TY - GEN
T1 - Stopped and stationary light with cold atomic ensembles and machine learning
AU - Buchler, Ben
AU - Everett, Jesse
AU - Cho, Young Wook
AU - Tranter, Aaron
AU - Slatyer, Harry
AU - Hush, Michael
AU - Paul, Karun
AU - Vernaz-Gris, Pierre
AU - Leung, Anthony
AU - Higginbottom, Daniel
AU - Lam, Ping Koy
AU - Campbell, Geoff
N1 - Publisher Copyright:
© OSA 2018.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85048940250&partnerID=8YFLogxK
U2 - 10.1364/CLEO_QELS.2018.FM1G.5
DO - 10.1364/CLEO_QELS.2018.FM1G.5
M3 - Conference contribution
SN - 9781943580422
T3 - Optics InfoBase Conference Papers
BT - CLEO
PB - Optica Publishing Group
T2 - CLEO: QELS_Fundamental Science, CLEO_QELS 2018
Y2 - 13 May 2018 through 18 May 2018
ER -