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

• Verfasst von: Czischek, Stefanie [VerfasserIn]
• Verfasst von: Gärttner, Martin [VerfasserIn]
• Verfasst von: Gasenzer, Thomas [VerfasserIn]
• Titel: Quenches near Ising quantum criticality as a challenge for artificial neural networks
• Verf.angabe: Stefanie Czischek, Martin Gärttner, and Thomas Gasenzer
• E-Jahr: 2018
• Jahr: 31 July 2018
• Umfang: 10 S.
• Fussnoten: Gesehen am 17.01.2019
• Titel Quelle: Enthalten in: Physical review
• Ort Quelle: Woodbury, NY : Inst., 2016
• Jahr Quelle: 2018
• Band/Heft Quelle: 98(2018,02) Artikel-Nummer 024311, 10 Seiten
• ISSN Quelle: 2469-9969
• DOI: doi:10.1103/PhysRevB.98.024311
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1586323237

Abstract

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.