用量子算法提升艾滋病聚集区检测与预测精度
Quantum Approximate Optimization Algorithm for Spatiotemporal Forecasting of HIV Clusters
- 结合量子近似优化算法（QAOA）与混合量子-经典神经网络
- 集群检测准确率达92%，预测准确率94%，耗时仅1.6秒
- 发现住房不稳是关键驱动因素，适合公共卫生决策者参考
HIV流行病学数据日益复杂，亟需先进计算支持精准的聚集区检测与预测。本研究利用量子加速机器学习，基于2022年AIDSVu和合成社会决定健康因素（SDoH）数据，在邮政编码层级分析了HIV患病率。方法对比了经典聚类（DBSCAN、HDBSCAN）与量子近似优化算法（QAOA），构建了混合量子-经典神经网络用于HIV患病率预测，并采用量子贝叶斯网络探索SDoH因素与HIV发病率之间的因果关系。结果显示，基于QAOA的方法在1.6秒内实现92%的集群检测准确率，优于经典算法；混合量子-经典神经网络预测准确率达94%，超过纯经典模型。量子贝叶斯分析识别出住房不稳是艾滋病聚集区形成与扩展的关键驱动因素，而污名化的影响具有地理异质性。这些量子增强方法显著提升了HIV监测的精度与效率，揭示了重要因果路径，可为靶向干预、优化暴露前预防（PrEP）资源分配及应对结构性不平等提供支持。
原文摘要 · Abstract (English)
HIV epidemiological data is increasingly complex, requiring advanced computation for accurate cluster detection and forecasting. We employed quantum-accelerated machine learning to analyze HIV prevalence at the ZIP-code level using AIDSVu and synthetic SDoH data for 2022. Our approach compared classical clustering (DBSCAN, HDBSCAN) with a quantum approximate optimization algorithm (QAOA), developed a hybrid quantum-classical neural network for HIV prevalence forecasting, and used quantum Bayesian networks to explore causal links between SDoH factors and HIV incidence. The QAOA-based method achieved 92% accuracy in cluster detection within 1.6 seconds, outperforming classical algorithms. Meanwhile, the hybrid quantum-classical neural network predicted HIV prevalence with 94% accuracy, surpassing a purely classical counterpart. Quantum Bayesian analysis identified housing instability as a key driver of HIV cluster emergence and expansion, with stigma exerting a geographically variable influence. These quantum-enhanced methods deliver greater precision and efficiency in HIV surveillance while illuminating critical causal pathways. This work can guide targeted interventions, optimize resource allocation for PrEP, and address structural inequities fueling HIV transmission.
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