Novel quantum circuit for image compression utilizing modified Toffoli gate and quantized transformed coefficient alongside a novel reset gate

TL;DR

This paper introduces a novel quantum image compression circuit that uses a modified Toffoli gate and a reset gate to significantly reduce gate count while maintaining image quality, advancing quantum image processing efficiency.

Contribution

The paper presents a new quantum circuit design with a modified Toffoli gate and reset gate that reduces gate complexity by 44.21% compared to existing methods like DCTEFRQI.

Findings

Reduces gate count by 44.21% compared to DCTEFRQI.

Maintains consistent PSNR in image compression.

Outperforms existing techniques in gate efficiency and image quality.

Abstract

Quantum image computing has emerged as a groundbreaking field, revolutionizing how we store and process data at speeds incomparable to classical methods. Nevertheless, as image sizes expand, so does the complexity of qubit connections, posing significant challenges in the efficient representation and compression of quantum images. In response, we introduce a modified Toffoli gate state connection using a quantized transform coefficient preparation process. This innovative strategy streamlines circuit complexity by modifying state connection from the state connection information. In our operational control gates, only input 1 impacts the output, allowing us to modify the state connection and dramatically enhance the efficiency of the proposed circuit. As a result, the proposed approach significantly reduces the number of gates required for both image compression and representation. Our findings reveal that it requires an impressive 44.21 percent fewer gates than existing techniques, such as the Direct Cosine Transform Efficient Flexible Representation of Quantum Images (DCTEFRQI), all while maintaining a consistent peak signal-to-noise ratio (PSNR). For an image block size of 2^Sx2^Sy with q gray levels, the complexity of our approach can be succinctly expressed as, O[3q+log2Sx+log2Sy+2q(log2Sx+log2Sy)]. Here, Sx and Sy represent the X and Y positional control gates while q indicates the non-zero transform coefficients. Moreover, experimental evaluations strongly demonstrate that it excels in both compressing and representing quantum images compared to the DCTEFRQI approach, particularly excelling in the essential metrics of gate requirements and PSNR performance. Embrace the future of quantum imaging with our innovative solution, where efficiency meets excellence.