Bridging the reality gap in quantum devices with physics-aware machine learning
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2021
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| author | 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. Ares, N. |
| author_facet | 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. Ares, N. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2111_11285 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Bridging the reality gap in quantum devices with physics-aware machine learning 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. Ares, N. Mesoscale and Nanoscale Physics Machine Learning 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. |
| title | Bridging the reality gap in quantum devices with physics-aware machine learning |
| topic | Mesoscale and Nanoscale Physics Machine Learning |
| url | https://arxiv.org/abs/2111.11285 |