用量子退火优化医学图像特征选择，提升小规模数据集处理效率。
Quantum Annealing Feature Selection on Light-weight Medical Image Datasets
- 结合线性伊辛惩罚与子采样阈值法，提升量子退火可扩展性。
- 在小规模医学图像重建任务中成功识别像素掩码，验证有效性。
- 适合对高维优化问题感兴趣的科研人员，但受限于当前硬件能力。
我们研究了在真实量子硬件上使用量子计算算法解决轻量级医学图像数据集的特征选择这一计算密集型任务。特征选择常被建模为k选n问题，其复杂度随k和n增长呈二项式上升，经典方法在问题规模增大时难以高效扩展。量子退火器对此类问题具有潜在优势。本文提出一种新方法，在商用量子退火器上实现了比以往更大的特征选择实例求解。该方法结合线性伊辛惩罚机制、子采样与阈值技术以增强可扩展性。实验在简化玩具问题中进行，目标是从像素掩码重构小规模医学图像。结果表明，基于量子退火的特征选择在此简化场景下有效，展现出在高维优化任务中的潜力。然而，考虑到当前量子计算硬件的限制，其在更广泛实际问题中的适用性仍不明确。
原文摘要 · Abstract (English)
We investigate the use of quantum computing algorithms on real quantum hardware to tackle the computationally intensive task of feature selection for light-weight medical image datasets. Feature selection is often formulated as a k of n selection problem, where the complexity grows binomially with increasing k and n. As problem sizes grow, classical approaches struggle to scale efficiently. Quantum computers, particularly quantum annealers, are well-suited for such problems, offering potential advantages in specific formulations. We present a method to solve larger feature selection instances than previously presented on commercial quantum annealers. Our approach combines a linear Ising penalty mechanism with subsampling and thresholding techniques to enhance scalability. The method is tested in a toy problem where feature selection identifies pixel masks used to reconstruct small-scale medical images. The results indicate that quantum annealing-based feature selection is effective for this simplified use case, demonstrating its potential in high-dimensional optimization tasks. However, its applicability to broader, real-world problems remains uncertain, given the current limitations of quantum computing hardware.
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