Convolutional neural networks for mode on-demand high finesse optical resonator design
Convolutional neural networks for mode on-demand high finesse optical resonator design
We demonstrate the use of machine learning through convolutional neural networks to solve inverse design problems of optical resonator engineering. The neural network finds a harmonic modulation of a spherical mirror to generate a resonator mode with a given target topology (“mode on-demand”). The procedure allows us to optimize the shape of mirrors to achieve a significantly enhanced coupling strength and cooperativity between a resonator photon and a quantum emitter located at the center of the resonator. In a second example, a double-peak mode is designed which would enhance the interaction between two quantum emitters, e.g., for quantum information processing.
Karpov, Denis V.
87bed409-5074-4b29-a54a-1bcfa5dae435
Kurdiumov, Sergei
ecd4ef31-4251-4703-bce6-2caee793b209
Horak, Peter
520489b5-ccc7-4d29-bb30-c1e36436ea03
20 September 2023
Karpov, Denis V.
87bed409-5074-4b29-a54a-1bcfa5dae435
Kurdiumov, Sergei
ecd4ef31-4251-4703-bce6-2caee793b209
Horak, Peter
520489b5-ccc7-4d29-bb30-c1e36436ea03
Karpov, Denis V., Kurdiumov, Sergei and Horak, Peter
(2023)
Convolutional neural networks for mode on-demand high finesse optical resonator design.
Scientific Reports, 13 (1), [15567].
(doi:10.1038/s41598-023-42223-w).
Abstract
We demonstrate the use of machine learning through convolutional neural networks to solve inverse design problems of optical resonator engineering. The neural network finds a harmonic modulation of a spherical mirror to generate a resonator mode with a given target topology (“mode on-demand”). The procedure allows us to optimize the shape of mirrors to achieve a significantly enhanced coupling strength and cooperativity between a resonator photon and a quantum emitter located at the center of the resonator. In a second example, a double-peak mode is designed which would enhance the interaction between two quantum emitters, e.g., for quantum information processing.
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Submitted date: 3 November 2022
Accepted/In Press date: 6 September 2023
Published date: 20 September 2023
Additional Information:
Funding Information:
We acknowledge financial support by the UK Quantum Technology Program under the EPSRC Hub in Quantum Computing and Simulation (EP/T001062/1). The University of Southampton supercomputer Iridis 5 was used for numerical simulations.
Funding Information:
We acknowledge financial support by the UK Quantum Technology Program under the EPSRC Hub in Quantum Computing and Simulation (EP/T001062/1). The University of Southampton supercomputer Iridis 5 was used for numerical simulations.
Publisher Copyright:
© 2023, Springer Nature Limited.
Identifiers
Local EPrints ID: 481943
URI: http://eprints.soton.ac.uk/id/eprint/481943
ISSN: 2045-2322
PURE UUID: 40ca73de-2316-4d1a-ba8f-ceda73ffe64a
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Date deposited: 13 Sep 2023 17:21
Last modified: 18 Mar 2024 02:55
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Contributors
Author:
Denis V. Karpov
Author:
Sergei Kurdiumov
Author:
Peter Horak
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