Overview
- Nominated as an outstanding Ph.D. thesis by the Heidelberg University, Heidelberg, Germany
- General introduction to quantum many-body physics and artificial neural networks
- Deep discussions of simulating quantum spin systems with artificial neural networks
- 48 color images illustrating fundamentals and results
Part of the book series: Springer Theses (Springer Theses)
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Table of contents (8 chapters)
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Front Matter
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Background
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Front Matter
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Simulations of Quantum Many-Body Systems
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Front Matter
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Relating Quantum Systems and Neuromorphic Hardware
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Front Matter
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Keywords
About this book
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.
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Neural-Network Simulation of Strongly Correlated Quantum Systems
Authors: Stefanie Czischek
Series Title: Springer Theses
DOI: https://doi.org/10.1007/978-3-030-52715-0
Publisher: Springer Cham
eBook Packages: Physics and Astronomy, Physics and Astronomy (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-52714-3Published: 28 August 2020
Softcover ISBN: 978-3-030-52717-4Published: 28 August 2021
eBook ISBN: 978-3-030-52715-0Published: 27 August 2020
Series ISSN: 2190-5053
Series E-ISSN: 2190-5061
Edition Number: 1
Number of Pages: XV, 205
Number of Illustrations: 3 b/w illustrations, 48 illustrations in colour
Topics: Quantum Physics, Machine Learning, Mathematical Models of Cognitive Processes and Neural Networks, Condensed Matter Physics