用泰勒展开加速量子图像压缩，提升效率与画质。
A Fast Quantum Image Compression Algorithm based on Taylor Expansion
- 用一阶泰勒展开降低量子电路计算开销
- 压缩迭代次数减少86%，损失更低
- 适合高分辨率图像处理场景
随着图像存储需求增长，传统压缩方法在压缩率与画质间难以平衡。本文基于参数化量子电路升级量子图像压缩算法，将图像数据编码为酉算子参数，并利用量子编译算法模拟加密过程。通过引入一阶泰勒展开，显著降低计算成本与压缩损失，优于先前版本。在Lenna和Cameraman等基准图像上的实验表明，该方法最多可减少86%的迭代次数，同时保持较低压缩损失，适用于高分辨率图像。结果验证了该算法在效率与可扩展性方面的优势，是未来图像处理的有前景方案。
原文摘要 · Abstract (English)
With the increasing demand for storing images, traditional image compression methods face challenges in balancing the compressed size and image quality. However, the hybrid quantum-classical model can recover this weakness by using the advantage of qubits. In this study, we upgrade a quantum image compression algorithm within parameterized quantum circuits. Our approach encodes image data as unitary operator parameters and applies the quantum compilation algorithm to emulate the encryption process. By utilizing first-order Taylor expansion, we significantly reduce both the computational cost and loss, better than the previous version. Experimental results on benchmark images, including Lenna and Cameraman, show that our method achieves up to 86\% reduction in the number of iterations while maintaining a lower compression loss, better for high-resolution images. The results confirm that the proposed algorithm provides an efficient and scalable image compression mechanism, making it a promising candidate for future image processing applications.
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