Quantum Systems written by Stefanie Czischek and published by Springer Nature. This book was released on 2020-08-27 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantum systems with many degrees of freedom are inherently difficult to describe and simulate quantitatively. The space of possible states is, in general, exponentially large in the number of degrees of freedom such as the number of particles it contains. Standard digital high-performance computing is generally too weak to capture all the necessary details, such that alternative quantum simulation devices have been proposed as a solution. Artificial neural networks, with their high non-local connectivity between the neuron degrees of freedom, may soon gain importance in simulating static and dynamical behavior of quantum systems. Particularly promising candidates are neuromorphic realizations based on analog electronic circuits which are being developed to capture, e.g., the functioning of biologically relevant networks. In turn, such neuromorphic systems may be used to measure and control real quantum many-body systems online. This thesis lays an important foundation for the realization of quantum simulations by means of neuromorphic hardware, for using quantum physics as an input to classical neural nets and, in turn, for using network results to be fed back to quantum systems. The necessary foundations on both sides, quantum physics and artificial neural networks, are described, providing a valuable reference for researchers from these different communities who need to understand the foundations of both.
Book Synopsis Quantum Computation and Quantum Information by : Michael A. Nielsen
Download or read book Quantum Computation and Quantum Information written by Michael A. Nielsen and published by Cambridge University Press. This book was released on 2010-12-09 with total page 709 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the most cited books in physics of all time, Quantum Computation and Quantum Information remains the best textbook in this exciting field of science. This 10th anniversary edition includes an introduction from the authors setting the work in context. This comprehensive textbook describes such remarkable effects as fast quantum algorithms, quantum teleportation, quantum cryptography and quantum error-correction. Quantum mechanics and computer science are introduced before moving on to describe what a quantum computer is, how it can be used to solve problems faster than 'classical' computers and its real-world implementation. It concludes with an in-depth treatment of quantum information. Containing a wealth of figures and exercises, this well-known textbook is ideal for courses on the subject, and will interest beginning graduate students and researchers in physics, computer science, mathematics, and electrical engineering.
Book Synopsis Neural Networks and Analog Computation by : Hava T. Siegelmann
Download or read book Neural Networks and Analog Computation written by Hava T. Siegelmann and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 193 pages. Available in PDF, EPUB and Kindle. Book excerpt: The theoretical foundations of Neural Networks and Analog Computation conceptualize neural networks as a particular type of computer consisting of multiple assemblies of basic processors interconnected in an intricate structure. Examining these networks under various resource constraints reveals a continuum of computational devices, several of which coincide with well-known classical models. On a mathematical level, the treatment of neural computations enriches the theory of computation but also explicated the computational complexity associated with biological networks, adaptive engineering tools, and related models from the fields of control theory and nonlinear dynamics. The material in this book will be of interest to researchers in a variety of engineering and applied sciences disciplines. In addition, the work may provide the base of a graduate-level seminar in neural networks for computer science students.
Book Synopsis Quantum Information Processing and Quantum Error Correction by : Ivan Djordjevic
Download or read book Quantum Information Processing and Quantum Error Correction written by Ivan Djordjevic and published by Academic Press. This book was released on 2012-04-16 with total page 597 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantum Information Processing and Quantum Error Correction is a self-contained, tutorial-based introduction to quantum information, quantum computation, and quantum error-correction. Assuming no knowledge of quantum mechanics and written at an intuitive level suitable for the engineer, the book gives all the essential principles needed to design and implement quantum electronic and photonic circuits. Numerous examples from a wide area of application are given to show how the principles can be implemented in practice. This book is ideal for the electronics, photonics and computer engineer who requires an easy- to-understand foundation on the principles of quantum information processing and quantum error correction, together with insight into how to develop quantum electronic and photonic circuits. Readers of this book will be ready for further study in this area, and will be prepared to perform independent research. The reader completed the book will be able design the information processing circuits, stabilizer codes, Calderbank-Shor-Steane (CSS) codes, subsystem codes, topological codes and entanglement-assisted quantum error correction codes; and propose corresponding physical implementation. The reader completed the book will be proficient in quantum fault-tolerant design as well. Unique Features Unique in covering both quantum information processing and quantum error correction - everything in one book that an engineer needs to understand and implement quantum-level circuits. Gives an intuitive understanding by not assuming knowledge of quantum mechanics, thereby avoiding heavy mathematics. In-depth coverage of the design and implementation of quantum information processing and quantum error correction circuits. Provides the right balance among the quantum mechanics, quantum error correction, quantum computing and quantum communication. Dr. Djordjevic is an Assistant Professor in the Department of Electrical and Computer Engineering of College of Engineering, University of Arizona, with a joint appointment in the College of Optical Sciences. Prior to this appointment in August 2006, he was with University of Arizona, Tucson, USA (as a Research Assistant Professor); University of the West of England, Bristol, UK; University of Bristol, Bristol, UK; Tyco Telecommunications, Eatontown, USA; and National Technical University of Athens, Athens, Greece. His current research interests include optical networks, error control coding, constrained coding, coded modulation, turbo equalization, OFDM applications, and quantum error correction. He presently directs the Optical Communications Systems Laboratory (OCSL) within the ECE Department at the University of Arizona. Provides everything an engineer needs in one tutorial-based introduction to understand and implement quantum-level circuits Avoids the heavy use of mathematics by not assuming the previous knowledge of quantum mechanics Provides in-depth coverage of the design and implementation of quantum information processing and quantum error correction circuits
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.
Book Synopsis Quantum Inspired Computational Intelligence by : Siddhartha Bhattacharyya
Download or read book Quantum Inspired Computational Intelligence written by Siddhartha Bhattacharyya and published by Morgan Kaufmann. This book was released on 2016-09-20 with total page 506 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantum Inspired Computational Intelligence: Research and Applications explores the latest quantum computational intelligence approaches, initiatives, and applications in computing, engineering, science, and business. The book explores this emerging field of research that applies principles of quantum mechanics to develop more efficient and robust intelligent systems. Conventional computational intelligence—or soft computing—is conjoined with quantum computing to achieve this objective. The models covered can be applied to any endeavor which handles complex and meaningful information. Brings together quantum computing with computational intelligence to achieve enhanced performance and robust solutions Includes numerous case studies, tools, and technologies to apply the concepts to real world practice Provides the missing link between the research and practice
Book Synopsis Quantum Implications by : Basil Hiley
Download or read book Quantum Implications written by Basil Hiley and published by Routledge. This book was released on 2012-06-25 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: David Bohm is one of the foremost scientific thinkers of today and one of the most distinguished scientists of his generation. His challenge to the conventional understanding of quantum theory has led scientists to reexamine what it is they are going and his ideas have been an inspiration across a wide range of disciplines. Quantum Implications is a collection of original contributions by many of the world' s leading scholars and is dedicated to David Bohm, his work and the issues raised by his ideas. The contributors range across physics, philosophy, biology, art, psychology, and include some of the most distinguished scientists of the day. There is an excellent introduction by the editors, putting Bohm's work in context and setting right some of the misconceptions that have persisted about the work of David Bohm
