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

TL;DR

This paper presents a machine learning and adaptive algorithm-based method for dynamically compensating stray electric fields in surface ion traps, improving ion fluorescence rates and aiding quantum computing scalability.

Contribution

It introduces a novel combination of gradient descent and deep learning techniques for real-time electric field compensation in ion traps.

Findings

78% increase in fluorescence rate with gradient descent

96% increase in fluorescence rate with machine learning

Effective compensation against UV laser-induced charging

Abstract

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