Bridging the reality gap in quantum devices with physics-aware machine learning
This work addresses the challenge of optimizing and scaling quantum devices, which is crucial for advancing quantum computing, though it appears incremental as it builds on existing methods.
The authors tackled the reality gap in solid-state quantum devices caused by material disorder by using a physics-aware machine learning approach to infer the disorder potential from electron transport data, validating it by accurately predicting gate voltages for a quantum dot device.
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.