mlsanityData to 14 Oct 2026

QUIET-SR: Quantum Image Enhancement Transformer for Single Image Super-Resolution

arXiv 2503.08759 ↗First posted 11 Mar 2025quant-ph cs.CV eess.IVcited by 2

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Abstract (arXiv)

Recent advancements in Single-Image Super-Resolution (SISR) using deep learning have significantly improved image restoration quality. However, the high computational cost of processing high-resolution images due to the large number of parameters in classical models, along with the scalability challenges of quantum algorithms for image processing, remains a major obstacle. In this paper, we propose the Quantum Image Enhancement Transformer for Super-Resolution (QUIET-SR), a hybrid framework that extends the Swin transformer architecture with a novel shifted quantum window attention mechanism, built upon variational quantum neural networks. QUIET-SR effectively captures complex residual mappings between low-resolution and high-resolution images, leveraging quantum attention mechanisms to enhance feature extraction and image restoration while requiring a minimal number of qubits, making it suitable for the Noisy Intermediate-Scale Quantum (NISQ) era. We evaluate our framework in MNIST (30.24 PSNR, 0.989 SSIM), FashionMNIST (29.76 PSNR, 0.976 SSIM) and the MedMNIST dataset collection, demonstrating that QUIET-SR achieves PSNR and SSIM scores comparable to state-of-the-art methods while using fewer parameters. Our efficient batching strategy directly enables massive parallelization on multiple QPU's paving the way for practical quantum-enhanced image super-resolution through coordinated QPU-GPU quantum supercomputing.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 20 of its 35 references that are in the map. First posted on arXiv 11 Mar 2025.

Builds on

20 of 35 references.

References · 20

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  3. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,908
  4. 2021SwinIR: Image Restoration Using Swin Transformercited by 1,887
  5. 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,552
  6. 2018Image Super-Resolution Using Very Deep Residual Channel Attention Networkscited by 1,264
  7. 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,224
  8. 2021MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classificationcited by 690
  9. 2019Parameterized quantum circuits as machine learning modelscited by 332
  10. 2020Efficient Image Super-Resolution Using Pixel Attentioncited by 87
  11. 2022Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restorationcited by 73
  12. 2023Quantum machine learning for image classificationcited by 57
8 more
  1. 2023Financial Fraud Detection using Quantum Graph Neural Networkscited by 35
  2. 2024Hybrid Quantum Vision Transformers for Event Classification in High Energy Physicscited by 10
  3. 2024QADQN: Quantum Attention Deep Q-Network for Financial Market Predictioncited by 9
  4. 2024Quantum Vision Transformers for Quark-Gluon Classificationcited by 7
  5. 2024How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubitscited by 6
  6. 2022Revisiting L1 Loss in Super-Resolution: A Probabilistic View and Beyondcited by 5
  7. 2023Quantum Annealing for Single Image Super-Resolutioncited by 5
  8. 2024Single image super-resolution based on trainable feature matching attention networkcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Design Space Exploration of Hybrid Quantum Neural Networks for Chronic Kidney Diseasecited by 2
  2. 2026QPredSGG: Hybrid Quantum Predicate Learning for Long-Tailed Scene Graph Generationcited by 0