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Efficiently measuring a quantum device using machine learning

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Efficiently measuring a quantum device using machine learning. / Lennon, D. T.; Moon, H.; Camenzind, L. C. et al.

In: npj Quantum Information , Vol. 5, 79, 26.09.2019.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Lennon, DT, Moon, H, Camenzind, LC, Yu, L, Zumbuhl, DM, Briggs, GAD, Osborne, MA, Laird, E & Ares, N 2019, 'Efficiently measuring a quantum device using machine learning', npj Quantum Information , vol. 5, 79. https://doi.org/10.1038/s41534-019-0193-4

APA

Lennon, D. T., Moon, H., Camenzind, L. C., Yu, L., Zumbuhl, D. M., Briggs, G. A. D., Osborne, M. A., Laird, E., & Ares, N. (2019). Efficiently measuring a quantum device using machine learning. npj Quantum Information , 5, [79]. https://doi.org/10.1038/s41534-019-0193-4

Vancouver

Lennon DT, Moon H, Camenzind LC, Yu L, Zumbuhl DM, Briggs GAD et al. Efficiently measuring a quantum device using machine learning. npj Quantum Information . 2019 Sep 26;5:79. doi: 10.1038/s41534-019-0193-4

Author

Lennon, D. T. ; Moon, H. ; Camenzind, L. C. et al. / Efficiently measuring a quantum device using machine learning. In: npj Quantum Information . 2019 ; Vol. 5.

Bibtex

@article{fff91974b1ee44a68d83bd65d96490f3,
title = "Efficiently measuring a quantum device using machine learning",
abstract = "Scalable quantum technologies such as quantum computers will require very large numbers of quantum devices to be characterised and tuned. As the number of devices on chip increases, this task becomes ever more time-consuming, and will be intractable on a large scale without efficient automation. We present measurements on a quantum dot device performed by a machine learning algorithm in real time. The algorithm selects the most informative measurements to perform next by combining information theory with a probabilistic deep-generative model that can generate full-resolution reconstructions from scattered partial measurements. We demonstrate, for two different current map configurations that the algorithm outperforms standard grid scan techniques, reducing the number of measurements required by up to 4 times and the measurement time by 3.7 times. Our contribution goes beyond the use of machine learning for data search and analysis, and instead demonstrates the use of algorithms to automate measurements. This works lays the foundation for learning-based automated measurement of quantum devices.",
author = "Lennon, {D. T.} and H. Moon and Camenzind, {L. C.} and Liuqi Yu and Zumbuhl, {D. M.} and Briggs, {G. A. D.} and Osborne, {M. A.} and Edward Laird and N. Ares",
year = "2019",
month = sep,
day = "26",
doi = "10.1038/s41534-019-0193-4",
language = "English",
volume = "5",
journal = "npj Quantum Information ",

}

RIS

TY - JOUR

T1 - Efficiently measuring a quantum device using machine learning

AU - Lennon, D. T.

AU - Moon, H.

AU - Camenzind, L. C.

AU - Yu, Liuqi

AU - Zumbuhl, D. M.

AU - Briggs, G. A. D.

AU - Osborne, M. A.

AU - Laird, Edward

AU - Ares, N.

PY - 2019/9/26

Y1 - 2019/9/26

N2 - Scalable quantum technologies such as quantum computers will require very large numbers of quantum devices to be characterised and tuned. As the number of devices on chip increases, this task becomes ever more time-consuming, and will be intractable on a large scale without efficient automation. We present measurements on a quantum dot device performed by a machine learning algorithm in real time. The algorithm selects the most informative measurements to perform next by combining information theory with a probabilistic deep-generative model that can generate full-resolution reconstructions from scattered partial measurements. We demonstrate, for two different current map configurations that the algorithm outperforms standard grid scan techniques, reducing the number of measurements required by up to 4 times and the measurement time by 3.7 times. Our contribution goes beyond the use of machine learning for data search and analysis, and instead demonstrates the use of algorithms to automate measurements. This works lays the foundation for learning-based automated measurement of quantum devices.

AB - Scalable quantum technologies such as quantum computers will require very large numbers of quantum devices to be characterised and tuned. As the number of devices on chip increases, this task becomes ever more time-consuming, and will be intractable on a large scale without efficient automation. We present measurements on a quantum dot device performed by a machine learning algorithm in real time. The algorithm selects the most informative measurements to perform next by combining information theory with a probabilistic deep-generative model that can generate full-resolution reconstructions from scattered partial measurements. We demonstrate, for two different current map configurations that the algorithm outperforms standard grid scan techniques, reducing the number of measurements required by up to 4 times and the measurement time by 3.7 times. Our contribution goes beyond the use of machine learning for data search and analysis, and instead demonstrates the use of algorithms to automate measurements. This works lays the foundation for learning-based automated measurement of quantum devices.

U2 - 10.1038/s41534-019-0193-4

DO - 10.1038/s41534-019-0193-4

M3 - Journal article

VL - 5

JO - npj Quantum Information

JF - npj Quantum Information

M1 - 79

ER -