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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Main Authors: 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
Other Authors: School of Electrical and Electronic Engineering
Format: Conference or Workshop Item
Language:English
Published: 2020
Subjects:
Online Access:https://hdl.handle.net/10356/138875
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Institution: Nanyang Technological University
Language: English
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spelling 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
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering::Optics, optoelectronics, photonics
Machine Learning
Heating Systems
spellingShingle 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
description 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.
author2 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