# Deep Learning-Assisted Classification of Site-Resolved Quantum Gas Microscope Images

## TL;DR

This paper introduces deep learning techniques to enhance the accuracy of classifying lattice sites in quantum gas microscope images, especially when imaging without cooling, by training neural networks on simulated data.

## Contribution

It develops two neural network architectures that outperform traditional threshold-based methods in noncooled quantum gas imaging scenarios.

## Key findings

- Up to twofold reduction in reconstruction error rate.
- Effective neural network models for noncooled quantum gas microscopy.
- Improved fidelity in site occupancy classification.

## Abstract

We present a novel method for the analysis of quantum gas microscope images, which uses deep learning to improve the fidelity with which lattice sites can be classified as occupied or unoccupied. Our method is especially suited to addressing the case of imaging without continuous cooling, in which the accuracy of existing threshold-based reconstruction methods is limited by atom motion and low photon counts. We devise two neural network architectures which are both able to improve upon the fidelity of threshold-based methods, following training on large data sets of simulated images. We evaluate these methods on simulations of a free-space erbium quantum gas microscope, and a noncooled ytterbium microscope in which atoms are pinned in a deep lattice during imaging. In some conditions we see reductions of up to a factor of two in the reconstruction error rate, representing a significant step forward in our efforts to implement high fidelity noncooled site-resolved imaging.