Quantum Denoising Diffusion Models
Michael Kölle Gerhard Stenzel Jonas Stein Sebastian Zielinski Björn Ommer Claudia Linnhoff-Popien
In recent years, machine learning models like DALL-E, Craiyon, and Stable
Diffusion have gained significant attention for their ability to generate
high-resolution images from concise descriptions. Concurrently, quantum
computing is showing promising advances, especially with quantum machine
learning which capitalizes on quantum mechanics to meet the increasing
computational requirements of traditional machine learning algorithms. This
paper explores the integration of quantum machine learning and variational
quantum circuits to augment the efficacy of diffusion-based image generation
models. Specifically, we address two challenges of classical diffusion models:
their low sampling speed and the extensive parameter requirements. We introduce
two quantum diffusion models and benchmark their capabilities against their
classical counterparts using MNIST digits, Fashion MNIST, and CIFAR-10. Our
models surpass the classical models with similar parameter counts in terms of
performance metrics FID, SSIM, and PSNR. Moreover, we introduce a consistency
model unitary single sampling architecture that combines the diffusion
procedure into a single step, enabling a fast one-step image generation.