A Fast Quantum Image Compression Algorithm based on Taylor Expansion

TL;DR

This paper presents a quantum image compression algorithm that leverages Taylor expansion to reduce computational cost and loss, demonstrating high efficiency and scalability on benchmark images.

Contribution

It introduces a novel quantum image compression method using first-order Taylor expansion within parameterized quantum circuits, improving efficiency over previous approaches.

Findings

Achieves up to 86% reduction in iterations

Maintains lower compression loss on benchmark images

Effective for high-resolution image compression

Abstract

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.