Sleep-Disordered Breathing Detection with Quantum Recurrent Neural Networks
Abstract
This work investigates the application of Quantum Recurrent Neural Networks to the binary classification of sleep-disordered breathing events in time-series data, aiming to distinguish between apneic and normal breathing episodes. A variational classical--quantum architecture is proposed and evaluated against classical Recurrent Neural Network models. Experiments were conducted on multiple real-world datasets with varying levels of complexity to assess the strengths and limitations of the quantum approach. In the comparative analysis, both classical and quantum models showed comparable performance. The quantum model achieved superior performance on the smallest dataset, while yielding slightly weaker results in the most complex setting. However, the quantum approach exhibited notable strengths, such as competitive performance despite a drastically reduced parameter count and low-qubit regime, as well as good generalization capabilities. These results suggest that, despite current hardware and algorithmic limitations, variational quantum recurrent models represent a promising alternative for time-series classification as Quantum Computing technologies continue to mature.
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BibTeX
@misc{mayorgaredondo2026sleepdisordered,
title = {{Sleep-Disordered Breathing Detection with Quantum Recurrent Neural Networks}},
author = {Alejandro Mayorga-Redondo and José Daniel Viqueira and Diego Alvarez-Estevez and Eduardo Mosqueira-Rey and Andrés Gómez},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.04818},
eprint = {2611.04818},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.04818},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Sleep-Disordered Breathing Detection with Quantum Recurrent Neural Networks
AU - Alejandro Mayorga-Redondo
AU - José Daniel Viqueira
AU - Diego Alvarez-Estevez
AU - Eduardo Mosqueira-Rey
AU - Andrés Gómez
PY - 2026
DA - 2026/11/03
PB - arXiv
DO - 10.48550/arxiv.2611.04818
UR - https://arxiv.org/abs/2611.04818
AB - This work investigates the application of Quantum Recurrent Neural Networks to the binary classification of sleep-disordered breathing events in time-series data, aiming to distinguish between apneic and normal breathing episodes. A variational classical--quantum architecture is proposed and evaluated against classical Recurrent Neural Network models. Experiments were conducted on multiple real-world datasets with varying levels of complexity to assess the strengths and limitations of the quantum approach. In the comparative analysis, both classical and quantum models showed comparable performance. The quantum model achieved superior performance on the smallest dataset, while yielding slightly weaker results in the most complex setting. However, the quantum approach exhibited notable strengths, such as competitive performance despite a drastically reduced parameter count and low-qubit regime, as well as good generalization capabilities. These results suggest that, despite current hardware and algorithmic limitations, variational quantum recurrent models represent a promising alternative for time-series classification as Quantum Computing technologies continue to mature.
ER -