A vision transformer predicts topological visibility and LDOS-based Majorana indicators from simulated nanowire conductance traces with high held-out accuracy, but with no experimental validation.
Reconstructing the potential configuration in a high-mobility semiconductor heterostructure with scanning gate microscopy
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abstract
The weak disorder potential seen by the electrons of a two-dimensional electron gas in high-mobility semiconductor heterostructures leads to fluctuations in the physical properties and can be an issue for nanodevices. In this paper, we show that a scanning gate microscopy (SGM) image contains information about the disorder potential, and that a machine learning approach based on SGM data can be used to determine the disorder. We reconstruct the electric potential of a sample from its experimental SGM data and validate the result through an estimate of its accuracy.
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Vision transformer based Deep Learning of Topological indicators in Majorana Nanowires
A vision transformer predicts topological visibility and LDOS-based Majorana indicators from simulated nanowire conductance traces with high held-out accuracy, but with no experimental validation.