A Fourier Neural Operator Approach for Modelling Exciton-Polariton Condensate Systems

TL;DR

This paper introduces a Fourier neural operator method that significantly accelerates the simulation of exciton-polariton condensate systems described by GPEs, enabling faster and scalable design of optical devices.

Contribution

It presents a novel machine learning approach using Fourier neural operators to efficiently solve coupled GPE and exciton rate equations for polariton systems.

Findings

Predicts solutions nearly 1000 times faster than CUDA-based solvers

Maintains high accuracy in numerical and experimental datasets

Enables scalable and rapid design of optical quantum devices

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 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 profound implications for quantum computing, neuromorphic systems, and beyond.