Determinating full parameters of U-matrix for reconfigurable Boson sampling circuits using machine learning
A method of tuning a reconfigurable silicon photonic circuit into an arbitrary unitary operator with machine learning was proposed to bypass the traditional phase-voltage calibration process and make the prediction of applied heating voltage directly.
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sg-ntu-dr.10356-1388752020-05-13T08:20:53Z Determinating full parameters of U-matrix for reconfigurable Boson sampling circuits using machine learning Wan, L. X. Zhang, Haochi Huang, Jian Guo Zhang, Gong Kwek, L. C. Fitzsimons, J. Chong, Yi Dong Gong, J. B. Szameit, A. Zhou, X. Q. Yung, M. H. Jin, X. M. Su, X. L. Ser, Wee Gao, W. B. Liu, Ai Qun School of Electrical and Electronic Engineering School of Physical and Mathematical Sciences 2018 Conference on Lasers and Electro-Optics (CLEO): Applications and Technology Engineering::Electrical and electronic engineering::Optics, optoelectronics, photonics Machine Learning Heating Systems A method of tuning a reconfigurable silicon photonic circuit into an arbitrary unitary operator with machine learning was proposed to bypass the traditional phase-voltage calibration process and make the prediction of applied heating voltage directly. Published version 2020-05-13T08:20:52Z 2020-05-13T08:20:52Z 2018 Conference Paper Wan, L. X., Zhang, H., Huang, J. G., Zhang, G., Kwek, L. C., Fitzsimons, J., . . . Liu, A. Q. (2018). Determinating full parameters of U-matrix for reconfigurable Boson sampling circuits using machine learning. 2018 Conference on Lasers and Electro-Optics (CLEO): Applications and Technology, FM1G.2-. doi:10.1364/CLEO_AT.2018.JTh2A.18 9781943580422 https://hdl.handle.net/10356/138875 10.1364/CLEO_AT.2018.JTh2A.18 2-s2.0-85049133178 en NRF-CRP13-2014-01 © The Author(s). All rights reserved. This paper was published by Optical Society of America (OSA) in 2018 Conference on Lasers and Electro-Optics (CLEO): Applications and Technology and is made available with permission of the author(s). application/pdf |
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Engineering::Electrical and electronic engineering::Optics, optoelectronics, photonics Machine Learning Heating Systems |
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Engineering::Electrical and electronic engineering::Optics, optoelectronics, photonics Machine Learning Heating Systems Wan, L. X. Zhang, Haochi Huang, Jian Guo Zhang, Gong Kwek, L. C. Fitzsimons, J. Chong, Yi Dong Gong, J. B. Szameit, A. Zhou, X. Q. Yung, M. H. Jin, X. M. Su, X. L. Ser, Wee Gao, W. B. Liu, Ai Qun Determinating full parameters of U-matrix for reconfigurable Boson sampling circuits using machine learning |
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A method of tuning a reconfigurable silicon photonic circuit into an arbitrary unitary operator with machine learning was proposed to bypass the traditional phase-voltage calibration process and make the prediction of applied heating voltage directly. |
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School of Electrical and Electronic Engineering |
author_facet |
School of Electrical and Electronic Engineering Wan, L. X. Zhang, Haochi Huang, Jian Guo Zhang, Gong Kwek, L. C. Fitzsimons, J. Chong, Yi Dong Gong, J. B. Szameit, A. Zhou, X. Q. Yung, M. H. Jin, X. M. Su, X. L. Ser, Wee Gao, W. B. Liu, Ai Qun |
format |
Conference or Workshop Item |
author |
Wan, L. X. Zhang, Haochi Huang, Jian Guo Zhang, Gong Kwek, L. C. Fitzsimons, J. Chong, Yi Dong Gong, J. B. Szameit, A. Zhou, X. Q. Yung, M. H. Jin, X. M. Su, X. L. Ser, Wee Gao, W. B. Liu, Ai Qun |
author_sort |
Wan, L. X. |
title |
Determinating full parameters of U-matrix for reconfigurable Boson sampling circuits using machine learning |
title_short |
Determinating full parameters of U-matrix for reconfigurable Boson sampling circuits using machine learning |
title_full |
Determinating full parameters of U-matrix for reconfigurable Boson sampling circuits using machine learning |
title_fullStr |
Determinating full parameters of U-matrix for reconfigurable Boson sampling circuits using machine learning |
title_full_unstemmed |
Determinating full parameters of U-matrix for reconfigurable Boson sampling circuits using machine learning |
title_sort |
determinating full parameters of u-matrix for reconfigurable boson sampling circuits using machine learning |
publishDate |
2020 |
url |
https://hdl.handle.net/10356/138875 |
_version_ |
1681057675035541504 |