QuantFormer: A hybrid quantum classical transformer for hyperspectral image classification

Abstract

Hyperspectral image (HSI) classification is challenging because each pixel has hundreds of spectral bands while only a small number of labelled samples are available. This paper presents QuantFormer, a hybrid quantum–classical transformer that embeds a small variational quantum circuit as a spectral token encoder inside a vision transformer backbone for pixel-wise land-cover mapping. A unified patch-based pipeline with band-wise normalization, principal component analysis, and quantum token encoding is evaluated on four benchmarks: Indian Pines, Pavia University, a 7-class Houston 2013 subset, and EuroSAT_MS. With roughly 35k trainable parameters, QuantFormer attains overall accuracy above 99% on the three airborne hyperspectral datasets and about 89.8% on EuroSAT_MS, competitive with deep 3D CNNs while using substantially fewer weights. Beyond full-data experiments, we also study limited-label regimes and provide practical guidance on when quantum token encoders are a viable alternative to classical projections, without claiming quantum advantage over the strongest classical baselines.