arxiv: 2604.15382 · v1 · submitted 2026-04-16 · 🪐 quant-ph

Recognition: unknown

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-Faul{\i} , Sergi Consul-Pacareu , Parfait Atchade-Adelomou

Authors on Pith no claims yet

Pith reviewed 2026-05-10 12:00 UTC · model grok-4.3

classification 🪐 quant-ph

keywords quantum machine learningclassical machine learningheat-related health predictionpopulation-level forecastingvariational quantum circuitspublic health dataclass imbalance

0 comments

The pith

Classical machine learning models currently outperform variational quantum circuits when predicting population-level heat-related physiological events from sparse data.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper builds a single data pipeline that harmonizes climate, demographic, and health records into weekly county-level features, then trains both classical regression models and variational quantum circuits with angle embedding on the same representation. Classical models deliver higher accuracy, especially when target events are rare and imbalanced, while the quantum models still extract genuine predictive patterns rather than random guessing. This comparison matters because heat-related health burdens are rising and population forecasts could inform public-health decisions; the work supplies an empirical baseline on real datasets from the United States and Catalonia that future hybrid systems can be measured against.

Core claim

In a unified framework that converts heterogeneous environmental and public-health data into a common weekly county-level representation, classical regression achieves higher predictive accuracy than parameterized quantum circuits with angle embedding and data re-uploading, particularly under strong class imbalance, yet the quantum models demonstrate non-trivial learning and capture meaningful structure in several tested scenarios.

What carries the argument

A unified predictive pipeline that performs data harmonization, temporal aggregation, feature engineering, and dimensionality reduction to produce a shared representation, then fits both classical regression and variational quantum circuits on identical inputs.

Load-bearing premise

The chosen feature engineering, temporal aggregation, and dimensionality reduction steps produce a representation that is equally suitable for both classical regression and variational quantum circuits without introducing bias that favors one paradigm.

What would settle it

Re-running the exact same pipeline on hardware with substantially lower noise or more qubits and measuring whether quantum-model accuracy exceeds the classical baseline on the held-out US and Catalonia test sets.

Figures

Figures reproduced from arXiv: 2604.15382 by Borja Vazquez-Morado, Daniel Casado-Faul{\i}, Parfait Atchade-Adelomou, Saul Gonzalez-Bermejo, Sergi Consul-Pacareu, Tommaso Albrigi, Urko Regueiro-Ramos.

**Figure 1.** Figure 1: Comparison between classical (blue) and quantum (orange) models across multiple regression tasks. Top panels show predicted versus [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗

read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This paper runs a direct classical-versus-quantum comparison on harmonized county-level heat-health data and finds classical regression ahead, while quantum circuits still pick up some signal.

read the letter

The main takeaway is that classical regression currently beats a variational quantum model with angle embedding and data re-uploading on the same pipeline for sparse, imbalanced heat-related event prediction. They harmonize US and Catalonia public datasets, aggregate to weekly county level, engineer features, reduce dimensions, and train both approaches side by side. Classical wins on accuracy, especially under strong imbalance, but the quantum side shows non-trivial learning in places. That direct head-to-head on this particular health forecasting task looks new relative to the cited literature, and the honest framing as a methodological baseline rather than a performance win is refreshing. The data work itself is careful and transparent about the challenges of seasonality and sparsity. The soft spots are mostly in the details that are missing from the summary. No numerical metrics, confidence intervals, or ablation results appear in the abstract, so it is hard to judge how much structure the quantum model actually captured versus simply avoiding total failure. The shared preprocessing steps could favor linear classical methods, and the paper does not test whether different feature choices would change the ordering. Class imbalance handling is noted as an issue but not described in enough depth to replicate or improve. No code or data release is mentioned, which would strengthen the baseline claim. This is for readers working on quantum machine learning applications to real public-health or climate data who want to see grounded comparisons instead of toy problems. It is not claiming quantum advantage, which makes the results more credible for discussion. I would bring it to a reading group to talk through the circuit design choices. It deserves peer review because the experimental setup is straightforward and the topic has clear relevance for hybrid modeling as hardware improves.

Referee Report

0 major / 3 minor

Summary. The manuscript proposes a unified predictive framework that harmonizes heterogeneous environmental, demographic, and public-health datasets to construct a weekly county-level dataset for heat-related physiological events. Within this common pipeline of temporal aggregation, feature engineering, and dimensionality reduction, it trains and compares a classical regression baseline against a variational quantum model using parameterized quantum circuits with angle embedding and data re-uploading. Evaluation on public datasets from the United States and Catalonia shows classical models achieving higher predictive accuracy, especially under strong class imbalance and sparse targets, while quantum models exhibit non-trivial learning and capture meaningful structure in several scenarios.

Significance. If the empirical comparison holds, the work is significant for establishing a methodological baseline and foundation for future hybrid classical-quantum health modeling as quantum hardware improves. Strengths include the use of external public datasets (avoiding circularity), the explicit positioning as a baseline rather than a performance claim, and the qualified language around quantum results.

minor comments (3)

Abstract: the performance claims would be more informative if key quantitative metrics (e.g., accuracy, AUC, or F1 scores with confidence intervals) were included to support the stated ordering and non-trivial quantum learning.
Methodology section: the description of how class imbalance was handled during training (weighting, sampling, or loss modification) is not detailed, which affects interpretability of the reported classical advantage under sparse targets.
Results section: ablation or sensitivity analysis on the shared preprocessing steps (feature engineering and dimensionality reduction) would clarify whether the representation is equally suitable for both paradigms.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the constructive summary of our work and for recommending minor revision. We are pleased that the positioning of the manuscript as an empirical baseline comparison—rather than a performance claim—is recognized, along with the use of public external datasets and the qualified language around the quantum results. As the report does not enumerate any specific major comments, we have no individual points to address in detail at this time.

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper is an empirical comparison of classical regression and variational quantum circuits on harmonized county-level datasets from the US and Catalonia. The methodology consists of standard data harmonization, aggregation, feature engineering, dimensionality reduction, and parallel model training, with results reported as observed accuracies under class imbalance. No equations or derivations are presented that reduce a claimed prediction to a fitted parameter or self-defined quantity by construction. No load-bearing self-citations, uniqueness theorems, or ansatzes imported from prior author work are invoked to justify the central claims. The work positions itself as a methodological baseline rather than a theoretical derivation, making the reported findings independent of any internal circular reduction.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The central claim rests on the premise that the harmonized weekly county-level representation preserves sufficient signal for both model families and that standard variational quantum circuit training can be applied without additional domain-specific constraints.

free parameters (2)

quantum circuit depth and qubit count
Number of layers and qubits in the parameterized circuit chosen to fit the dimensionality-reduced feature space.
classical regression hyperparameters
Regularization strength and feature-selection thresholds selected during model training.

axioms (2)

domain assumption Harmonization and dimensionality reduction preserve all predictive information relevant to both classical and quantum learners.
Invoked in the construction of the unified weekly county-level dataset.
standard math Variational quantum circuits with angle embedding and data re-uploading can be trained on the resulting feature vectors using standard optimizers.
Assumed when stating that the quantum models demonstrate learning capability.

pith-pipeline@v0.9.0 · 5527 in / 1339 out tokens · 44945 ms · 2026-05-10T12:00:15.553984+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

24 extracted references · 3 canonical work pages · 1 internal anchor

[1]

Ebi, Shakoor Hajat, Jason M

Kristie L. Ebi, Shakoor Hajat, Jason M. Hess, et al. Hot weather and heat extremes: health risks.The Lancet, 398(10301):698– 708, 2021

2021
[2]

Siddiqui et al

Syed A. Siddiqui et al. A systematic review and meta-analysis of the impact of heat exposure on morbidity and mortality associated with non-communicable diseases in low- and middle-income countries.Environmental Research, 278:121319, 2025

2025
[3]

LightGBM: A highly efficient gradient boosting decision tree

Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. LightGBM: A highly efficient gradient boosting decision tree. InAdvances in Neural Information Processing Systems 30 (NeurIPS 2017), 2017

2017
[4]

PennyLane: Automatic differentiation of hybrid quantum-classical computations

Ville Bergholm, Josh Izaac, Maria Schuld, Christian Gogolin, Shahnawaz Ahmed, et al. Pennylane: Automatic differentia- tion of hybrid quantum-classical computations.arXiv preprint arXiv:1811.04968, 2022

work page internal anchor Pith review arXiv 2022
[5]

Quantum machine learning in feature hilbert spaces.Physical Review Letters, 122(4):040504, 2019

Maria Schuld and Nathan Killoran. Quantum machine learning in feature hilbert spaces.Physical Review Letters, 122(4):040504, 2019

2019
[6]

Adri ´an P ´erez-Salinas, Alba Cervera-Lierta, Elies Gil-Fuster, and Jos´e I. Latorre. Data re-uploading for a universal quantum classifier.Quantum, 4:226, 2020

2020
[7]

Antonio Gasparrini, Ben Armstrong, and Michael G. Kenward. Distributed lag non-linear models.Statistics in Medicine, 29(21):2224–2234, 2010

2010
[8]

Time series regression studies in environmental epidemiology.International Journal of Epidemi- ology, 42(4):1187–1195, 2013

Krishnan Bhaskaran, Antonio Gasparrini, Shakoor Hajat, Liam Smeeth, and Ben Armstrong. Time series regression studies in environmental epidemiology.International Journal of Epidemi- ology, 42(4):1187–1195, 2013

2013
[9]

Association between heat exposure and hos- pitalization for hyperglycemic emergencies in japan: a nationwide study.Environmental Health Perspectives, 130(9):097008, 2022

Kei Miyamura et al. Association between heat exposure and hos- pitalization for hyperglycemic emergencies in japan: a nationwide study.Environmental Health Perspectives, 130(9):097008, 2022

2022
[10]

Temperature-related hospitalization burden under climate change.Nature, 2025

Shanshan Liao et al. Temperature-related hospitalization burden under climate change.Nature, 2025

2025
[11]

Hansen et al

K. Hansen et al. The spatial distribution of heat related hospital- izations and local vulnerability across a wide area.Environmental Research, 2024

2024
[12]

Clark et al

A. Clark et al. Identifying groups at-risk to extreme heat: Inter- sections of social vulnerability and heat-related health impacts. Environmental Research, 2024

2024
[13]

Fourier se- ries weight in quantum machine learning.arXiv preprint arXiv:2302.00105, 2023

Parfait Atchade-Adelomou and Kent Larson. Fourier se- ries weight in quantum machine learning.arXiv preprint arXiv:2302.00105, 2023

work page arXiv 2023
[14]

The effect of data encoding on the expressive power of varia- tional quantum machine learning models.Physical Review A, 103(3):032430, 2021

Maria Schuld, Ryan Sweke, and Johannes Jakob Meyer. The effect of data encoding on the expressive power of varia- tional quantum machine learning models.Physical Review A, 103(3):032430, 2021

2021
[15]

Benjamin, Vedran Dun- jko, and Maria Schuld

Sofiene Jerbi, Casper Gyurik, Simon C. Benjamin, Vedran Dun- jko, and Maria Schuld. Quantum machine learning beyond kernel methods.Nature Communications, 14:517, 2023

2023
[16]

McClean, Sergio Boixo, Vadim N

Jarrod R. McClean, Sergio Boixo, Vadim N. Smelyanskiy, Ryan Babbush, and Hartmut Neven. Barren plateaus in quantum neural network training landscapes.Nature Communications, 9:4812, 2018

2018
[17]

Cerezo, Kunal Sharma, An- drew Sornborger, Patrick J

Samson Wang, Enrico Fontana, M. Cerezo, Kunal Sharma, An- drew Sornborger, Patrick J. Coles, and Lakshminarayan Subra- manian. Noise-induced barren plateaus in variational quantum algorithms.Nature Communications, 12:6961, 2021

2021
[18]

Is quantum advantage the right goal for quantum machine learning?PRX Quantum, 3(3):030101, 2022

Maria Schuld, Nathan Killoran, et al. Is quantum advantage the right goal for quantum machine learning?PRX Quantum, 3(3):030101, 2022

2022
[19]

Bowles, S

Rocco Ghigliazza, Giuseppe Carleo, et al. Better than classi- cal? the subtle art of benchmarking quantum machine learning models.arXiv preprint arXiv:2403.07059, 2024

work page arXiv 2024
[20]

Vaidyanathan, A

A. Vaidyanathan, A. Gates, C. Brown, E. Prezzato, and A. Bern- stein. Heat-related emergency department visits.CDC Morbidity and Mortality Weekly Report, pages 324–329, 2024

2024
[21]

Consul-Pacareu, R

S. Consul-Pacareu, R. Monta ˜no, Kevin Rodriguez-Fernandez, `Alex Corretg´e, Esteve Vilella-Moreno, Daniel Casado-Faul ´ı, and Parfait Atchade-Adelomou. Quantum machine learning hyperpa- rameter search, 2023

2023
[22]

Jolliffe.Principal Component Analysis

Ian T. Jolliffe.Principal Component Analysis. Springer, New York, 2nd edition, 2002

2002
[23]

Unique: A general- purpose platform for benchmarking classical and quantum ma- chine learning algorithms.TechRxiv, 2025

Daniel Casado-Faul ´ı, Sa´ul Gonz´alez-Bermejo, Marc Rovira, Alba Mei, Laia Coronas Sala, Sergi C `onsul-Pacareu, Esteve Vilella, Jordi Alb ´o-Canals, Parfait Atchade-Adelomou, Xavier Vilasis- Cardona, and Elisabet Golobardes Ribe. Unique: A general- purpose platform for benchmarking classical and quantum ma- chine learning algorithms.TechRxiv, 2025

2025
[24]

Applying the unique platform to prototype a solution for the wesharecare use case.Authorea Preprints, 2025

Daniel Casado-Faul ´ı, Saul Gonzalez-Bermejo, Marc Rovira, Alba Mei, Laia Coronas Sala, Sergi Consul-Pacareu, Esteve Vilella, Jordi Albo-Canals, and Aarfait Atchade-Adelomou. Applying the unique platform to prototype a solution for the wesharecare use case.Authorea Preprints, 2025

2025