A new discipline, Quantum Information Science, has emerged in the last two decades of the twentieth century at the intersection of Physics, Mathematics, and Computer Science. Quantum Information Processing is an application of Quantum Information Science which covers the transformation, storage, and transmission of quantum information; it represents a revolutionary approach to information processing. Classical and Quantum Information covers topics in quantum computing, quantum information theory, and quantum error correction, three important areas of quantum information processing. Quantum information theory and quantum error correction build on the scope, concepts, methodology, and techniques developed in the context of their close relatives, classical information theory and classical error correcting codes. - Presents recent results in quantum computing, quantum information theory, and quantum error correcting codes - Covers both classical and quantum information theory and error correcting codes - The last chapter of the book covers physical implementation of quantum information processing devices - Covers the mathematical formalism and the concepts in Quantum Mechanics critical for understanding the properties and the transformations of quantum information

A self-contained, graduate-level textbook that develops from scratch classical results as well as advances of the past decade.

A detailed formulation of neural networks from the information-theoretic viewpoint. The authors show how this perspective provides new insights into the design theory of neural networks. In particular they demonstrate how these methods may be applied to the topics of supervised and unsupervised learning, including feature extraction, linear and non-linear independent component analysis, and Boltzmann machines. Readers are assumed to have a basic understanding of neural networks, but all the relevant concepts from information theory are carefully introduced and explained. Consequently, readers from varied scientific disciplines, notably cognitive scientists, engineers, physicists, statisticians, and computer scientists, will find this an extremely valuable introduction to this topic.

Failure-Tolerant Computer Design focuses on the use of redundancy theory in improving the reliability of computers. The book first offers information on redundancy theory and limit theorems. Discussions focus on applications in determining the optimum placement of restoring organs; time asymptotes for log failure probability for exponential survival probability; reliability of multiple-function system with paralleled individual units; and basic concepts for making reliable computers out of unreliable parts. The text then examines decision theory in redundant systems and adaptive decision elements. The publication examines the interconnection structure for redundant logic and redundant relay theory. Topics include Moore-Shannon limit theorem; systematic groupings of inputs in single-layer error-correcting interwoven redundant logic; interwoven logic with alternating-layer error correction; and interwoven logic with single-layer error correction. The book also elaborates on transition analyses in reliability theory, including Markov chain theory and probability bounds in Markov chains having many states or inexactly known transition matrices. The manuscript is a vital source of data for engineers and researchers interested in failure-tolerant computer design.

"This comprehensive reference work provides immediate, fingertip access to state-of-the-art technology in nearly 700 self-contained articles written by over 900 international authorities. Each article in the Encyclopedia features current developments and trends in computers, software, vendors, and applications...extensive bibliographies of leading figures in the field, such as Samuel Alexander, John von Neumann, and Norbert Wiener...and in-depth analysis of future directions."

Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.