Stopped and stationary light with cold atomic ensembles and machine learning

Ben Buchler, Jesse Everett, Young Wook Cho, Aaron Tranter, Harry Slatyer, Michael Hush, Karun Paul, Pierre Vernaz-Gris, Anthony Leung, Daniel Higginbottom, Ping Koy Lam, Geoff Campbell

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Quantum information systems demand methods for the storage and manipulation of qubits. For optical qubits, atomic ensembles provide a potential platform for such operations. In this work, we demonstrate a stopped light optical quantum memory with efficiency up to 87%. We also demonstrate and visualise stationary light, which could potentially enhance weak optical nonlinearities. At the heart of our experiments is a laser-cooled atomic ensemble, which has recently been optimised with the help of a machine learning system that uses an artificial neural network.

Original languageEnglish
Title of host publicationCLEO
Subtitle of host publicationQELS_Fundamental Science, CLEO_QELS 2018
PublisherOSA - The Optical Society
ISBN (Electronic)9781557528209
Publication statusPublished - 2018
EventCLEO: QELS_Fundamental Science, CLEO_QELS 2018 - San Jose, United States
Duration: 2018 May 132018 May 18

Publication series

NameOptics InfoBase Conference Papers
VolumePart F93-CLEO_QELS 2018


OtherCLEO: QELS_Fundamental Science, CLEO_QELS 2018
Country/TerritoryUnited States
CitySan Jose

Bibliographical note

Publisher Copyright:
© OSA 2018.

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Mechanics of Materials


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