Quantum-enhanced satellite image classification

Qi Zhang; Anton Simen; Carlos Flores-Garrigós; Gabriel Alvarado Barrios; Paolo A. Erdman; Enrique Solano; Aaron C. Kemp; Vincent Beltrani

quant-phFeb 20, 2026

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,

Organizations: Kipu Quantum, Greifswalderstrasse 212, 10405 Berlin, Germany · Department of Physical Chemistry, University of the Basque Country UPV/EHU, Apartado 644, 48080 Bilbao, Spain · IDAL, Electronic Engineering Department, ETSE-UV, University of Valencia, Avgda. Universitat s/n, 46100 Burjassot, Valencia, Spain · KPMG LLP, 2 Manhattan West, New York, NY 10001 ·

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.

Figures & tables

Figure 1: Overview of the quantum-enhanced classification pipeline. Multisensor remote-sensing imagery is first mapped to an n-dimensional classical feature vector (in this case, n = 15, 120 and 156). These features parametrize an n-qubit Hamiltonian, which is processed through Digitized Quantum Feature extraction (DQFE) using a counterdiabatic evolution protocol. Local measurements yield one-body expectation values and two-body correlation functions, which are then used as input to a classical classifier such as gradient boosting or random forests.

Figure 2: Overview of the reduced TreeSatAI dataset. (a) Representative aerial patches from the five selected classes used in the hardware-compatible benchmark. (b) Example of the three sensing modalities available for each ground location: aerial imagery, Sentinel–2 multispectral measurements, and Sentinel–1 radar data. These heterogeneous inputs are reduced to $n$ classical features before quantum processing.

Dataset	Backend/Model	Var./Qubits	Accuracy (%)
Hybrid	AER (sim.)	15	83.5
	KINGSTON	156	81.5
	BOSTON	120	86.5
	PITTSBURG	120	86.5
Quantum	AER (sim.)	15	82.0
	KINGSTON	156	81.0

Table 1: Classification accuracy (%) for classical, quantum-only, and hybrid classical-quantum models across different IBM quantum hardware backends. For each backend, the dimensionality of the classical feature representation is matched to the number of available qubits. In the classical segment, the number of variables are given by the number of nodes in the last hidden layer before the output one. The best result for each segment is highlighted in bold.

Figure 3: Two-dimensional PCA projections and their mean Fisher information, $\bar{F}$ , of the (a) classical and (c) quantum feature representations and the respective classification performances on classical and quantum datasets for the TreeSatAI reduced benchmark.

Hybrid Quantum-Classical ML

Hybrid quantum-classical machine learning combines quantum circuits or annealers with classical models for learning, prediction, generation, and optimization.

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Enhancing Blood Cells Classification using Hybrid Quantum Neural Networks

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Hybrid Quantum-Classical ML Medical Image Classification Variational Quantum Circuits White Blood Cell Classification

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Accurate classification of microscopic blood cells is still a critical task in medical image analysis, where subtle variations and limited data can challenge conventional deep learning models. As such, we investigate in this work the potential of Hybrid Quantum-Classical Neural Networks (HQNNs) to enhance feature representation and improve classification performance in this domain. We propose a modular architecture combining a pre-trained ResNet-50 backbone with a low-dimensional latent bottleneck and a variational quantum circuit, enabling a direct comparison between quantum-enhanced and purely classical transformation mechanisms. To isolate the contribution of the quantum component, we evaluate three architectures: a HQNN model, a Classical Matched Model with an additional nonlinear transformation layer of comparable capacity, and a baseline model without an intermediate transformation stage. Experiments conducted on two publicly available blood cell datasets, namely the Blood Cell Images dataset and the PBC dataset, demonstrate that HQNNs consistently achieve superior or more balanced performance across evaluation metrics. In the Blood Cell Images Dataset, the proposed approach improves macro F1-score by up to 3.7% compared to classical baselines, while improving the F1-score from 98.54% to 98.69% in the more challenging 8-class scenario with near-saturated performance. Additional evaluation on IBM quantum hardware shows that the model remains robust under noise, with only a modest performance degradation relative to simulated results. These results indicate that quantum feature transformations can enhance discriminative representations, particularly in challenging classification scenarios, and highlight the practical potential of HQNN models for medical imaging tasks.

Abstract

Hybrid Quantum-Classical ML

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