Classical and Quantum Machine Learning for Population-Level Prediction of Heat-Related Physiological Events

Quantum Physics [Submitted on 16 Apr 2026] Classical and Quantum Machine Learning for Population-Level Prediction of Heat-Related Physiological Events Saul Gonzalez-Bermejo, Tommaso Albrigi, Borja Vazquez-Morado, Urko Regueiro-Ramos, Daniel Casado-Faulı, Sergi Consul-Pacareu, Parfait Atchade-Adelomou Predicting heat-related physiological events at the population level is challenging due to the complex interactions among climatic, demographic, and socioeconomic factors, as well as the strong sparsity and seasonality of observational data. In this work, we propose a unified predictive framework that integrates heterogeneous environmental and public-health datasets and evaluates two learning paradigms within a common pipeline: classical machine learning and quantum machine learning. The methodology combines data harmonization, temporal aggregation, feature engineering, and dimensionality reduction to construct a weekly county-level population dataset. On this unified representation, we train both a classical regression baseline and a variational quantum model based on parameterized quantum circuits with angle embedding and data re-uploading. Experimental evaluation on datasets from the United States and Catalonia shows that classical models currently achieve higher predictive accuracy, particularly under conditions of strong class imbalance and sparse targets. Nevertheless, the quantum models demonstrate non-trivial learning capability and capture meaningful predictive structure in several scenarios. These results provide an empirical comparison between classical and quantum learning approaches for population-level physiological prediction and establish a methodological foundation for future hybrid health modeling as quantum hardware continues to evolve. Comments: 9 pages 1 figure Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2604.15382 [quant-ph]   (or arXiv:2604.15382v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2604.15382 Focus to learn more Submission history From: Parfait Atchade [view email] [v1] Thu, 16 Apr 2026 01:32:05 UTC (174 KB) Access Paper: HTML (experimental) view license Current browse context: quant-ph < prev   |   next > new | recent | 2026-04 References & Citations INSPIRE HEP NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)