mlsanityData to 7 Oct 2026

Quantum Circuits in Diffusion Models: A Fair-Comparison Study and a Mechanistic Analysis of Angle-Embedding Failures

arXiv 2607.09108 ↗First posted 10 Jul 2026cs.LGcited by 0

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

We study the integration of variational quantum circuits (VQCs) into diffusion models through a squeeze-and-excitation (SE) channel-modulation scaffold that isolates the quantum contribution. Using a role-matched classical control and multi-seed significance testing across DDPM and latent diffusion on MNIST and CIFAR-10, with a score-based NCSN study on MNIST, we find that quantum cores achieve comparable mean FID to the classical control across DDPM and latent diffusion, while paired sampling-seed tests for EfficientSU2 detect no statistically significant difference. Although the quantum cores use 4.5--9× fewer core parameters than the role-matched control, parameter-matched classical controls attain comparable mean FID, so the experiments do not establish a quantum parameter-efficiency advantage. We further identify a structural failure in score-based NCSN: the unbounded score target, proportional to 1/σ, drives angle-embedding inputs far beyond the 2π period of rotation gates, causing phase aliasing and collapse of the quantum modulator. A bounding transformation, θ leftarrow π tanh(·), maps inputs to the non-aliasing domain and substantially improves both quantum cores. Since all circuits are classically simulated at a few-qubit scale, we do not claim quantum advantage. Instead, the study provides a fair-comparison protocol for quantum-enhanced generative models and a mechanistic account of when and why angle embeddings fail.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 12 of its 21 references that are in the map. First posted on arXiv 10 Jul 2026.

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12 of 21 references.

Closest ancestors · 5

Same direction, newest first

  1. 2024Quantum Denoising Diffusion Models13 Jan
  2. 2023Quantum-Noise-Driven Generative Diffusion Models23 Aug
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Models20 Dec
  4. 2020Score-Based Generative Modeling through Stochastic Differential Equations26 Nov
  5. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 7

  1. 2017Squeeze-and-Excitation Networkscited by 4,763
  2. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  3. 2020Variational Quantum Algorithmscited by 687
  4. 2018Supervised learning with quantum enhanced feature spacescited by 654
  5. 2018Quantum generative adversarial networkscited by 128
  6. 2018Differentiable Learning of Quantum Circuit Born Machinecited by 90
  7. 2024Better than classical? The subtle art of benchmarking quantum machine learning modelscited by 82

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  5. 2025Overcoming Dimensional Factorization Limits in Discrete Diffusion Models through Quantum Joint Distribution Learning
  6. 2025Mitigating Barren Plateaus in Quantum Denoising Diffusion Probabilistic Model
  7. 2026An Hybrid Quantum-Classical Diffusion Model for Image Generation
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