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Quantum 2.0 Conference and Exhibition

Machine learner optimization of atom loading in optical nanofiber evanescent dipole traps

Ratnesh Kumar Gupta, Jesse L. Everett, Aaron D. Tranter, René Henke, Vandna Gokhroo, Ping Koy Lam, Síle Nic Chormaic

Published Jan 1, 2022• 0 citations
Publisher

Abstract

We use an online machine learning algorithm to optimize cooling and loading of rubidium-87 atoms into an evanescent dipole trap array along an optical nanofiber, increasing the number of trapped atoms by 50%.
DOI
10.1364/quantum.2022.qw2a.45

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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.
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 present experimental techniques and recently demonstrated results related to the optimization and characterization of our recently demonstrated nanofiber-based atom trap. The atoms are confined in an optical lattice that is created using a two-color evanescent field surrounding the optical nanofiber. For this purpose, the polarization state of the trapping light fields has to be properly adjusted. We demonstrate that this can be accomplished by analyzing the light scattered by the nanofiber. Furthermore, we show that loading the nanofiber trap from a magneto-optical trap leads to sub-Doppler temperatures of the trapped atomic ensemble and yields a sub-Poissonian distribution of the number of trapped atoms per trapping site.