We demonstrate the application of a quantum feature extraction method to enhance multi-class image classification for space applications. By harnessing the dynamics of many-body spin Hamiltonians, the method generates expressive quantum features that, when combined with classical processing, lead to quantum-enhanced classification accuracy. Using a strong and well-established ResNet50 baseline, we achieved a maximum classical accuracy of 83%, which can be improved to 84% with a transfer learning approach. In contrast, applying our quantum-classical method the performance is increased to 87% accuracy, demonstrating a clear and reproducible improvement over robust classical approaches. Implemented on several of IBM's quantum processors, our hybrid quantum-classical approach delivers consistent gains of 2-3% in absolute accuracy. These results highlight the practical potential of current and near-term quantum processors in high-stakes, data-driven domains such as satellite imaging and remote sensing, while suggesting broader applicability in real-world machine learning tasks.

翻译：我们展示了一种量子特征提取方法在空间应用多类图像分类中的增强效果。通过利用多体自旋哈密顿量的动力学特性，该方法生成具有强表达能力的量子特征，结合经典处理后实现了量子增强的分类精度。基于强大且成熟的ResNet50基准模型，我们获得了83%的最高经典准确率，通过迁移学习方法可提升至84%。相比之下，应用我们的量子-经典混合方法可将性能提升至87%的准确率，这相较于稳健的经典方法展现出明确且可复现的改进。在IBM多款量子处理器上实现后，我们的混合量子-经典方法在绝对准确率上实现了2-3%的稳定提升。这些结果凸显了当前及近期量子处理器在卫星成像与遥感等高风险数据驱动领域的实用潜力，同时表明该方法在现实世界机器学习任务中具有更广泛的适用性。