![[2602.18350] Quantum-enhanced satellite image classification](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.18350] Quantum-enhanced satellite image classification
Summary
This paper presents a quantum feature extraction method that enhances multi-class image classification for satellite applications, achieving improved accuracy over classical methods.
Why It Matters
The research demonstrates the practical potential of quantum computing in enhancing machine learning tasks, particularly in high-stakes fields like satellite imaging. This advancement could lead to more accurate data analysis in remote sensing, impacting various applications from environmental monitoring to urban planning.
Key Takeaways
- Quantum-classical hybrid methods can improve classification accuracy by 2-3%.
- The study achieved a maximum accuracy of 87% using quantum feature extraction.
- Results indicate broader applicability of quantum computing in machine learning tasks.
Quantum Physics arXiv:2602.18350 (quant-ph) [Submitted on 20 Feb 2026] Title:Quantum-enhanced satellite image classification 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 View a PDF of the paper titled Quantum-enhanced satellite image classification, by Qi Zhang and 9 other authors View PDF HTML (experimental) 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 broa...