发表机构
Istituto Nazionale di Geofisica e Vulcanologia (INGV); Thales Alenia Space Italia; Euro-Mediterranean Center on Climate Change; Università di Catania; INFN（国家地球物理与火山学研究所; 泰雷兹阿莱尼亚空间意大利公司; 欧亚地中海气候变化中心; 卡塔尼亚大学; 国家核物理研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究探讨混合量子卷积神经网络（QCNN）对含火山云的卫星图像分类的潜力，对比不同量子比特数QCNN与纯经典架构的性能，以解决火山云检测难题。
AI 中文摘要
量子计算的最新进展为地球观测（EO）数据分析开辟了新的可能性。量子机器学习（QML）方法通过利用叠加和纠缠等量子现象，提供了处理信息的新方式。这些能力促使人们探索量子增强模型是否能解决卫星遥感中长期存在的挑战，其中复杂的光谱和空间信号往往需要复杂的特征提取。在各种应用领域中，EO数据可对火山云进行全球监测，对航空安全、灾害评估、实时喷发响应以及火山对气候影响的评估至关重要。然而，由于火山云与气象云相似、喷发特征多变，以及静止轨道传感器的光谱采样较粗，准确检测火山云仍然困难。本研究探讨了混合量子卷积神经网络（QCNNs）在包含火山云的卫星图像分类中的潜力，这些架构将量子计算层集成到经典卷积框架中。考虑了两种QCNN变体（分别具有2和4个量子比特），以评估它们对SEVIRI图像数据集的分类能力，该数据集包括含火山云（由火山灰、SO₂或混合成分组成）的场景以及非火山背景。最后，将混合QCNN模型的性能与纯经典架构的性能进行了比较。
英文摘要
Recent advances in quantum computing are opening new possibilities for Earth Observation (EO) data analysis. Quantum machine learning (QML) approaches offer novel ways to process information by exploiting quantum phenomena such as superposition and entanglement. These capabilities have motivated the exploration of whether quantum-enhanced models can address long-standing challenges in satellite remote sensing, where complex spectral and spatial signals often require sophisticated feature extraction. Among various fields of application, EO data allow the global monitoring of volcanic clouds and are crucial for aviation safety, hazard assessment, real-time eruption response, and evaluation of volcanic impacts on climate. Yet accurate detection of volcanic clouds remains difficult due to their similarity with meteorological clouds, the variability of eruption signatures, and the coarse spectral sampling of geostationary sensors. In this work, the potential of hybrid quantum convolutional neural networks (QCNNs) for the classification of satellite images containing volcanic clouds was investigated. These architectures integrate quantum computational layers into a classical convolutional framework. Two QCNN variants (with 2 and 4 qubits) have been considered to evaluate their ability to classify a dataset of SEVIRI images, including scenes with volcanic clouds (composed of ash, $SO_2$, or mixed components) as well as non-volcanic backgrounds. Finally, the performance of the hybrid QCNN models was compared with that of purely classical architectures.
