Leveraging quantum latent diffusion models for data augmentation on the eurosat dataset

Generative models are transforming earth observation, enabling high-quality image generation for applications like data augmentation, cloud removal or image inpainting. This study investigates quantum latent diffusion models, integrating quantum variational circuits within latent diffusion processes. Using three ansatzes Rx-Rz-Rx, universal, and matchgate, we compare those models with classical latent diffusion models and generative adversarial networks. The quantum latent diffusion models with the universal ansatz achieved a 21.5% improvement in Frechet inception distance and a 29.9% improvement in kernel inception distance compared to the best classical counterpart, while maintaining competitive diversity with an inception score of 1.3152. These results demonstrate the efficiency of quantum-enhanced generative models in the specific case of data augmentation for earth observation applications, expecting similar improvements also in other related earth observation tasks.