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This research experimentally integrates room-temperature exciton-polariton condensates into a Generative Adversarial Network to physically realize stochastic nonlinear transformations. By leveraging intrinsic many-body dynamics, the system achieves superior sampling quality and output diversity compared to purely digital or laser-based baselines for digit-to-image translation tasks. The study establishes light-matter coupled systems as viable hardware accelerators that can enhance physics-informed machine learning architectures without requiring cryogenic cooling.
Generative modelling powered by room-temperature polariton condensates
arXiv:2606.15344v1 Announce Type: cross Abstract: 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.
Abstract: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.
Disordered Systems and Neural Networks (cond-mat.dis-nn); Machine Learning (cs.LG); Optics (physics.optics); Quantum Physics (quant-ph)
arXiv:2606.15344 [cond-mat.dis-nn]
(or arXiv:2606.15344v1 [cond-mat.dis-nn] for this version)
https://doi.org/10.48550/arXiv.2606.15344 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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