Neuralnetwork simulation of strongly correlated quantum systems
 Responsibility
 Stefanie Czischek.
 Digital
 text file
 Publication
 Cham, Switzerland : Springer, [2020]
 Physical description
 1 online resource (xv, 205 pages) : illustrations
 Series
 Springer theses. 21905061
Online
More options
Description
Creators/Contributors
 Author/Creator
 Czischek, Stefanie, 1990 author.
Contents/Summary
 Bibliography
 Includes bibliographical references.
 Contents

 Introduction. Quantum Mechanics and Spin Systems. Artificial Neural Networks. Discrete Truncated Wigner Approximation. BMBased Wave Function Parametrization. Deep Neural Networks and Phase Reweighting. Towards Neuromorphic Sampling of Quantum States. Conclusion.
 (source: Nielsen Book Data)
 Publisher's summary

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 highperformance 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 nonlocal 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 manybody 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)
Subjects
 Subjects
 Quantum systems.
 Quantum theory.
 Neural networks (Computer science)
 Quantum Theory
 Neural Networks, Computer
 Systèmes quantiques.
 Théorie quantique.
 Réseaux neuronaux (Informatique)
 Machine learning.
 Mathematical modelling.
 Materials > States of matter.
 Quantum physics (quantum mechanics & quantum field theory)
 Computers > Intelligence (AI) & Semantics.
 Mathematics > Applied.
 Science > Solid State Physics.
 Science > Quantum Theory.
Bibliographic information
 Publication date
 2020
 Series
 Springer theses, 21905061
 Note
 "Doctoral thesis accepted by Heidelberg University, Heidelberg, Germany."
 ISBN
 9783030527150 (electronic book)
 3030527158 (electronic book)
 9783030527143 (print)
 303052714X (print)
 DOI
 10.1007/9783030527150