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

Physics

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

A. Impertro, J. F. Wienand, et al.

A deep convolutional neural network algorithm has been developed by researchers Alexander Impertro, Julian F. Wienand, Sophie Häfele, Hendrik von Raven, Scott Hubele, Till Klostermann, Cesar R. Cabrera, Immanuel Bloch, and Monika Aidelsburger for accurately reconstructing site-resolved lattice occupation in quantum gas microscopy. Achieving an impressive reconstruction fidelity of 96%, this algorithm holds significant potential for enhancing imaging systems in future experiments.

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