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Machine learning enables completely automatic tuning of a quantum device faster than human experts

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Machine learning enables completely automatic tuning of a quantum device faster than human experts. / Moon, H.; Lennon, D. T.; Kirkpatrick, J. et al.
In: Nature Communications, Vol. 11, 4161, 19.08.2020.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Moon, H, Lennon, DT, Kirkpatrick, J, van Esbroeck, NM, Camenzind, LC, Yu, L, Vigneau, F, Zumbühl, D, Briggs, GAD, Osborne, MA, Sejdinovic, D, Laird, E & Ares, N 2020, 'Machine learning enables completely automatic tuning of a quantum device faster than human experts', Nature Communications, vol. 11, 4161. https://doi.org/10.1038/s41467-020-17835-9

APA

Moon, H., Lennon, D. T., Kirkpatrick, J., van Esbroeck, N. M., Camenzind, L. C., Yu, L., Vigneau, F., Zumbühl, D., Briggs, G. A. D., Osborne, M. A., Sejdinovic, D., Laird, E., & Ares, N. (2020). Machine learning enables completely automatic tuning of a quantum device faster than human experts. Nature Communications, 11, Article 4161. https://doi.org/10.1038/s41467-020-17835-9

Vancouver

Moon H, Lennon DT, Kirkpatrick J, van Esbroeck NM, Camenzind LC, Yu L et al. Machine learning enables completely automatic tuning of a quantum device faster than human experts. Nature Communications. 2020 Aug 19;11:4161. doi: 10.1038/s41467-020-17835-9

Author

Moon, H. ; Lennon, D. T. ; Kirkpatrick, J. et al. / Machine learning enables completely automatic tuning of a quantum device faster than human experts. In: Nature Communications. 2020 ; Vol. 11.

Bibtex

@article{997f464e186f43b28ef58a98042f9923,
title = "Machine learning enables completely automatic tuning of a quantum device faster than human experts",
abstract = "Variability is a problem for the scalability of semiconductor quantum devices. The parameter space is large, and the operating range is small. Our statistical tuning algorithm searches for specific electron transport features in gate-defined quantum dot devices with a gate voltage space of up to eight dimensions. Starting from the full range of each gate voltage, our machine learning algorithmcan tune each device to optimal performance in a median time of under 70 minutes. This performance surpassed our best human benchmark (although both human and machine performance can be improved). The algorithm is approximately 180 times faster than an automated random search of the parameter space, and is suitable for different material systems and device architectures. Our results yield a quantitative measurement of device variability, from one device to another and after thermal cycling. Our machine learning algorithm can be extended to higher dimensions and other technologies.",
author = "H. Moon and Lennon, {D. T.} and J. Kirkpatrick and {van Esbroeck}, {N. M.} and Camenzind, {L. C.} and Liuqi Yu and F. Vigneau and Dominik Zumb{\"u}hl and Briggs, {G. Andrew D.} and Osborne, {M. A.} and D. Sejdinovic and Edward Laird and N. Ares",
year = "2020",
month = aug,
day = "19",
doi = "10.1038/s41467-020-17835-9",
language = "English",
volume = "11",
journal = "Nature Communications",
issn = "2041-1723",
publisher = "Nature Publishing Group",

}

RIS

TY - JOUR

T1 - Machine learning enables completely automatic tuning of a quantum device faster than human experts

AU - Moon, H.

AU - Lennon, D. T.

AU - Kirkpatrick, J.

AU - van Esbroeck, N. M.

AU - Camenzind, L. C.

AU - Yu, Liuqi

AU - Vigneau, F.

AU - Zumbühl, Dominik

AU - Briggs, G. Andrew D.

AU - Osborne, M. A.

AU - Sejdinovic, D.

AU - Laird, Edward

AU - Ares, N.

PY - 2020/8/19

Y1 - 2020/8/19

N2 - Variability is a problem for the scalability of semiconductor quantum devices. The parameter space is large, and the operating range is small. Our statistical tuning algorithm searches for specific electron transport features in gate-defined quantum dot devices with a gate voltage space of up to eight dimensions. Starting from the full range of each gate voltage, our machine learning algorithmcan tune each device to optimal performance in a median time of under 70 minutes. This performance surpassed our best human benchmark (although both human and machine performance can be improved). The algorithm is approximately 180 times faster than an automated random search of the parameter space, and is suitable for different material systems and device architectures. Our results yield a quantitative measurement of device variability, from one device to another and after thermal cycling. Our machine learning algorithm can be extended to higher dimensions and other technologies.

AB - Variability is a problem for the scalability of semiconductor quantum devices. The parameter space is large, and the operating range is small. Our statistical tuning algorithm searches for specific electron transport features in gate-defined quantum dot devices with a gate voltage space of up to eight dimensions. Starting from the full range of each gate voltage, our machine learning algorithmcan tune each device to optimal performance in a median time of under 70 minutes. This performance surpassed our best human benchmark (although both human and machine performance can be improved). The algorithm is approximately 180 times faster than an automated random search of the parameter space, and is suitable for different material systems and device architectures. Our results yield a quantitative measurement of device variability, from one device to another and after thermal cycling. Our machine learning algorithm can be extended to higher dimensions and other technologies.

U2 - 10.1038/s41467-020-17835-9

DO - 10.1038/s41467-020-17835-9

M3 - Journal article

VL - 11

JO - Nature Communications

JF - Nature Communications

SN - 2041-1723

M1 - 4161

ER -