Minimization of ion micromotion with artificial neural network

• URL: http://arxiv.org/abs/2103.02231v3
• Date: Fri, 5 Mar 2021 08:22:10 GMT
• Title: Minimization of ion micromotion with artificial neural network
• Authors: Yang Liu, Qi-feng Lao, Peng-fei Lu, Xin-xin Rao, Hao Wu, Teng Liu, Kun-xu Wang, Zhao Wang, Ming-shen Li, Feng Zhu, and Le Luo
• Abstract summary: Minimizing the micromotion of the single trapped ion in a linear Paul trap is a tedious and time-consuming work. Here we demonstrate that systematic machine learning based on artificial neural networks can quickly and efficiently find optimal voltage settings for the electrodes. Our approach achieves a very high level of control for the ion micromotion, and can be extended to other configurations of Paul trap.
• Score: 8.729142722225598
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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