Minimization of ion micromotion with artificial neural network
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2021
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866909227247730688 |
|---|---|
| author | Liu, Yang Lao, Qi-feng Lu, Peng-fei Rao, Xin-xin Wu, Hao Liu, Teng Wang, Kun-xu Wang, Zhao Li, Ming-shen Zhu, Feng Luo, Le |
| author_facet | Liu, Yang Lao, Qi-feng Lu, Peng-fei Rao, Xin-xin Wu, Hao Liu, Teng Wang, Kun-xu Wang, Zhao Li, Ming-shen Zhu, Feng Luo, Le |
| contents | Minimizing the micromotion of the single trapped ion in a linear Paul trap is a tedious and time-consuming work,but is of great importance in cooling the ion into the motional ground state as well as maintaining long coherence time, which is crucial for quantum information processing and quantum computation. Here we demonstrate that systematic machine learning based on artificial neural networks can quickly and efficiently find optimal voltage settings for the electrodes using rf-photon correlation technique, consequently minimizing the micromotion to the minimum. Our approach achieves a very high level of control for the ion micromotion, and can be extended to other configurations of Paul trap. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2103_02231 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Minimization of ion micromotion with artificial neural network Liu, Yang Lao, Qi-feng Lu, Peng-fei Rao, Xin-xin Wu, Hao Liu, Teng Wang, Kun-xu Wang, Zhao Li, Ming-shen Zhu, Feng Luo, Le Atomic Physics Quantum Physics Minimizing the micromotion of the single trapped ion in a linear Paul trap is a tedious and time-consuming work,but is of great importance in cooling the ion into the motional ground state as well as maintaining long coherence time, which is crucial for quantum information processing and quantum computation. Here we demonstrate that systematic machine learning based on artificial neural networks can quickly and efficiently find optimal voltage settings for the electrodes using rf-photon correlation technique, consequently minimizing the micromotion to the minimum. Our approach achieves a very high level of control for the ion micromotion, and can be extended to other configurations of Paul trap. |
| title | Minimization of ion micromotion with artificial neural network |
| topic | Atomic Physics Quantum Physics |
| url | https://arxiv.org/abs/2103.02231 |