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

• URL: http://arxiv.org/abs/2111.11285v1
• Date: Mon, 22 Nov 2021 15:45:01 GMT
• Title: Bridging the reality gap in quantum devices with physics-aware machine learning
• Authors: D.L. Craig, H. Moon, F. Fedele, D.T. Lennon, B. Van Straaten, F. Vigneau, L.C. Camenzind, D.M. Zumb\"uhl, G.A.D. Briggs, M.A. Osborne, D. Sejdinovic, and N. Ares
• Abstract summary: Disorder induced by the unpredictable distribution of material defects is one of the major contributions to the reality gap. We bridge this gap 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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