Repository logo
 

Optimized observable readout from single-shot images of ultracold atoms via machine learning

Published version
Peer-reviewed

Change log

Authors

Lode, Axel UJ 
Lin, Rui 
Buettner, Miriam 
Papariello, Luca 
Leveque, Camille 

Abstract

Single-shot images are the standard readout of experiments with ultracold atoms, the imperfect reflection of their many-body physics. The efficient extraction of observables from single-shot images is thus crucial. Here we demonstrate how artificial neural networks can optimize this extraction. In contrast to standard averaging approaches, machine learning allows both one- and two-particle densities to be accurately obtained from a drastically reduced number of single-shot images. Quantum fluctuations and correlations are directly harnessed to obtain physical observables for bosons in a tilted double-well potential at an extreme accuracy. Strikingly, machine learning also enables a reliable extraction of momentum-space observables from real-space single-shot images and vice versa. With this technique, the reconfiguration of the experimental setup between in situ and time-of-flight imaging is required only once to obtain training data, thus potentially granting an outstanding reduction in resources.

Description

Keywords

5108 Quantum Physics, 5102 Atomic, Molecular and Optical Physics, 51 Physical Sciences, Networking and Information Technology R&D (NITRD), Machine Learning and Artificial Intelligence

Journal Title

PHYSICAL REVIEW A

Conference Name

Journal ISSN

2469-9926
2469-9934

Volume Title

104

Publisher

American Physical Society (APS)
Sponsorship
Engineering and Physical Sciences Research Council (EP/P009565/1)