[Paper] Quantum-enhanced satellite image classification

Source: arXiv - 2602.18350v1

Overview

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.

Key Contributions

• quant‑ph
• cs.CV
• cs.LG

Methodology

Please refer to the full paper for detailed methodology.

Practical Implications

This research contributes to the advancement of quant‑ph.

Authors

• Qi Zhang
• Anton Simen
• Carlos Flores‑Garrigós
• Gabriel Alvarado Barrios
• Paolo A. Erdman
• Enrique Solano
• Aaron C. Kemp
• Vincent Beltrani
• Vedangi Pathak
• Hamed Mohammadbagherpoor

Paper Information

• arXiv ID: 2602.18350v1
• Categories: quant‑ph, cs.CV, cs.LG
• Published: February 20, 2026
• PDF: Download PDF