  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.
