量子神经网络提升小样本医学数据因果推断精度
Quantum Neural Networks for Propensity Score Estimation and Survival Analysis in Observational Biomedical Studies
- 用量子神经网络编码患者特征，结合无梯度优化应对硬件噪声
- 在小样本下AUC达0.750，优于传统模型，且噪声模拟提升稳定性
- 适合处理高维小样本的医学研究，尤其关注因果分析
本研究探讨量子神经网络（QNN）在1177名结直肠癌患者队列中用于倾向评分估计，以缓解腹腔镜与开放手术生存结局比较中的选择偏差。数据集包含77个变量，重点建模年龄、性别、分期和体重指数四类关键协变量。采用线性ZFeatureMap编码、SummedPaulis算子预测，并用CMA-ES实现抗噪优化。引入方差正则化抑制测量噪声，在精确、1024次采样及假曼哈顿（FakeManhattanV2）硬件噪声条件下进行模拟。在小样本（n=100）下，QNN AUC最高达0.750，优于经典逻辑回归与梯度提升机；噪声建模显著增强预测稳定性。通过遗传匹配与匹配权重实现协变量平衡（标准化均值差分别为0.0849和0.0869）。经Kaplan-Meier、Cox比例风险及Aalen加法回归分析，调整后无显著生存差异（p值0.287–0.851），表明原始结果受混杂偏倚影响。结果表明，结合CMA-ES与噪声感知策略的QNN在小样本高维医学数据中具潜在优势。
原文摘要 · Abstract (English)
This study investigates the application of quantum neural networks (QNNs) for propensity score estimation to address selection bias in comparing survival outcomes between laparoscopic and open surgical techniques in a cohort of 1177 colorectal carcinoma patients treated at University Hospital Ostrava (2001-2009). Using a dataset with 77 variables, including patient demographics and tumor characteristics, we developed QNN-based propensity score models focusing on four key covariates (Age, Sex, Stage, BMI). The QNN architecture employed a linear ZFeatureMap for data encoding, a SummedPaulis operator for predictions, and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for robust, gradient-free optimization in noisy quantum environments. Variance regularization was integrated to mitigate quantum measurement noise, with simulations conducted under exact, sampling (1024 shots), and noisy hardware (FakeManhattanV2) conditions. QNNs, particularly with simulated hardware noise, outperformed classical logistic regression and gradient boosted machines in small samples (AUC up to 0.750 for n=100), with noise modeling enhancing predictive stability. Propensity score matching and weighting, optimized via genetic matching and matching weights, achieved covariate balance with standardized mean differences of 0.0849 and 0.0869, respectively. Survival analyses using Kaplan-Meier estimation, Cox proportional hazards, and Aalen additive regression revealed no significant survival differences post-adjustment (p-values 0.287-0.851), indicating confounding bias in unadjusted outcomes. These results highlight QNNs' potential, enhanced by CMA-ES and noise-aware strategies, to improve causal inference in biomedical research, particularly for small-sample, high-dimensional datasets.
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