This work introduces a compact and hardware efficient method for compressing color images using near term quantum devices. The approach segments the image into fixed size blocks called bixels, and computes the total intensity within each block. A global histogram with B bins is then constructed from these block intensities, and the normalized square roots of the bin counts are encoded as amplitudes into an n qubit quantum state. Amplitude embedding is performed using PennyLane and executed on real IBM Quantum hardware. The resulting state is measured to reconstruct the histogram, enabling approximate recovery of block intensities and full image reassembly. The method maintains a constant qubit requirement based solely on the number of histogram bins, independent of the resolution of the image. By adjusting B, users can control the trade off between fidelity and resource usage. Empirical results demonstrate high quality reconstructions using as few as 5 to 7 qubits, significantly outperforming conventional pixel level encodings in terms of qubit efficiency and validating the practical application of the method for current NISQ era quantum systems.

翻译：本研究提出了一种紧凑且硬件高效的彩色图像压缩方法，适用于近期量子设备。该方法将图像分割为固定大小的块（称为"bixel"），并计算每个块的总强度。随后基于这些块强度构建具有B个区间的全局直方图，将区间计数的归一化平方根编码为幅度嵌入到n量子比特的量子态中。幅度嵌入通过PennyLane实现并在真实的IBM Quantum硬件上执行。通过测量最终量子态重建直方图，实现块强度的近似恢复和完整图像的重组。该方法仅依赖直方图区间数量维持恒定的量子比特需求，与图像分辨率无关。通过调整B值，用户可在保真度与资源消耗之间进行权衡。实验结果表明，仅使用5至7个量子比特即可获得高质量重建图像，在量子比特效率方面显著优于传统像素级编码方法，验证了该方法在当前NISQ时代量子系统中的实际应用价值。