Open AccessBayesian machine learning of frequency-bin CNOT
Author(s) -
Hsuan-Hao Lu,
Joseph M. Lukens,
Brian P. Williams,
Poolad Imany,
Nicholas A. Peters,
Andrew M. Weiner,
Pavel Lougovski
Publication year - 2019
Publication title -
2019 conference on lasers and electro-optics (cleo)
Language(s) - English
Resource type - Conference proceedings
ISBN - 978-1-943580-57-6
DOI - 10.1364/cleo_qels.2019.ff1f.3
Subject(s) - photonics and electrooptics
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. © 2019 The Author(s)
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