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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    $$^{171}$$  171 Yb   $$^+$$  + ion driven by a laser tuned to    $$-7.8$$  - 7.8 MHz of the    $$^2$$  2 S   $$_{1/2}\leftrightarrow ^2$$  1 / 2 ↔ 2 P   $$_{1/2}$$  1 / 2 Doppler cooling transition at 369.5 nm.

Dynamic compensation of stray electric fields in an ion trap using machine learning and adaptive algorithm