Accepted author manuscript, 16 MB, PDF document
Available under license: CC BY: Creative Commons Attribution 4.0 International License
Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
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 -