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Quantum Information Processing

Methane emissions forecasting using hybrid quantum–classical deep learning models: case study of North Africa

  • 01-11-2025
Published in:
Authors
Widad Hassina Belkadi
Yassine Drias
Habiba Drias
Sarah Ferkous
Maroua Khemissi
Published in
Quantum Information Processing | Issue 11/2025

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Abstract

This article delves into the application of hybrid quantum-classical deep learning models for forecasting methane emissions in North Africa, a region significantly impacted by climate change. The study utilizes Sentinel-5P satellite data to develop and evaluate quantum models such as QLSTM, QGRU, QCNN-LSTM, and QCNN-GRU. The research highlights the superior performance of QCNN-GRU and QCNN-LSTM models, which achieve a 6% improvement in RMSE compared to classical methods. The article also explores the challenges and potential of quantum computing in environmental monitoring, providing insights into the future of climate change mitigation strategies. Additionally, the study compares the performance of quantum models against classical counterparts, demonstrating the advantages of quantum-enhanced techniques in terms of accuracy and computational efficiency.
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previous article Revocation and reconstruction of shared quantum states
next article Construction and optimization of quantum modular exponentiation circuits based on the V gate
Title
Methane emissions forecasting using hybrid quantum–classical deep learning models: case study of North Africa
Authors
Widad Hassina Belkadi
Yassine Drias
Habiba Drias
Sarah Ferkous
Maroua Khemissi
Publication date
01-11-2025
Publisher
Springer US
Published in
Quantum Information Processing / Issue 11/2025
Print ISSN: 1570-0755
Electronic ISSN: 1573-1332
DOI
https://doi.org/10.1007/s11128-025-04979-0
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