An unsupervised deep learning algorithm for single-site reconstruction in quantum gas microscopes
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. Co...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-07-01
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| Series: | Communications Physics |
| Online Access: | https://doi.org/10.1038/s42005-023-01287-w |