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

• URL: http://arxiv.org/abs/2212.11974v1
• Date: Thu, 22 Dec 2022 18:57:27 GMT
• Title: An unsupervised deep learning algorithm for single-site reconstruction in quantum gas microscopes
• Authors: Alexander Impertro, Julian F. Wienand, Sophie H\"afele, Hendrik von Raven, Scott Hubele, Till Klostermann, Cesar R. Cabrera, Immanuel Bloch, Monika Aidelsburger
• Abstract summary: In quantum gas microscopy experiments, reconstructing the site-resolved lattice occupation with high fidelity is essential for the accurate extraction of physical observables. Here, we present a novel algorithm based on deep convolutional neural networks to reconstruct the site-resolved lattice occupation with high fidelity.
• Score: 47.187609203210705
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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