Generative modelling requires efficient stochastic nonlinear transformations and physical platforms that can naturally realise them. We experimentally demonstrate that nonlinear optical systems operating in the strong light-matter coupling regime can serve as physical transformation layers for conditional generative modelling. Specifically, we develop a workflow in which room-temperature exciton-polariton condensates formed in organic dye microcavities act as a physical stochastic transform within a generative adversarial network and enable conditional digit-to-image translation. By using the nonlinear many-body dynamics and intrinsic stochasticity of polariton condensates, the workflow outperforms baseline approaches based on digitally injected perturbations. We find that polariton-enabled sampling via generative adversarial network (Polariton GAN) yields improved inception score, digit preservation accuracy and structural similarity compared with both digital sampling and laser-based systems. We further show that spatially correlated output variations can naturally regularise adversarial training and enhance output diversity. Our results establish polariton condensation as a new computational resource for generative modelling, opening a pathway towards physics-enhanced machine learning systems.

翻译：生成式建模需要高效的随机非线性变换以及能自然实现这些变换的物理平台。我们通过实验证明，工作在强光-物质耦合状态下的非线性光学系统可以作为条件生成式建模的物理变换层。具体而言，我们开发了一种工作流程，其中在有机染料微腔中形成的室温激子-极化激元凝聚体在生成对抗网络中充当物理随机变换，并实现条件性数字到图像的转换。通过利用极化激元凝聚体的非线性多体动力学和内在随机性，该工作流程的性能优于基于数字注入扰动的基线方法。我们发现，通过生成对抗网络进行的极化激元辅助采样（极化激元生成对抗网络）与数字采样和基于激光的系统相比，在初始分数、数字保留准确度和结构相似性方面均有所提升。我们进一步表明，空间相关的输出变化可以自然地正则化对抗训练并增强输出多样性。我们的研究结果将极化激元凝聚确立为生成式建模的一种新计算资源，开辟了通往物理增强型机器学习系统的道路。