Dynamic compensation of stray electric fields in an ion trap using machine learning and adaptive algorithm
Surface ion traps are among the most promising technologies for scaling up quantum computing machines, but their complicated multi-electrode geometry can make some tasks, including compensation for stray electric fields, challenging both at the level of modeling and of practical implementation. Here...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9054784/ https://www.ncbi.nlm.nih.gov/pubmed/35487938 http://dx.doi.org/10.1038/s41598-022-11142-7 |
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author | Ghadimi, Moji Zappacosta, Alexander Scarabel, Jordan Shimizu, Kenji Streed, Erik W. Lobino, Mirko |
author_facet | Ghadimi, Moji Zappacosta, Alexander Scarabel, Jordan Shimizu, Kenji Streed, Erik W. Lobino, Mirko |
author_sort | Ghadimi, Moji |
collection | PubMed |
description | Surface ion traps are among the most promising technologies for scaling up quantum computing machines, but their complicated multi-electrode geometry can make some tasks, including compensation for stray electric fields, challenging both at the level of modeling and of practical implementation. Here we demonstrate the compensation of stray electric fields using a gradient descent algorithm and a machine learning technique, which trained a deep learning network. We show automated dynamical compensation tested against induced electric charging from UV laser light hitting the chip trap surface. The results show improvement in compensation using gradient descent and the machine learner over manual compensation. This improvement is inferred from an increase of the fluorescence rate of 78% and 96% respectively, for a trapped [Formula: see text] Yb[Formula: see text] ion driven by a laser tuned to [Formula: see text] MHz of the [Formula: see text] S[Formula: see text] P[Formula: see text] Doppler cooling transition at 369.5 nm. |
format | Online Article Text |
id | pubmed-9054784 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-90547842022-05-01 Dynamic compensation of stray electric fields in an ion trap using machine learning and adaptive algorithm Ghadimi, Moji Zappacosta, Alexander Scarabel, Jordan Shimizu, Kenji Streed, Erik W. Lobino, Mirko Sci Rep Article Surface ion traps are among the most promising technologies for scaling up quantum computing machines, but their complicated multi-electrode geometry can make some tasks, including compensation for stray electric fields, challenging both at the level of modeling and of practical implementation. Here we demonstrate the compensation of stray electric fields using a gradient descent algorithm and a machine learning technique, which trained a deep learning network. We show automated dynamical compensation tested against induced electric charging from UV laser light hitting the chip trap surface. The results show improvement in compensation using gradient descent and the machine learner over manual compensation. This improvement is inferred from an increase of the fluorescence rate of 78% and 96% respectively, for a trapped [Formula: see text] Yb[Formula: see text] ion driven by a laser tuned to [Formula: see text] MHz of the [Formula: see text] S[Formula: see text] P[Formula: see text] Doppler cooling transition at 369.5 nm. Nature Publishing Group UK 2022-04-29 /pmc/articles/PMC9054784/ /pubmed/35487938 http://dx.doi.org/10.1038/s41598-022-11142-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Ghadimi, Moji Zappacosta, Alexander Scarabel, Jordan Shimizu, Kenji Streed, Erik W. Lobino, Mirko Dynamic compensation of stray electric fields in an ion trap using machine learning and adaptive algorithm |
title | Dynamic compensation of stray electric fields in an ion trap using machine learning and adaptive algorithm |
title_full | Dynamic compensation of stray electric fields in an ion trap using machine learning and adaptive algorithm |
title_fullStr | Dynamic compensation of stray electric fields in an ion trap using machine learning and adaptive algorithm |
title_full_unstemmed | Dynamic compensation of stray electric fields in an ion trap using machine learning and adaptive algorithm |
title_short | Dynamic compensation of stray electric fields in an ion trap using machine learning and adaptive algorithm |
title_sort | dynamic compensation of stray electric fields in an ion trap using machine learning and adaptive algorithm |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9054784/ https://www.ncbi.nlm.nih.gov/pubmed/35487938 http://dx.doi.org/10.1038/s41598-022-11142-7 |
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