Quantum Denoising Diffusion Models
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.
65a76cce0637ea5cccef525d
Michael Kölle
admin_assigned
2024-01-17T09:46:40.646Z
6454ca9b1a543cf97b1b6c8e
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/hNCtOOd5Sh_r-HaWg-GaL.png
Michael Kölle
user
michaelkoelle
65a76cce0637ea5cccef525e
Gerhard Stenzel
claimed_verified
2024-01-17T11:00:57.453Z
65a7a60f8ae5a595375b78e9
/avatars/5e730924d7055624c1036e4fcf5cccc7.svg
Gerhard Stenzel
user
gstenzel
65a76cce0637ea5cccef525f
Jonas Stein
65a76cce0637ea5cccef5260
Sebastian Zielinski
65a76cce0637ea5cccef5261
Björn Ommer
65a76cce0637ea5cccef5262
Claudia Linnhoff-Popien
Quantum Denoising Diffusion Models
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.
Quantum diffusion models address sampling speed and parameter requirements in classical diffusion models by using variational quantum circuits, demonstrating superior performance and one-step image generation.
quantum machine learning variational quantum circuits diffusion-based image generation quantum diffusion models MNIST digits Fashion MNIST CIFAR-10 FID SSIM PSNR consistency model unitary single sampling architecture