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arXiv:2606.06798

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

W. Crump, M. Sadeghi, M. D. Hoogerland

quant-phphysics.atom-phSubmitted Jun 5, 2026• Announced Jun 8, 2026• 0 citations
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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.

Comments
6 pages, 4 figures

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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 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.
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.
We use a machine learning optimizer to increase the number of rubidium-87 atoms trapped in an optical nanofiber-based two-color evanescent dipole trap array. Collisional blockade limits the average number of atoms per trap to about 0.5, and a typical uncompensated rubidium trap has even lower occupancy due to challenges in simultaneously cooling atoms and loading them in the traps. Here, we report on the implementation of an in-loop stochastic artificial neural network machine learner to optimize this loading by optimizing the absorption of a near-resonant, nanofiber-guided, probe beam. By giving the neural network control of the laser cooling process, we observe an increase in peak optical depth of 66% from 3.2 ± 0.2 to 5.3 ± 0.3. We use a microscopic model of the atomic absorption to infer an increase in the number of dipole-trapped atoms from 300 ± 60 to 450 ± 90 and a small decrease in their average temperature from 150 to 140 μK. The machine learner is able to quickly and effectively explore the large parameter space of the laser cooling control process so as to find optimal parameters for loading the dipole traps. The increased number of atoms should facilitate studies of collective atom–light interactions mediated via the evanescent field.
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We experimentally study the ground state coherence properties of cesium atoms in a nanofiber-based two-color dipole trap, localized ∼ 200 nm away from the fiber surface. Using microwave radiation to coherently drive the clock transition, we record Ramsey fringes as well as spin echo signals and infer a reversible dephasing time of T(2)(*) = 0.6 ms and an irreversible dephasing time of T(2)(') = 3.7 ms. By modeling the signals, we find that, for our experimental parameters, T(2)(*) and T(2)(') are limited by the finite initial temperature of the atomic ensemble and the heating rate, respectively. Our results represent a fundamental step towards establishing nanofiber-based traps for cold atoms as a building block in an optical fiber quantum network.