用直方图编码图像，5-7个量子比特即可高效压缩彩色图片。
Histogram Driven Amplitude Embedding for Qubit Efficient Quantum Image Compression
- 将图像分块统计亮度，用直方图频次平方根编码为量子态幅度。
- 仅需5-7个量子比特就实现高质量重建，远优于传统像素编码方法。
- 适合资源受限的当前量子设备，特别适用于图像压缩场景。
本文提出一种紧凑且硬件友好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（bixels），计算每块总亮度，基于这些亮度构建包含B个区间的全局直方图，并将各区间计数的归一化平方根作为振幅编码至n量子比特量子态中。通过PennyLane实现幅度嵌入，并在真实的IBM量子硬件上执行。测量后可重构直方图，从而近似恢复块亮度并完整重建图像。该方法的量子比特数量恒定，仅取决于直方图区间的数量，与图像分辨率无关。通过调节B值，用户可在保真度与资源消耗间灵活权衡。实验表明，仅使用5至7个量子比特即可实现高质量重建，显著优于传统像素级编码在量子比特效率方面的表现，验证了该方法在当前NISQ时代量子系统中的实际应用潜力。
原文摘要 · Abstract (English)
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.
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