发表机构
Nanyang Technological University; Mahindra University（南洋理工大学; 马欣德拉大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出混合量子-经典框架，结合变分量子小波变换和量子卷积网络实现灰度图像压缩，经SRGAN增强后重建性能接近并超越JPEG2000，验证了量子压缩与深度学习结合的潜力。
AI 中文摘要
本文提出了一种用于灰度图像压缩和解压缩的混合量子-经典框架，充分利用了量子计算和深度学习的优势。压缩流程集成了变分量子多贝西小波变换（V-QDWT）和可训练的量子卷积神经网络（QCNN），并通过端到端优化以实现高效的、图像自适应的多分辨率分析和基于纠缠的特征缩减。输入图像采用正态任意叠加态（NASS）表示进行编码，从而实现紧凑且可扩展的量子存储。对于解压缩，我们实现了逆QCNN和V-QDWT电路以原生重建粗粒度图像特征，随后使用经典的超分辨率生成对抗网络（SRGAN）来增强感知质量。在基准灰度数据集上的实验评估证明了我们混合方法的有效性。通过联合训练量子层，基础量子流程的性能与经典JPEG2000标准非常接近。随后的SRGAN细化显著提升了重建效果，实现了优越的结构保真度（PSNR：30.0667 dB，SSIM：0.8744，直方图相关性：0.9244）。直方图分析和定性比较进一步验证了细粒度纹理和强度分布的重建效果。我们的研究结果凸显了将变分量子压缩与经典深度学习相结合，在量子增强计算环境中实现高效、可扩展且具有感知意识的图像处理的潜力。
英文摘要
This paper presents a hybrid quantum-classical framework for grayscale image compression and decompression, leveraging the strengths of quantum computing and deep learning. The compression pipeline integrates a Variational Quantum Daubechies Wavelet Transform (V-QDWT) and a trainable Quantum Convolutional Neural Network (QCNN) optimized end-to-end to achieve efficient, image-adaptive multi-resolution analysis and entanglement-based feature reduction. Input images are encoded using the Normal Arbitrary Superposition State (NASS) representation, enabling compact and scalable quantum storage. For decompression, we implement inverse QCNN and V-QDWT circuits to reconstruct coarse image features natively, followed by a classical Super-Resolution Generative Adversarial Network (SRGAN) to enhance perceptual quality. Experimental evaluations on benchmark grayscale datasets demonstrate the efficacy of our hybrid approach. By jointly training the quantum layers, the base quantum pipeline closely rivals classical JPEG2000 standards. Subsequent SRGAN refinement substantially pushes the boundaries of the reconstruction, achieving superior structural fidelity (PSNR: 30.0667 dB, SSIM: 0.8744, Histogram Correlation: 0.9244). Histogram analysis and qualitative comparisons further validate the restoration of fine textures and intensity distributions. Our findings highlight the potential of combining variational quantum compression with classical deep learning to enable efficient, scalable, and perceptually-aware image processing in quantum-enhanced computing environments.
