Bridging the Reality Gap in Quantum Devices with Physics-Aware Machine Learning
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 learnin...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Physical Society
2024-01-01
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| Series: | Physical Review X |
| Online Access: | http://doi.org/10.1103/PhysRevX.14.011001 |
| _version_ | 1797366071178559488 |
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| author | D. L. Craig H. Moon F. Fedele D. T. Lennon B. van Straaten F. Vigneau L. C. Camenzind D. M. Zumbühl G. A. D. Briggs M. A. Osborne D. Sejdinovic N. Ares |
| author_facet | D. L. Craig H. Moon F. Fedele D. T. Lennon B. van Straaten F. Vigneau L. C. Camenzind D. M. Zumbühl G. A. D. Briggs M. A. Osborne D. Sejdinovic N. Ares |
| author_sort | D. L. Craig |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T16:58:59Z |
| format | Article |
| id | doaj.art-9270b25aae9e4a1aa4ba928c45f94906 |
| institution | Directory Open Access Journal |
| issn | 2160-3308 |
| language | English |
| last_indexed | 2024-03-08T16:58:59Z |
| publishDate | 2024-01-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review X |
| spelling | doaj.art-9270b25aae9e4a1aa4ba928c45f949062024-01-04T16:34:05ZengAmerican Physical SocietyPhysical Review X2160-33082024-01-0114101100110.1103/PhysRevX.14.011001Bridging the Reality Gap in Quantum Devices with Physics-Aware Machine LearningD. L. CraigH. MoonF. FedeleD. T. LennonB. van StraatenF. VigneauL. C. CamenzindD. M. ZumbühlG. A. D. BriggsM. A. OsborneD. SejdinovicN. AresThe 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.http://doi.org/10.1103/PhysRevX.14.011001 |
| spellingShingle | D. L. Craig H. Moon F. Fedele D. T. Lennon B. van Straaten F. Vigneau L. C. Camenzind D. M. Zumbühl G. A. D. Briggs M. A. Osborne D. Sejdinovic N. Ares Bridging the Reality Gap in Quantum Devices with Physics-Aware Machine Learning Physical Review X |
| title | Bridging the Reality Gap in Quantum Devices with Physics-Aware Machine Learning |
| title_full | Bridging the Reality Gap in Quantum Devices with Physics-Aware Machine Learning |
| title_fullStr | Bridging the Reality Gap in Quantum Devices with Physics-Aware Machine Learning |
| title_full_unstemmed | Bridging the Reality Gap in Quantum Devices with Physics-Aware Machine Learning |
| title_short | Bridging the Reality Gap in Quantum Devices with Physics-Aware Machine Learning |
| title_sort | bridging the reality gap in quantum devices with physics aware machine learning |
| url | http://doi.org/10.1103/PhysRevX.14.011001 |
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