Non-Gaussian state design with the quantum frequency processor

Andrew J. Pizzimenti, Joseph M. Lukens, Hsuan Hao Lu, Nicholas A. Peters, Saikat Guha, Christos Gagatsos

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

Abstract

We develop an approach for engineering non-Gaussian photonic states in discrete frequency bins. Combining the quantum frequency processor and photon-number-resolving detection, simulated examples demonstrate the potential for producing high-fidelity cat states with reasonable resource requirements.

Original languageEnglish
Title of host publication2022 Conference on Lasers and Electro-Optics, CLEO 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781957171050
StatePublished - 2022
Event2022 Conference on Lasers and Electro-Optics, CLEO 2022 - San Jose, United States
Duration: May 15 2022May 20 2022

Publication series

Name2022 Conference on Lasers and Electro-Optics, CLEO 2022 - Proceedings

Conference

Conference2022 Conference on Lasers and Electro-Optics, CLEO 2022
Country/TerritoryUnited States
CitySan Jose
Period05/15/2205/20/22

Bibliographical note

Publisher Copyright:
© Optica Publishing Group 2022, © 2022 The Author(s)

Funding

We thank R. C. Pooser and K. K. Sabapathy for useful discussions. This research was performed in part at Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U.S. Department of Energy under contract no. DE-AC05-00OR22725. Funding was provided by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, through the Transparent Optical Quantum Networks for Distributed Science Program and Early Career Research Program (Field Work Proposals ERKJ355 and ERKJ353). A.J.P. acknowledges support from the U.S. Department of Energy, Office of Science, Office of Workforce Development for Teachers and Scientists Science Undergraduate Laboratory Internship Program. C.N.G. and S.G. acknowledge an ORNL/DOE subaward under grant number 4000178321, and partial support from the Office of Naval Research (ONR) under grant number N00014-19-1-2189.

FundersFunder number
Office of Workforce Development for Teachers
Office of Naval ResearchN00014-19-1-2189
U.S. Department of EnergyDE-AC05-00OR22725, 4000178321
Office of Science
Advanced Scientific Computing ResearchERKJ355, ERKJ353
Oak Ridge National Laboratory

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