Quantum Approximate Optimization Algorithm for Spatiotemporal Forecasting of HIV Clusters

TL;DR

This paper introduces quantum-enhanced machine learning methods for more accurate and efficient HIV cluster detection and prevalence forecasting, revealing key social determinants influencing HIV spread.

Contribution

It develops novel quantum algorithms for clustering, forecasting, and causal analysis of HIV data, outperforming classical methods in accuracy and speed.

Findings

QAOA achieved 92% accuracy in cluster detection within 1.6 seconds.

Hybrid quantum-classical neural network predicted HIV prevalence with 94% accuracy.

Quantum Bayesian networks identified housing instability as a key driver of HIV clusters.

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.