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

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. Exciton-polariton condensates in optical microcavities enable next-generation all-optical devices, but simulating their complex nonlinear dynamics remains computationally challenging. The authors demonstrate that Fourier Neural Operators trained on experimental and numerical data predict condensate behavior three orders of magnitude faster than conventional solvers while maintaining high accuracy.