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 Yb-171(+) ion driven by a laser tuned to -7.8 MHz of the S-2(1/2) <-> P-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 / Ghadimi, Moji; Zappacosta, Alexander; Scarabel, Jordan; Shimizu, Kenji; Streed, Erik W; Lobino, Mirko. - In: SCIENTIFIC REPORTS. - ISSN 2045-2322. - 12:1(2022), p. 7067. [10.1038/s41598-022-11142-7]

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

Lobino, Mirko
Ultimo
2022-01-01

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 Yb-171(+) ion driven by a laser tuned to -7.8 MHz of the S-2(1/2) <-> P-2(1/2) Doppler cooling transition at 369.5 nm.
2022
1
Ghadimi, Moji; Zappacosta, Alexander; Scarabel, Jordan; Shimizu, Kenji; Streed, Erik W; Lobino, Mirko
Dynamic compensation of stray electric fields in an ion trap using machine learning and adaptive algorithm / Ghadimi, Moji; Zappacosta, Alexander; Scarabel, Jordan; Shimizu, Kenji; Streed, Erik W; Lobino, Mirko. - In: SCIENTIFIC REPORTS. - ISSN 2045-2322. - 12:1(2022), p. 7067. [10.1038/s41598-022-11142-7]
File in questo prodotto:
File Dimensione Formato  
s41598-022-11142-7.pdf

accesso aperto

Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Creative commons
Dimensione 2.01 MB
Formato Adobe PDF
2.01 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/357644
Citazioni
  • ???jsp.display-item.citation.pmc??? 0
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 1
social impact