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Cs Quantum Gas Microscope

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Recent publications

An unsupervised deep learning algorithm for single-site reconstruction

We have recently implemented an unsupervised deep learning algorithm that allows us to reconstruct the site occupation in our short-spacing lattice with high fidelity!

large-hom-MIIn 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 383.5nm and a typical Rayleigh resolution of 850nm. We obtain promising reconstruction fidelities ≳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.

Original publication:
An unsupervised deep learning algorithm for single-site reconstruction in quantum gas microscopes
Alexander Impertro, Julian F. Wienand, Sophie Häfele, Hendrik von Raven, Scott Hubele, Till Klostermann, Cesar R. Cabrera, Immanuel Bloch, Monika Aidelsburger, arXiv:2212.11974

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