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<p>Quantum machine learning (QML) has gained increasing attention as a potential framework to address certain data analysis challenges in the future. Earth observation (EO) has entered the era of Big Data, where increasingly sophisticated deep learning models often require substantial computational resources for EO data analysis. Motivated by this, we explore the potential of hybrid quantum-classical learning for EO data classification despite the current limitations of quantum devices. This paper presents a hybrid model that incorporates multitask learning to assist efficient data encoding and employs a location weight module together with quantum convolution operations to extract valid features for classification. The validity of our proposed model was evaluated using multiple simplified EO benchmark settings as a proof-of-concept. Additionally, we experimentally investigated the robustness of the proposed model under reduced-sample and class-imbalanced conditions, providing insights into the behavior of hybrid QML models for EO data analysis.</p>

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Multitask learning for earth observation data classification with hybrid quantum network

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月26日

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  • Fan Fan,
  • Yilei Shi,
  • Tobias Guggemos,
  • Xiao Xiang Zhu

摘要

Quantum machine learning (QML) has gained increasing attention as a potential framework to address certain data analysis challenges in the future. Earth observation (EO) has entered the era of Big Data, where increasingly sophisticated deep learning models often require substantial computational resources for EO data analysis. Motivated by this, we explore the potential of hybrid quantum-classical learning for EO data classification despite the current limitations of quantum devices. This paper presents a hybrid model that incorporates multitask learning to assist efficient data encoding and employs a location weight module together with quantum convolution operations to extract valid features for classification. The validity of our proposed model was evaluated using multiple simplified EO benchmark settings as a proof-of-concept. Additionally, we experimentally investigated the robustness of the proposed model under reduced-sample and class-imbalanced conditions, providing insights into the behavior of hybrid QML models for EO data analysis.