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Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning

Fuente: arXiv

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Detalles Bibliográficos
Autores principales:	Severin, B., Lennon, D. T., Camenzind, L. C., Vigneau, F., Fedele, F., Jirovec, D., Ballabio, A., Chrastina, D., Isella, G., de Kruijf, M., Carballido, M. J., Svab, S., Kuhlmann, A. V., Braakman, F. R., Geyer, S., Froning, F. N. M., Moon, H., Osborne, M. A., Sejdinovic, D., Katsaros, G., Zumbühl, D. M., Briggs, G. A. D., Ares, N.
Formato:	Preprint
Publicado:	2021
Materias:	Mesoscale and Nanoscale Physics Machine Learning Quantum Physics
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

_version_	1866929500960325632
author	Severin, B. Lennon, D. T. Camenzind, L. C. Vigneau, F. Fedele, F. Jirovec, D. Ballabio, A. Chrastina, D. Isella, G. de Kruijf, M. Carballido, M. J. Svab, S. Kuhlmann, A. V. Braakman, F. R. Geyer, S. Froning, F. N. M. Moon, H. Osborne, M. A. Sejdinovic, D. Katsaros, G. Zumbühl, D. M. Briggs, G. A. D. Ares, N.
author_facet	Severin, B. Lennon, D. T. Camenzind, L. C. Vigneau, F. Fedele, F. Jirovec, D. Ballabio, A. Chrastina, D. Isella, G. de Kruijf, M. Carballido, M. J. Svab, S. Kuhlmann, A. V. Braakman, F. R. Geyer, S. Froning, F. N. M. Moon, H. Osborne, M. A. Sejdinovic, D. Katsaros, G. Zumbühl, D. M. Briggs, G. A. D. Ares, N.
contents	The potential of Si and SiGe-based devices for the scaling of quantum circuits is tainted by device variability. Each device needs to be tuned to operation conditions. We give a key step towards tackling this variability with an algorithm that, without modification, is capable of tuning a 4-gate Si FinFET, a 5-gate GeSi nanowire and a 7-gate SiGe heterostructure double quantum dot device from scratch. We achieve tuning times of 30, 10, and 92 minutes, respectively. The algorithm also provides insight into the parameter space landscape for each of these devices. These results show that overarching solutions for the tuning of quantum devices are enabled by machine learning.
format	Preprint
id	arxiv_https___arxiv_org_abs_2107_12975
institution	arXiv
publishDate	2021
record_format	arxiv
spellingShingle	Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning Severin, B. Lennon, D. T. Camenzind, L. C. Vigneau, F. Fedele, F. Jirovec, D. Ballabio, A. Chrastina, D. Isella, G. de Kruijf, M. Carballido, M. J. Svab, S. Kuhlmann, A. V. Braakman, F. R. Geyer, S. Froning, F. N. M. Moon, H. Osborne, M. A. Sejdinovic, D. Katsaros, G. Zumbühl, D. M. Briggs, G. A. D. Ares, N. Mesoscale and Nanoscale Physics Machine Learning Quantum Physics The potential of Si and SiGe-based devices for the scaling of quantum circuits is tainted by device variability. Each device needs to be tuned to operation conditions. We give a key step towards tackling this variability with an algorithm that, without modification, is capable of tuning a 4-gate Si FinFET, a 5-gate GeSi nanowire and a 7-gate SiGe heterostructure double quantum dot device from scratch. We achieve tuning times of 30, 10, and 92 minutes, respectively. The algorithm also provides insight into the parameter space landscape for each of these devices. These results show that overarching solutions for the tuning of quantum devices are enabled by machine learning.
title	Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning
topic	Mesoscale and Nanoscale Physics Machine Learning Quantum Physics
url	https://arxiv.org/abs/2107.12975