Journal
PHYSICAL REVIEW RESEARCH
Volume 3, Issue 3, Pages -Publisher
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevResearch.3.033267
Keywords
-
Categories
Funding
- MCF
- Flemish Government-Department EWI
- FWO-aspirant [FWO18/ASP/279]
- NSERC
- AI4D Program
Ask authors/readers for more resources
Optical lattice experiments with ultracold atoms provide a way to study contemporary problems in many-body physics experimentally, and machine learning has been successfully utilized to analyze experimental data of ultracold quantum gases. Generative deep learning has been shown to model experimental data distributions, enabling the prediction of snapshots under unknown conditions.
Optical lattice experiments with ultracold atoms allow for the experimental realization of contemporary problems in many-body physics. Yet, devising models that faithfully describe experimental observables is often difficult and problem dependent; there is currently no theoretical method which accounts for all experimental observations. Leveraging the large data volume and presence of strong correlations, machine learning provides a novel avenue for the study of such systems. It has recently been proven successful in analyzing properties of experimental data of ultracold quantum gases. Here we show that generative deep learning succeeds in the challenging task of modeling such an experimental data distribution. Our method is able to produce synthetic experimental snapshots of a doped two-dimensional Fermi-Hubbard model that are indistinguishable from previously reported experimental realizations. We demonstrate how our generative model interprets physical conditions such as temperature at the level of individual configurations. We use our approach to predict snapshots at conditions and scales which are currently experimentally inaccessible, mapping the large-scale behavior of optical lattices at unseen conditions.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available