TY - GEN
T1 - Quantum-Optimal Binary Object Classification in Sub-Diffraction Incoherent Imaging
AU - Grace, Michael R.
AU - Guha, Saikat
N1 - Funding Information:
The views, opinions and/or findings expressed are those of the author and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government. This research was developed with funding from the Defense Advanced Research Projects Agency.
Publisher Copyright:
© 2021 OSA.
PY - 2021/5
Y1 - 2021/5
N2 - We derive the quantum limit on average error for hypothesis tests between any two incoherent, diffraction-limited objects and identify quantum-optimal measurements that achieve a quadratic scaling improvement over direct imaging.
AB - We derive the quantum limit on average error for hypothesis tests between any two incoherent, diffraction-limited objects and identify quantum-optimal measurements that achieve a quadratic scaling improvement over direct imaging.
UR - http://www.scopus.com/inward/record.url?scp=85120498485&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85120498485&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85120498485
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 -