Bayesian machine learning of frequency-bin CNOT

Hsuan Hao Lu, Joseph M. Lukens, Brian P. Williams, Poolad Imany, Nicholas A. Peters, Andrew M. Weiner, Pavel Lougovski

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publication2019 Conference on Lasers and Electro-Optics, CLEO 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781943580576
DOIs
StatePublished - May 2019
Event2019 Conference on Lasers and Electro-Optics, CLEO 2019 - San Jose, United States
Duration: May 5 2019May 10 2019

Publication series

Name2019 Conference on Lasers and Electro-Optics, CLEO 2019 - Proceedings

Conference

Conference2019 Conference on Lasers and Electro-Optics, CLEO 2019
Country/TerritoryUnited States
CitySan Jose
Period05/5/1905/10/19

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