Machine-Learning Optimization and Characterization of a High-Optical-Depth Two-Color Nanofiber Trap
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Quantum Physics arXiv:2606.06798 (quant-ph) [Submitted on 5 Jun 2026] Title:Machine-Learning Optimization and Characterization of a High-Optical-Depth Two-Color Nanofiber Trap Authors:W. Crump, M. Sadeghi, M.D. Hoogerland View a PDF of the paper titled Machine-Learning Optimization and Characterization of a High-Optical-Depth Two-Color Nanofiber Trap, by W. Crump and 1 other authors View PDF HTML (experimental) 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: Subjects: Quantum Physics (quant-ph); Atomic Physics (physics.atom-ph) Cite as: arXiv:2606.06798 [quant-ph] (or arXiv:2606.06798v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2606.06798 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Maarten Hoogerland [view email] [v1] Fri, 5 Jun 2026 00:57:17 UTC (748 KB) Full-text links: Access Paper: View a PDF of the paper titled Machine-Learning Optimization and Characterization of a High-Optical-Depth Two-Color Nanofiber Trap, by W. Crump and 1 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-06 Change to browse by: physics physics.atom-ph References & Citations INSPIRE HEP NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)