arXiv subjects

Tommaso Albrigi

Publications and source records attributed to Tommaso Albrigi.

4 recordsLinked to original sources

arXiv2026

Hydration Monitoring Using Urinary Biomarkers: A Hybrid Classical Quantum Predictive Modeling Framework

Hydration status is a key physiological indicator associated with cellular homeostasis, renal function, and overall health. Recent advances in smart sensing environments enable passive monitoring of urinary biomarkers that can provide continuous insight into hydration dynamics. In this work, we investigate predictive modeling approaches for hydration monitoring using biomarker data collected through the Predict Health Toilet (PHT) system. The problem is formulated as a regression task using urinary indicators such as urine specific gravity, conductivity, and volume. We evaluate classical machine learning models and quantum machine learning architectures based on variational quantum circuits. In particular, we introduce a modular Quantum Sequential Model (QSM) designed to construct flexible hybrid quantum classical predictive pipelines. Experimental results compare classical regression models, symmetry-constrained quantum regressors, and QSM architectures. The results provide insights into the potential role of quantum machine learning in digital health monitoring systems and highlight the opportunities and current limitations of near-term quantum computing for physiological data analysis.

quant-ph↗

arXiv2026

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

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.

quant-ph↗

arXiv2025

Phonon interference effects in GaAs-GaP superlattice nanowires

Fine-tuning the functional properties of nanomaterials is crucial for technological applications. Superlattices, characterized by periodic repetitions of two or more materials in different dimensions, have emerged as a promising area of investigation. We present a study of the phonon interference effect on thermal transport in GaAs-GaP superlattice nanowires with sharp interfaces between the GaAs and GaP layers, as confirmed by high-resolution transmission electron microscopy. We performed thermal conductivity measurements using the so-called thermal bridge method on superlattice nanowires with a period varying from 4.8 to 23.3 nm. The measurements showed a minimum of the thermal conductivity as a function of superlattice period up to room temperature, that we interpreted as an indication of the crossover from coherent to incoherent thermal transport. Notably, this effect is not destroyed by surface boundary or by phonon-phonon scattering, as the crossover trend is also observed at room temperature. Our results were corroborated by both ab initio lattice dynamics and semiclassical nonequilibrium molecular dynamics calculations. These findings provide insights into the wave-like and particle-like transport of phonons in superlattice nanowires and demonstrate the potential for engineering thermal properties through precise control of the superlattice structure.

cond-mat.mes-hall↗

arXiv2023

GaAs/GaP Superlattice Nanowires for Tailoring Phononic Properties at the Nanoscale: Implications for Thermal Engineering

The possibility to tune the functional properties of nanomaterials is key to their technological applications. Superlattices, i.e., periodic repetitions of two or more materials in different dimensions are being explored for their potential as materials with tailor-made properties. Meanwhile, nanowires offer a myriad of possibilities to engineer systems at the nanoscale, as well as to combine materials which cannot be put together in conventional heterostructures due to the lattice mismatch. In this work, we investigate GaAs/GaP superlattices embedded in GaP nanowires and demonstrate the tunability of their phononic and optoelectronic properties by inelastic light scattering experiments corroborated by ab initio calculations. We observe clear modifications in the dispersion relation for both acoustic and optical phonons in the superlattices nanowires. We find that by controlling the superlattice periodicity we can achieve tunability of the phonon frequencies. We also performed wavelength-dependent Raman microscopy on GaAs/GaP superlattice nanowires and our results indicate a reduction in the electronic bandgap in the superlattice compared to the bulk counterpart. All our experimental results are rationalized with the help of ab initio density functional perturbation theory (DFPT) calculations. This work sheds fresh insights into how material engineering at the nanoscale can tailor phonon dispersion and open pathways for thermal engineering.

cond-mat.mtrl-sci↗