May 2019

Conference Paper

Bayesian machine learning of frequency-bin CNOT

By:
Lu, Hsuan-Hao; Lukens, Joseph M; Williams, Brian P; Imany, Poolad; Peters, Nicholas A; Weiner, Andrew; Lougovski, Pavel
Journal Name:
CLEO:QELS Fundamental Science
Page Number:
3
Volume:
2019
Issue Number:
FF1F
Publication Date:
May 2019
Conference Name:
CLEO: QELS_Fundamental Science 2019
Conference Location:
San Jose, California, United States of America
Conference Sponsor:
OSA
View DOI Listing:
https://doi.org/10.1364/CLEO_QELS.2019.FF1F.3

Abstract

We analyze the first experimental two-photon frequency-bin gate: a coincidence-basis CNOT. A novel characterization approach based on Bayesian machine learning is developed to estimate the gate performance with measurements in the logical basis alone.