Bridging the reality gap in quantum devices with physics-aware machine learning

Abstract

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.

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• Automated in situ optimization and disorder mitigation in a quantum device
• Machine-learning-enabled characterization of individual ring resonators in integrated photonic lattices
• Learning the dynamics of Markovian open quantum systems from experimental data