Quantum Denoising Diffusion Model Based on Gaussian Boson Sampling

ABSTRACT Diffusion models excel in generative modeling for complex distributions, but suffer from parameter inefficiency. Quantum variants partly address this via quantum resources, yet significant room remains for enhancing denoising efficiency. A quantum denoising diffusion model based on Gaussian boson sampling is proposed to further enhance denoising efficiency, which employs a trainable boson sampling circuit instead of traditional neural networks for backward denoising. Our model demonstrates superior denoising efficiency compared to both selected quantum denoising diffusion models and classical diffusion models(specified later in the text) on the MNIST handwritten digit dataset under the same number of training parameters. Moreover, our model achieves good denoising results under four different noise addition methods, which indicates its robust generalization capabilities. These results provide a paradigm for enhancing the denoising efficiency of quantum denoising diffusion models.