Berlin 2024 – wissenschaftliches Programm
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TT: Fachverband Tiefe Temperaturen
TT 14: Artificial Intelligence in Condensed Matter Physics II (joint session TT/DY)
TT 14.4: Vortrag
Montag, 18. März 2024, 15:45–16:00, H 3025
Neural quantum states for a two-leg Bose-Hubbard ladder under a magnetic field — •Kadir Çeven1,2, Mehmet Özgür Oktel2, and Ahmet Keleş3 — 1Institut für Theoretische Physik, Georg-August-Universität Göttingen, Germany — 2Department of Physics, Bilkent University, Ankara, Turkey — 3Department of Physics, Middle East Technical University, Ankara, Turkey
This work explores novel quantum phases in a two-leg Bose-Hubbard ladder, achieved using neural quantum states. The remarkable potential of quantum gas systems for analog quantum simulation of strongly correlated quantum matter is well-known; however, it is equally evident that new theoretical bases are urgently required to comprehend their intricacies fully. While simple one-dimensional models have served as valuable test cases, ladder models naturally emerge as the next step, enabling studying higher dimensional effects, including gauge fields. Using [Çeven et al., PRA 106, 063320 (2022)], this work investigates the application of neural quantum states to a two-leg Bose-Hubbard ladder in the presence of strong synthetic magnetic fields. This paper showcased the reliability of variational neural networks, such as restricted Boltzmann machines and feedforward neural networks, in accurately predicting the phase diagram exhibiting superfluid-Mott insulator phase transition under strong interaction. Moreover, the neural networks successfully identified other intriguing many-body phases in the weakly interacting regime. These exciting findings firmly designate a two-leg Bose-Hubbard ladder with magnetic flux as an ideal testbed for advancing the field of neural quantum states.
Keywords: Bose-Hubbard model; two-leg ladder flux system; synthetic magnetic field; neural-network quantum states; machine learning