用量子神经核分类卫星图像中的太阳能板，性能媲美顶尖经典方法。
Satellite image classification with neural quantum kernels
- 基于训练好的量子神经网络生成量子核，实现图像分类。
- 在8个量子比特下仍保持稳定表现，结果与最优经典方法相当。
- 适合关注量子机器学习在遥感应用中潜力的研究者。
尽管量子机器学习在理论上取得显著进展，但在实际应用场景中仍面临挑战。本文提出一种新方法，用于分类包含太阳能板的卫星图像，该任务对地球观测产业具有重要意义。首先通过经典预处理降低卫星图像数据集的维度，随后采用由训练过的量子神经网络导出的神经量子核（NQKs）进行分类。在该框架内评估多种策略，结果表明性能可与最佳经典方法竞争。关键发现包括结果的鲁棒性与可扩展性，在最多8个量子比特下均表现良好。
原文摘要 · Abstract (English)
Achieving practical applications of quantum machine learning for real-world scenarios remains challenging despite significant theoretical progress. This paper proposes a novel approach for classifying satellite images, a task of particular relevance to the earth observation (EO) industry, using quantum machine learning techniques. Specifically, we focus on classifying images that contain solar panels, addressing a complex real-world classification problem. Our approach begins with classical pre-processing to reduce the dimensionality of the satellite image dataset. We then apply neural quantum kernels (NQKs)-quantum kernels derived from trained quantum neural networks (QNNs)-for classification. We evaluate several strategies within this framework, demonstrating results that are competitive with the best classical methods. Key findings include the robustness of or results and their scalability, with successful performance achieved up to 8 qubits.
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