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

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

Department of Physics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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 language	English
Title of host publication	2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Print)	9781943580422
Publication status	Published - 2018 Aug 6
Event	2018 Conference on Lasers and Electro-Optics, CLEO 2018 - San Jose, United States Duration: 2018 May 13 → 2018 May 18

Publication series

Name	2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings

Other

Other	2018 Conference on Lasers and Electro-Optics, CLEO 2018
Country	United States
City	San Jose
Period	18/5/13 → 18/5/18

Bibliographical note

Publisher Copyright:
© 2018 OSA.

All Science Journal Classification (ASJC) codes

Instrumentation
Atomic and Molecular Physics, and Optics

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Buchler, B., Everett, J., Cho, Y. W., Tranter, A., Slatyer, H., Hush, M., Paul, K., Vernaz-Gris, P., Leung, A., Higginbottom, D., Lam, P. K., & Campbell, G. (2018). Stopped and stationary light with cold atomic ensembles and machine learning. In 2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings [8426522] (2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc..

Buchler, Ben ; Everett, Jesse ; Cho, Young Wook ; Tranter, Aaron ; Slatyer, Harry ; Hush, Michael ; Paul, Karun ; Vernaz-Gris, Pierre ; Leung, Anthony ; Higginbottom, Daniel ; Lam, Ping Koy ; Campbell, Geoff. / Stopped and stationary light with cold atomic ensembles and machine learning. 2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. (2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings).

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title = "Stopped and stationary light with cold atomic ensembles and machine learning",

abstract = "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.",

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

note = "Publisher Copyright: {\textcopyright} 2018 OSA.; 2018 Conference on Lasers and Electro-Optics, CLEO 2018 ; Conference date: 13-05-2018 Through 18-05-2018",

year = "2018",

month = aug,

day = "6",

language = "English",

isbn = "9781943580422",

series = "2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings",

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Buchler, B, Everett, J, Cho, YW, Tranter, A, Slatyer, H, Hush, M, Paul, K, Vernaz-Gris, P, Leung, A, Higginbottom, D, Lam, PK & Campbell, G 2018, Stopped and stationary light with cold atomic ensembles and machine learning. in 2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings., 8426522, 2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2018 Conference on Lasers and Electro-Optics, CLEO 2018, San Jose, United States, 18/5/13.

Stopped and stationary light with cold atomic ensembles and machine learning. / Buchler, Ben; Everett, Jesse; Cho, Young Wook; Tranter, Aaron; Slatyer, Harry; Hush, Michael; Paul, Karun; Vernaz-Gris, Pierre; Leung, Anthony; Higginbottom, Daniel; Lam, Ping Koy; Campbell, Geoff.

2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. 8426522 (2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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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

PY - 2018/8/6

Y1 - 2018/8/6

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.

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M3 - Conference contribution

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SN - 9781943580422

T3 - 2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings

BT - 2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 Conference on Lasers and Electro-Optics, CLEO 2018

Y2 - 13 May 2018 through 18 May 2018

ER -

Stopped and stationary light with cold atomic ensembles and machine learning

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All Science Journal Classification (ASJC) codes

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