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dc.contributor.authorWan, L. X.en_US
dc.contributor.authorZhang, Haochien_US
dc.contributor.authorHuang, Jian Guoen_US
dc.contributor.authorZhang, Gongen_US
dc.contributor.authorKwek, L. C.en_US
dc.contributor.authorFitzsimons, J.en_US
dc.contributor.authorChong, Yi Dongen_US
dc.contributor.authorGong, J. B.en_US
dc.contributor.authorSzameit, A.en_US
dc.contributor.authorZhou, X. Q.en_US
dc.contributor.authorYung, M. H.en_US
dc.contributor.authorJin, X. M.en_US
dc.contributor.authorSu, X. L.en_US
dc.contributor.authorSer, Weeen_US
dc.contributor.authorGao, W. B.en_US
dc.contributor.authorLiu, Ai Qunen_US
dc.identifier.citationWan, 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.18en_US
dc.description.abstractA 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.en_US
dc.rights© 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).en_US
dc.subjectEngineering::Electrical and electronic engineering::Optics, optoelectronics, photonicsen_US
dc.titleDeterminating full parameters of U-matrix for reconfigurable Boson sampling circuits using machine learningen_US
dc.typeConference Paperen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.contributor.schoolSchool of Physical and Mathematical Sciencesen_US
dc.contributor.conference2018 Conference on Lasers and Electro-Optics (CLEO): Applications and Technologyen_US
dc.description.versionPublished versionen_US
dc.subject.keywordsMachine Learningen_US
dc.subject.keywordsHeating Systemsen_US
dc.citation.conferencelocationSan Jose, USAen_US
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