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

11 Feb 2021 Moji Ghadimi Alexander Zappacosta Jordan Scarabel Kenji Shimizu Erik W Streed Mirko Lobino

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... (read more)

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  • QUANTUM PHYSICS