1. Bridging the reality gap in quantum devices with physics-aware machine learning
- Author
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Craig, D. L., Moon, H., Fedele, F., Lennon, D. T., Van Straaten, B., Vigneau, F., Camenzind, L. C., Zumbühl, D. M., Briggs, G. A. D., Osborne, M. A., Sejdinovic, D., and Ares, N.
- Subjects
Condensed Matter - Mesoscale and Nanoscale Physics ,Computer Science - 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.
- Published
- 2021
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