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This research addresses a critical bottleneck in trapped-ion quantum computing by developing a predictive model for electric-field noise caused by material fluctuations. By isolating energy loss as the primary variable, the study provides a scalable framework to optimize trap geometries and reduce gate errors across arbitrary designs. This advancement directly supports the engineering of more stable and fault-tolerant quantum processors.
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Predicting electric-field noise in ion traps using fluctuation electrodynamics

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arXiv:2610.01693v1 Announce Type: new Abstract: Electric-field noise in microfabricated surface ion traps contributes to gate errors in trapped-ion quantum computers, but this noise has been challenging to predict. Here, we present a method to predict the electric-field noise arising from fluctuations in the bulk of dielectric and metallic materials. The method is valid for arbitrary trap geometries, and the only relevant material property is energy loss. We apply it to an ion trap with an electrically floating electrode and find the contribution from dielectrics to be dominant. Our model predicts the observed noise for this trap within a factor of four. In addition, we apply our model to typical surface trap designs and analyze how the electrode structures shield the noise.

Abstract:Electric-field noise in microfabricated surface ion traps contributes to gate errors in trapped-ion quantum computers, but this noise has been challenging to predict. Here, we present a method to predict the electric-field noise arising from fluctuations in the bulk of dielectric and metallic materials. The method is valid for arbitrary trap geometries, and the only relevant material property is energy loss. We apply it to an ion trap with an electrically floating electrode and find the contribution from dielectrics to be dominant. Our model predicts the observed noise for this trap within a factor of four. In addition, we apply our model to typical surface trap designs and analyze how the electrode structures shield the noise.

arXiv:2610.01693 [quant-ph]

(or arXiv:2610.01693v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2610.01693 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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