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Quenches near Ising quantum criticality as a challenge for artificial neural networks

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The near-critical unitary dynamics of quantum Ising spin chains in transversal and longitudinal magnetic fields is studied using an artificial neural network representation of the wave function. A focus is set on strong spatial correlations which build up in the system following a quench into the vi...

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Bibliographic Details
Main Authors: Czischek, Stefanie (Author) , Gärttner, Martin (Author) , Gasenzer, Thomas (Author)
Format: Article (Journal)
Language:English
Published: 31 July 2018
In: Physical review
Year: 2018, Volume: 98, Issue: 2
ISSN:2469-9969
DOI:10.1103/PhysRevB.98.024311
Online Access:Verlag, Volltext: http://dx.doi.org/10.1103/PhysRevB.98.024311
Verlag, Volltext: https://link.aps.org/doi/10.1103/PhysRevB.98.024311
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Author Notes:Stefanie Czischek, Martin Gärttner, and Thomas Gasenzer
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Summary:The near-critical unitary dynamics of quantum Ising spin chains in transversal and longitudinal magnetic fields is studied using an artificial neural network representation of the wave function. A focus is set on strong spatial correlations which build up in the system following a quench into the vicinity of the quantum critical point. We compare correlations obtained by optimizing the parameters of the network states with analytical solutions in integrable cases and time-dependent density matrix renormalization group (tDMRG) simulations, as well as with predictions from a semiclassical discrete truncated Wigner analysis. While the semiclassical approach yields quantitatively correct results only at very short times and near zero transverse fields, the neural-network representation is applicable in a much wider regime. However, for quenches close to the quantum critical point the representation becomes inefficient. For nonintegrable models we show that in regimes where tDMRG is limited to short times due to extensive entanglement growth, also the neural-network parametrization converges only at short times.
Item Description:Gesehen am 17.01.2019
Physical Description:Online Resource
ISSN:2469-9969
DOI:10.1103/PhysRevB.98.024311

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