Quantum Approximate Optimization Algorithm for Spatiotemporal Forecasting of HIV Clusters
Journal:
arXiv
Published Date:
Jul 1, 2025
Abstract
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.
