A Fourier Neural Operator Approach for Modelling Exciton-Polariton Condensate Systems
This work addresses the need for fast and accurate simulations in designing next-generation all-optical devices, such as transistors and quantum simulators, with incremental improvements in efficiency.
The authors tackled the challenge of predicting properties of exciton-polariton condensate systems, which are described by Gross-Pitaevskii equations, by proposing a Fourier neural operator approach that predicts solutions almost three orders of magnitude faster than existing GPU-based solvers while maintaining high accuracy.
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.