Abstract
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.
Abstract (translated)
我们演示了量子特征提取方法在增强空间应用中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，该方法生成具有表达力的量子特征，当与经典处理结合时，可以提高分类准确率。使用强大而稳定的ResNet50基线模型，我们达到了最大83%的经典准确性，并且可以通过迁移学习的方法将这一数值提升至84%。相比之下，应用我们的量子-经典方法后，性能提升到了87%，这表明在稳健的经典方法上实现了明确可重复的改进。该方法在IBM的各种量子处理器上实现，混合量子和经典方法在整个准确率方面带来了2-3%的绝对改善。这些结果突显了当前及近期量子处理器在卫星成像和遥感等高风险数据驱动领域中的实际潜力，并暗示其在现实世界的机器学习任务中具有更广泛的应用前景。
URL
https://arxiv.org/abs/2602.18350
