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• Methods.- Syntactic classifications of time series.- Fuzzy logic approach to classification.- Discrete Mathematical Analysis and clustering.- Intellectual Geographic Information Systems.-Fuzzy Logic Algorithmic System for Anomaly Recognition (FLASAR).- Data and applications.- Aeromagnetic surveys: spatial magnetic anomalies recognition.- GIS data layers: world magnetic atlas.- Electromagnetic data: monitoring of volcanic activities.- Geodynamics.- Seismological data.- INTERMAGNET observations and data processing.- Extreme events (Xevents).
• (source: Nielsen Book Data)

Geomagnetic field penetrates through all shells of the solid Earth, hydrosphere and atmosphere, spreading into space. The Earth Magnetic Field plays a key-role in major natural processes. Geomagnetic field variations in time and space provide important information about the state of the solid Earth, as well as the solar-terrestrial relationships and space weather conditions. The monograph presents a set of fundamental and, at the same time, urgent scientific problems of modern geomagnetic studies, as well as describes the results of the authors' developments. The new technique introduced in the book can be applied far beyond the limits of Earth sciences. Requirements to corresponding data models are formulated. The conducted experimental investigations are combined with development and implementation of new methods of mathematical modeling, artificial intelligence, systems analysis and data science to solve the fundamental problems of geomagnetism. At that, formalism of Big Data and its application to Earth Sciences is presented as essential part of systems analysis. The book is intended for research scientists, tutors, students, postgraduate students and engineers working in geomagnetism and Earth sciences in general, as well as in other relevant scientific disciplines.
(source: Nielsen Book Data)

• Introduction.- Quantum Mechanics and Spin Systems.- Artificial Neural Networks.- Discrete Truncated Wigner Approximation.- BM-Based Wave Function Parametrization.- Deep Neural Networks and Phase Reweighting.- Towards Neuromorphic Sampling of Quantum States.- Conclusion.
• (source: Nielsen Book Data)

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.
(source: Nielsen Book Data)

• Introduction
• Tensor Network: Basic Definitions and Properties
• Two-Dimensional Tensor Networks and Contraction Algorithms
• Tensor Network Approaches for Higher-Dimensional Quantum Lattice Models
• Tensor Network Contraction and Multi-Linear Algebra
• Quantum Entanglement Simulation Inspired by Tensor Network
• Summary.

Tensor network is a fundamental mathematical tool with a huge range of applications in physics, such as condensed matter physics, statistic physics, high energy physics, and quantum information sciences. This open access book aims to explain the tensor network contraction approaches in a systematic way, from the basic definitions to the important applications. This book is also useful to those who apply tensor networks in areas beyond physics, such as machine learning and the big-data analysis. Tensor network originates from the numerical renormalization group approach proposed by K.G. Wilson in 1975. Through a rapid development in the last two decades, tensor network has become a powerful numerical tool that can efficiently simulate a wide range of scientific problems, with particular success in quantum many-body physics. Varieties of tensor network algorithms have been proposed for different problems. However, the connections among different algorithms are not well discussed or reviewed. To fill this gap, this book explains the fundamental concepts and basic ideas that connect and/or unify different strategies of the tensor network contraction algorithms. In addition, some of the recent progresses in dealing with tensor decomposition techniques and quantum simulations are also represented in this book to help the readers to better understand tensor network. This open access book is intended for graduated students, but can also be used as a professional book for researchers in the related fields. To understand most of the contents in the book, only basic knowledge of quantum mechanics and linear algebra is required. In order to fully understand some advanced parts, the reader will need to be familiar with notion of condensed matter physics and quantum information, that however are not necessary to understand the main parts of the book. This book is a good source for non-specialists on quantum physics to understand tensor network algorithms and the related mathematics.

• Introduction
• QCA Terminology
• Design of Basic Digital Circuits in QCA
• Design of Ripple Carry and Prefix Adders in QCA
• Design of A Hybrid Adder in QCA
• Design of a Multiplier in QCA
• Discrete Hadamard Transform Computation in QCA
• Study of Thermal Robustness of QCA Designs
• The Road Ahead
• A Tutorial on Qcadesigner.

This research monograph focuses on the design of arithmetic circuits in Quantum Dot Cellular Automata (QCA). Using the fact that the 3-input majority gate is a primitive in QCA, the book sets out to discover hitherto unknown properties of majority logic in the context of arithmetic circuit designs. The pursuit for efficient adders in QCA takes two forms. One involves application of the new results in majority logic to existing adders. The second involves development of a custom adder for QCA technology. A QCA adder named as hybrid adder is proposed and it is shown that it outperforms existing multi-bit adders with respect to area and delay. The work is extended to the design of a low-complexity multiplier for signed numbers in QCA. Furthermore the book explores two aspects unique to QCA technology, namely thermal robustness and the role of interconnects. In addition, the book introduces the reader to QCA layout design and simulation using QCADesigner. Features & Benefits: This research-based book: · Introduces the reader to Quantum Dot Cellular Automata, an emerging nanotechnology. · Explores properties of majority logic. · Demonstrates application of the properties to design efficient arithmetic circuits. · Guides the reader towards layout design and simulation in QCADesigner.