A Fast Quantum Image Compression Algorithm based on Taylor Expansion

2025-02-15

AI Summary

To address the trade-off between reconstruction quality and bit-rate in high-resolution image compression, this paper proposes a quantum-classical hybrid compression framework: images are encoded as parameterized unitary operators, and reversible compression is simulated via quantum circuit compilation. A key innovation is the adoption of first-order Taylor expansion to approximate gradient computations during circuit training, substantially reducing the computational overhead of gradient evaluation. Experimental results on benchmark images—including Lena and Cameraman—demonstrate up to an 86% reduction in required optimization iterations while achieving lower compression distortion compared to baseline methods. Unlike conventional compression schemes, the proposed framework exhibits high efficiency, scalability, and hardware compatibility. By bridging quantum-inspired computation with classical image coding, it establishes a novel paradigm for quantum-enhanced image compression.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

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.

Problem

• Enhances quantum image compression efficiency
• Reduces computational cost and loss significantly
• Improves scalability for high-resolution images

Innovation

• Quantum image compression enhances
• Taylor expansion reduces computational cost
• Parameterized quantum circuits optimize