Journal article
Bridging the reality gap in quantum devices with physics-aware machine learning
- 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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 2.3MB, Terms of use)
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- Publisher copy:
- 10.1103/physrevx.14.011001
Authors
+ Engineering and Physical Sciences Research Council
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- Grant:
- EP/N014995/1
- Programme:
- National Quantum Technology Hub in Networked Quantum Information Technology
+ Royal Society
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- Funder identifier:
- https://ror.org/03wnrjx87
- Grant:
- URF\R1\191150
+ Engineering and Physical Sciences Research Council
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- Funder identifier:
- https://ror.org/0439y7842
- Grant:
- EP/R029229/1
- EP/M013243/1
+ European Research Council
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- Funder identifier:
- https://ror.org/0472cxd90
- Grant:
- 948932
- Publisher:
- American Physical Society
- Journal:
- Physical Review X More from this journal
- Volume:
- 14
- Issue:
- 1
- Article number:
- 11001
- Publication date:
- 2024-01-04
- Acceptance date:
- 2023-09-29
- DOI:
- EISSN:
-
2160-3308
- Language:
-
English
- Pubs id:
-
1609358
- Local pid:
-
pubs:1609358
- Deposit date:
-
2024-03-08
Terms of use
- Copyright date:
- 2024
- Rights statement:
- 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.
- Licence:
- CC Attribution (CC BY)
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