Quantum Fourier Transform-Based Adaptive Image Compression and Transmission System

Abstract

This study proposes an adaptive quantum Fourier transform (QFT)-based framework for efficient, high-quality near-lossless image compression and transmission. The system converts the input image into a bitstream, applies channel encoding, and maps it to quantum states. A key innovation is the dynamic selection of the qubit encoding size based on real-time channel conditions, optimizing the trade-off between compression efficiency and noise resilience. The proposed QFT framework selectively transmits a subset of coefficients through the noisy quantum channel. At the receiver, the full quantum state is estimated, followed by inverse QFT and channel decoding to reconstruct the original image. Compared to fixed-qubit methods, the proposed system achieves superior performance, with compression ratios of up to 256:1 and near-perfect reconstruction quality (PSNR approaching infinity and SSIM of 1). These results demonstrate its potential for future quantum communication applications.