Deep learning-assisted classification of site-resolved quantum gas microscope images

@article{1904.08074,
doi = {10.1088/1361-6501/ab44d8},
url = {https://doi.org/10.1088%2F1361-6501%2Fab44d8},
year = {2019},
month = {nov},
abstract = {Dipolar interactions are ubiquitous in nature and rule the behavior of a broad range of systems spanning from energy transfer in biological systems to quantum magnetism. Here, we study magnetization-conserving dipolar induced spin-exchange dynamics in dense arrays of fermionic erbium atoms confined in a deep three-dimensional lattice. Harnessing the special atomic properties of erbium, we demonstrate control over the spin dynamics by tuning the dipole orientation and changing the initial spin state within the large 20 spin hyperfine manifold. Furthermore, we demonstrate the capability to quickly turn on and off the dipolar exchange dynamics via optical control. The experimental observations are in excellent quantitative agreement with numerical calculations based on discrete phase-space methods, which capture entanglement and beyond-mean field effects. Our experiment sets the stage for future explorations of rich magnetic behaviors in long-range interacting dipoles, including exotic phases of matter and applications for quantum information processing.},
publisher = {{IOP} Publishing},
volume = {31},
number = {2},
pages = {025201},
author = {L. R. B. Picard and M. J. Mark and F. Ferlaino and R. Van Bijnen},
title = {Deep learning-assisted classification of site-resolved quantum gas microscope images},
journal = {Measurement Science and Technology},
arXiv = {https://arxiv.org/abs/1904.08074}
}