Quantum Images with Super-Resolution

Prashant Gohel, Manjunath Joshi

Ron Tenne, Uri Rossman, Batel Rephael +5 more

Siddhant Dutta, Nouhaila Innan, Khadijeh Najafi +2 more

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.

Andrew Forbes, Valeria Rodríguez-Fajardo

Ystallonne Carlos da Silva Alves

Super-Resolution (SR) is a technique that has been exhaustively exploited and incorporates strategic aspects to image processing. As quantum computers gradually evolve and provide unconditional proof of computational advantage at solving intractable problems over their classical counterparts, quantum computing emerges with the compelling prospect to offer exponential speedup to process computationally expensive operations, such as the ones verified in SR imaging. Envisioning the design of a quantum-ready method for near-term noisy devices and igniting Rapid and Accurate Image Super Resolution (RAISR), an implementation using Variational Quantum Eigensolver (VQE) is demonstrated. This study proposes an approach that combines the benefits of RAISR, a non hallucinating and computationally efficient method, and VQE, a hybrid classical-quantum algorithm, to conduct SR with the support of quantum computation, preserving quantitative performance in terms of Image Quality Assessment (IQA).

Ystallonne Carlos da Silva Alves

Super-Resolution (SR) is a technique that has been exhaustively exploited and incorporates strategic aspects to image processing. As quantum computers gradually evolve and provide unconditional proof of a computational advantage at solving intractable problems over their classical counterparts, quantum computing emerges with the compelling argument of offering exponential speed-up to process computationally expensive operations. Envisioning the design of parallel, quantum-ready algorithms for near-term noisy devices and igniting Rapid and Accurate Image Super Resolution (RAISR), an implementation applying variational quantum computation is demonstrated for enhancing degraded imagery. This study proposes an approach that combines the benefits of RAISR, a non hallucinating and computationally efficient method, and Variational Quantum Eigensolver (VQE), a hybrid classical-quantum algorithm, to conduct SR with the support of a quantum computer, while preserving quantitative performance in terms of Image Quality Assessment (IQA). It covers the generation of additional hash-based filters learned with the classical implementation of the SR technique, in order to further explore performance improvements, produce images that are significantly sharper, and induce the learning of more powerful upscaling filters with integrated enhancement effects. As a result, it extends the potential of applying RAISR to improve low quality assets generated by low cost cameras, as well as fosters the eventual implementation of robust image enhancement methods powered by the use of quantum computation.