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Quantum Denoising Diffusion Models
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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.
Forward citations
Cited by 6 Pith papers
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Quantum Circuits in Diffusion Models: A Fair-Comparison Study and a Mechanistic Analysis of Angle-Embedding Failures
Quantum SE cores match role-matched classical FID on DDPM/LDM with no significant difference and no efficiency advantage; NCSN fails via angle aliasing fixed by π tanh.
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MonoPartNeRF:Human Reconstruction from Monocular Video via Part-Based Neural Radiance Fields
Randomized weak measurements provide a physically motivated forward diffusion for quantum states, with reverse recovery via learned unitary controls, Petz maps, or classical shadows.
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Quantum Reversibility Meets Classical Reverse Diffusion
The semiclassical limit of the Petz-reversed Lindblad equation reproduces the Bayes-rule reverse-time diffusion equation, with the reference state's Wigner function playing the role of the classical score distribution.
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Enhancing Quantum Diffusion Models with Pairwise Bell State Entanglement
A pairwise Bell-state entanglement trick reduces the trainable parameter count in a quantum diffusion model, but the reported image quality scores are too low to support the claimed advances.
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Text to Image Generation and Editing: A Survey
A broad survey of text-to-image generation and editing research from 2021 to 2024, organized by architecture and comparison tables.
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Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers
A structured tutorial that introduces quantum machine learning concepts, algorithms, theory, and PennyLane code to classical ML practitioners.
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