量子混合模型提升遥感数据分类效率与精度
Multitask Learning for Earth Observation Data Classification with Hybrid Quantum Network
- 融合多任务学习与量子卷积，优化遥感数据编码和特征提取
- 在多个遥感基准上实现高效分类，验证了量子优势潜力
- 适合关注量子机器学习与遥感分析交叉应用的研究者
量子机器学习（QML）作为应对未来计算需求的潜在方案受到越来越多关注。地球观测（EO）已进入大数据时代，利用复杂深度学习模型高效分析大规模遥感数据的计算需求已成为瓶颈。为此，本文提出一种混合模型，结合多任务学习以提升数据编码效率，并引入带有位置权重的量子卷积模块提取有效特征用于分类。模型在多个地球观测基准数据集上进行了验证。此外，我们实验评估了模型的泛化能力，并探究了其性能优势的关键因素，凸显了量子机器学习在遥感数据分析中的潜力。
原文摘要 · Abstract (English)
Quantum machine learning (QML) has gained increasing attention as a potential solution to address the challenges of computation requirements in the future. Earth observation (EO) has entered the era of Big Data, and the computational demands for effectively analyzing large EO data with complex deep learning models have become a bottleneck. Motivated by this, we aim to leverage quantum computing for EO data classification and explore its advantages 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 with quantum convolution operations to extract valid features for classification. The validity of our proposed model was evaluated using multiple EO benchmarks. Additionally, we experimentally explored the generalizability of our model and investigated the factors contributing to its advantage, highlighting the potential of QML in EO data analysis.
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