title: Quantum Denoising Diffusion Models
summary: 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.
paper_authors._id: 65a76cce0637ea5cccef525d
paper_authors.name: Michael Kölle
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-17T09:46:40.646Z
paper_authors._id: 6454ca9b1a543cf97b1b6c8e
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/hNCtOOd5Sh_r-HaWg-GaL.png
paper_authors.fullname: Michael Kölle
paper_authors.type: user
paper_authors.user: michaelkoelle
paper_authors._id: 65a76cce0637ea5cccef525e
paper_authors.name: Gerhard Stenzel
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-01-17T11:00:57.453Z
paper_authors._id: 65a7a60f8ae5a595375b78e9
paper_authors.avatarUrl: /avatars/5e730924d7055624c1036e4fcf5cccc7.svg
paper_authors.fullname: Gerhard Stenzel
paper_authors.type: user
paper_authors.user: gstenzel
paper_authors._id: 65a76cce0637ea5cccef525f
paper_authors.name: Jonas Stein
paper_authors._id: 65a76cce0637ea5cccef5260
paper_authors.name: Sebastian Zielinski
paper_authors._id: 65a76cce0637ea5cccef5261
paper_authors.name: Björn Ommer
paper_authors._id: 65a76cce0637ea5cccef5262
paper_authors.name: Claudia Linnhoff-Popien
paper_title: Quantum Denoising Diffusion Models
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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