A Fourier Neural Operator Approach for Modelling Exciton-Polariton Condensate Systems
Yuan Wang, Surya T. Sathujoda, Krzysztof Sawicki, Kanishk Gandhi, Angelica I Aviles-Rivero, Pavlos G. Lagoudakis
近年来,已经提出了大量基于exciton-polaritons的下一代全光器件,包括晶体管,开关,模拟量子模拟器等的原型。 然而,对于由多个极子凝聚态组成的系统,以快速和准确的方式预测其特性仍然具有挑战性。 凝析物理是由Gross-Pitaevskii方程(GPE)常规描述的。 虽然目前存在基于GPU的求解器,但我们提出了一个效率更高的基于机器学习的傅里叶神经运算方法,以找到GPE的解决方案,以及exciton速率方程,在数值和实验数据集上训练。 拟议的方法预测解决方案在数值研究中比基于CUDA的求解器快近三个数量级,保持高度的准确性。 我们的方法不仅加速了模拟,而且还为全光学芯片和设备提供了更快,更可扩展的设计的大门,为量子计算,神经形态系统等提供了深远的影响。
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 s...
