Abstract
The discrepancies between reality and simulation impede the optimization 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 enables 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.
- Received 19 December 2021
- Revised 16 May 2023
- Accepted 29 September 2023
DOI:https://doi.org/10.1103/PhysRevX.14.011001
Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.
Published by the American Physical Society
Physics Subject Headings (PhySH)
Popular Summary
One of the driving forces of human discovery is the difference between predictions and observed results; this is the reality gap. When playing “crazy golf,” for example, the ball may enter a tunnel and exit with a speed or direction that does not match our predictions. But with a few more shots, a crazy-golf simulator, and some machine learning, we might get better at predicting the ball’s movements and narrow the reality gap. Solid-state quantum devices throw up similar barriers to understanding: Nominally identical devices will often display different characteristics. Here, we take a machine learning approach to narrow the reality gap in such devices.
Specifically, our work considers the control of current through a nanoscale device by changing gate voltages: Devices that seem identical can display different current behaviors at the same voltage settings due to material imperfections. This variability opens a gap between simulation predictions and reality. Our physics-aware machine learning approach to bridge this reality gap reveals hidden features of the material imperfections affecting our device. We use simple measurements of current at different voltage settings to inform the simulator.
For different types of current measurements, our approach reduces the gap between simulations and experimental measurements. Accurate predictions of nanoscale device properties further our understanding of device variability, which is key to developing more complex quantum systems.
