TY - GEN
T1 - Merging machine learning with quantum photonics
T2 - CLEO: QELS_Fundamental Science, CLEO_QELS 2020
AU - Kudyshev, Zhaxylyk
AU - Bogdanov, Simeon
AU - Isacsson, Theodor
AU - Kildishev, Alexander V.
AU - Boltasseva, Alexandra
AU - Shalaev, Vladimir M.
N1 - Funding Information:
This work was supported in part by the U.S. Department of Energy (DE-SC0017717; S.B. and V.S.), NSF (0939370-CCF, Zh.K.), DARPA/DSO Extreme Optics and Imaging (EXTREME) Program (HR00111720032, A.K.).
Publisher Copyright:
CLEO 2020 © OSA 2020.
PY - 2020
Y1 - 2020
N2 - Single quantum emitters offer useful functionalities for quantum optics, but measurements of their properties are time-consuming. We demonstrate that machine learning dramatically reduces data collection time (1s), increasing the accuracy of second-order autocorrelation measurements (>90%).
AB - Single quantum emitters offer useful functionalities for quantum optics, but measurements of their properties are time-consuming. We demonstrate that machine learning dramatically reduces data collection time (1s), increasing the accuracy of second-order autocorrelation measurements (>90%).
UR - http://www.scopus.com/inward/record.url?scp=85095421234&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85095421234&partnerID=8YFLogxK
U2 - 10.1364/CLEO_QELS.2020.FM4C.4
DO - 10.1364/CLEO_QELS.2020.FM4C.4
M3 - Conference contribution
AN - SCOPUS:85095421234
T3 - Optics InfoBase Conference Papers
BT - CLEO
PB - OSA - The Optical Society
Y2 - 10 May 2020 through 15 May 2020
ER -