量子特征提取提升卫星图像分类准确率
Quantum-enhanced satellite image classification
- 利用自旋哈密顿量生成量子特征，与经典模型结合
- 在ResNet50基础上实现87%准确率，比经典方法高3%
- 可在实际量子处理器上稳定运行，适合遥感等场景
我们展示了量子特征提取方法在空间应用多类图像分类中的应用。通过利用多体自旋哈密顿量的动力学特性，该方法生成具有表达力的量子特征，与经典处理结合后可实现量子增强的分类精度。以强而稳健的ResNet50为基线，经典方法最高达到83%准确率，迁移学习可提升至84%。相比之下，采用我们的量子-经典方法，准确率提升至87%，显著且可复现地优于现有经典方法。该方法在IBM多个量子处理器上实现，混合量子-经典框架在绝对准确率上保持2-3%的一致提升。结果表明，当前及近中期量子处理器在卫星成像、遥感等高风险数据驱动领域具有实际潜力，并可能推广至其他现实机器学习任务。
原文摘要 · Abstract (English)
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.
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