A Fourier neural operator approach for modelling exciton-polariton condensate systems

Abstract

A plethora of next-generation all-optical devices based on exciton-polaritons have been proposed in latest years, including prototypes of transistors, switches, analogue quantum simulators and others. However, for such systems consisting of multiple polariton condensates, it is still challenging to predict their properties in a fast and accurate manner. The condensate physics is conventionally described by polariton Gross-Pitaevskii equations (GPEs). While GPU-based solvers currently exist, we propose a significantly more efficient machine-learning-based Fourier neural operator approach to find the solution to the GPE coupled with exciton rate equations, trained on both numerical and experimental datasets. The proposed method predicts solutions almost three orders of magnitude faster than CUDA-based solvers in numerical studies, maintaining the high degree of accuracy. Our method not only accelerates simulations but also opens the door to faster, more scalable designs for all-optical chips and devices, offering promising implications for quantum computing, neuromorphic systems, and various photonic applications.

Metadata

Item Type:	Article
Authors/Creators:	Wang, Y. https://orcid.org/0000-0002-0688-3276 Sathujoda, S.T. https://orcid.org/0009-0009-4913-7669 Sawicki, K. https://orcid.org/0000-0003-1617-2678 Gandhi, K. Aviles-Rivero, A.I. Lagoudakis, P.G. https://orcid.org/0000-0002-3557-5299
Copyright, Publisher and Additional Information:	© The Author(s) 2025. This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/bync-nd/4.0/.
Keywords:	Bose–Einstein condensates; Computational science; Polaritons
Dates:	Submitted: 21 March 2025 Accepted: 2 November 2025 Published (online): 25 November 2025 Published: 25 November 2025
Institution:	The University of Sheffield
Academic Units:	The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematical and Physical Sciences
Date Deposited:	03 Sep 2026 15:18
Last Modified:	03 Sep 2026 15:18
Status:	Published
Publisher:	Springer Science and Business Media LLC
Refereed:	Yes
Identification Number:	10.1038/s42005-025-02409-2
Related URLs:	Dataset Software or Code
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:245018