TY - GEN
T1 - Machine learning assisted quantum super-resolution microscopy
AU - Kudyshev, Zhaxylyk A.
AU - Sychev, Demid
AU - Martin, Zachariah
AU - Bogdanov, Simeon
AU - Xu, Xiaohui
AU - Kildishev, Alexander V.
AU - Boltasseva, Alexandra
AU - Shalaev, Vladimir M.
N1 - Funding Information:
ML-assisted super-resolution framework ensures a 12 times speed-up compared to the conventional biexponential fitting for retrieving the second-order autocorrelation value at zero delay in the microscopy of quantum optical light sources. The developed framework can be extended for rapid measurement of the higher-order autocorrelation functions, which opens up a way for the practical realization of scalable quantum super-resolution imaging. Our approach is compatible with the CW excitation regime, which reduces emitter photobleaching due to multi-photon absorption and does not impose restrictions on the fluorescence lifetime. Therefore, it can be applied to a wide variety of quantum emitters used in biological labeling and quantum photonics. This work is supported by the U.S. Department of Energy (DOE), Office of Science through the Quantum Science Center (QSC), a National Quantum Information Science Research Center (developing ML algorithms), DARPA/DSO Extreme Optics and Imaging (EXTREME) Program (HR00111720032) (A.V.K.) and National Science Foundation award 2029553-ECCS (sample fabrication).
Publisher Copyright:
© 2021 OSA.
PY - 2021/5
Y1 - 2021/5
N2 - A machine learning assisted framework significantly speeds up image acquisition in super-resolution microscopy based on photon antibunching. The technique is compatible with a CW excitation regime and applicable to a wide range of quantum emitters.
AB - A machine learning assisted framework significantly speeds up image acquisition in super-resolution microscopy based on photon antibunching. The technique is compatible with a CW excitation regime and applicable to a wide range of quantum emitters.
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M3 - Conference contribution
AN - SCOPUS:85120495135
T3 - 2021 Conference on Lasers and Electro-Optics, CLEO 2021 - Proceedings
BT - 2021 Conference on Lasers and Electro-Optics, CLEO 2021 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 Conference on Lasers and Electro-Optics, CLEO 2021
Y2 - 9 May 2021 through 14 May 2021
ER -