Quantum-enhanced satellite image classification

TL;DR

This paper demonstrates a quantum-classical hybrid method that improves satellite image classification accuracy by 2-3% over classical methods, showcasing the practical potential of current quantum processors in remote sensing.

Contribution

The paper introduces a quantum feature extraction technique using many-body spin Hamiltonians that enhances multi-class satellite image classification accuracy.

Findings

Quantum-classical hybrid approach increases accuracy to 87%.

Consistent 2-3% accuracy gains on IBM quantum processors.

Potential for practical quantum applications in remote sensing.

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.