Chronic obstructive pulmonary disease (COPD) affects hundreds of millions of people worldwide, and skeletal-muscle dysfunction is clinically important. Quantum machine learning is increasingly explored for biomedical prediction, but its value in small biomarker cohorts requires benchmarking against strong classical baselines. We analysed a cigarette-smoke COPD cohort of 213 animals with blood and bronchoalveolar-lavage biomarkers to predict tibialis anterior muscle weight, muscle quality, and force. We developed a kernel-geometric quantum hybrid method in which synthetic symmetric positive definite (SPD) references are mapped through a reproducing kernel Hilbert space, compressed using train-only random projection, normalised, and supplied to low-dimensional quantum regression circuits. We benchmarked this approach against classical ridge/kernel models, SPD relational representations, and quantum-kernel regression (QKR). All methods were evaluated using condition-stratified repeated cross-validation. The largest numerical improvement was observed for muscle weight, where the proposed method had the numerically lowest mean root mean squared error (RMSE), approximately 1.8% below the best classical comparator; paired fold-level testing did not establish statistically significant superiority after Holm adjustment, but the endpoint is biologically meaningful. The method also had the numerically lowest mean RMSE for muscle quality. For force, biomarker-only Ridge performed best, suggesting a more linear endpoint structure.

翻译：慢性阻塞性肺疾病（COPD）影响全球数亿人口，其骨骼肌功能障碍具有重要临床意义。量子机器学习正日益被探索用于生物医学预测，但其在小型生物标志物队列中的价值需通过与强经典基线方法的对比基准测试来验证。我们分析了包含213只动物的香烟烟雾诱导COPD队列，利用血液和支气管肺泡灌洗生物标志物来预测胫骨前肌重量、肌肉质量及肌力。我们提出了一种核-几何量子混合方法，该方法通过再生核希尔伯特空间映射合成对称正定（SPD）参考矩阵，采用仅基于训练集的随机投影进行压缩、归一化，并输入至低维量子回归电路。我们将该方法与经典岭回归/核模型、SPD关系表示及量子核回归（QKR）进行了基准对比。所有方法均采用条件分层重复交叉验证进行评估。在肌肉重量预测中观察到最大数值改进：所提方法的数值最低均方根误差（RMSE）较最佳经典对照方法低约1.8%；经Holm校正后的配对折间检验未证实统计显著性优势，但该终点具有生物学意义。该方法在肌肉质量预测中也获得数值最低的均方根误差。对于肌力预测，仅基于生物标志物的岭回归表现最优，提示该终点具有更线性的结构特征。