  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.
