The discrepancies between reality and simulation impede the optimisation and scalability of solid-state quantum devices. Disorder induced by the unpredictable distribution of material defects is one of the major contributions to the reality gap. We bridge this gap using physics-aware machine learning, in particular, using an approach combining a physical model, deep learning, Gaussian random field, and Bayesian inference. This approach has enabled us to infer the disorder potential of a nanoscale electronic device from electron transport data. This inference is validated by verifying the algorithm's predictions about the gate voltage values required for a laterally-defined quantum dot device in AlGaAs/GaAs to produce current features corresponding to a double quantum dot regime.
@article{arxiv.2111.11285,
title = {Bridging the reality gap in quantum devices with physics-aware machine learning},
author = {D. L. Craig and H. Moon and F. Fedele and D. T. Lennon and B. Van Straaten and F. Vigneau and L. C. Camenzind and D. M. Zumbühl and G. A. D. Briggs and M. A. Osborne and D. Sejdinovic and N. Ares},
journal= {arXiv preprint arXiv:2111.11285},
year = {2024}
}