An unsupervised deep learning algorithm for single-site reconstruction in quantum gas microscopes

TL;DR

This paper introduces an unsupervised deep learning algorithm using convolutional neural networks for high-fidelity site-resolved reconstruction in quantum gas microscopes, especially effective at short lattice spacings and limited resolution.

Contribution

The authors develop a novel unsupervised deep learning method that improves reconstruction fidelity and speed in quantum gas microscopy, outperforming traditional techniques at small lattice spacings.

Findings

Reconstruction fidelity exceeds 96% across all fillings.

Algorithm performs well with short-spaced optical lattices.

Enables faster and more accurate imaging in quantum gas experiments.

Abstract

In quantum gas microscopy experiments, reconstructing the site-resolved lattice occupation with high fidelity is essential for the accurate extraction of physical observables. For short interatomic separations and limited signal-to-noise ratio, this task becomes increasingly challenging. Common methods rapidly decline in performance as the lattice spacing is decreased below half the imaging resolution. Here, we present a novel algorithm based on deep convolutional neural networks to reconstruct the site-resolved lattice occupation with high fidelity. The algorithm can be directly trained in an unsupervised fashion with experimental fluorescence images and allows for a fast reconstruction of large images containing several thousand lattice sites. We benchmark its performance using a quantum gas microscope with cesium atoms that utilizes short-spaced optical lattices with lattice constant $383.5\,$nm and a typical Rayleigh resolution of $850\,$nm. We obtain promising reconstruction fidelities~$\gtrsim 96\%$ across all fillings based on a statistical analysis. We anticipate this algorithm to enable novel experiments with shorter lattice spacing, boost the readout fidelity and speed of lower-resolution imaging systems, and furthermore find application in related experiments such as trapped ions.