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Quantum Machine Learning

Machine-Learning Optimization and Characterization of a High-Optical-Depth Two-Color Nanofiber Trap

arXiv
Authors: W. Crump, M. Sadeghi, M. D. Hoogerland

Year

2026

Paper ID

69025

Status

Preprint

Abstract Read

~2 min

Abstract Words

112

Citations

0

Abstract

Optical nanofibers provide a way of coupling quantum information in cold atoms across large distances, however, this coupling requires atoms to reside close to the nanofiber surface. Atoms can be trapped close to the surface using a two-color dipole trap. Here we present our experimental realization of a two-color dipole trap. We optimize the number of trapped atoms using a machine learning algorithm and measure the optical density via the transmission. We estimate the number of atoms in the trap to be approximately 1400 and the lifetime of the atoms in the trap to be 28 ms. Machine-learning optimization improved the on-resonance optical depth from 0.5 in the initial optimization stage to optical depths exceeding 15.

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  • Optical nanofibers provide a way of coupling quantum information in cold atoms across large distances, however, this coupling requires atoms to reside close to the nanofiber...

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2606.06798 [2] arXiv https://arxiv.org/abs/2606.06798 [3] Publisher https://arxiv.org/abs/2606.06798

Local Citation Graph (Related-Paper Links)

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External citation index: OpenAlex citation signal • updated 2026-06-13 11:08:47

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