HQ-UNet: A Hybrid Quantum-Classical U-Net with a Quantum Bottleneck for Remote Sensing Image Segmentation

TL;DR

This paper introduces HQ-UNet, a hybrid quantum-classical model for remote sensing image segmentation, which integrates a quantum bottleneck to improve feature representation under near-term quantum hardware constraints.

Contribution

It proposes a novel hybrid quantum-classical U-Net architecture with a quantum bottleneck, demonstrating improved segmentation performance on remote sensing data.

Findings

HQ-UNet achieves a mean IoU of 0.8050.

HQ-UNet attains an overall accuracy of 94.76%.

Quantum bottleneck enhances feature representation in segmentation.

Abstract

Semantic segmentation in remote sensing is commonly addressed using classical deep learning architectures such as U-Net, which require a large number of parameters to model complex spatial relationships. Quantum machine learning (QML) provides an alternative representation paradigm by mapping classical features into quantum states, but its direct application to high-dimensional images remains challenging under near-term quantum hardware constraints. In this work, we propose HQ-UNet, a hybrid quantum-classical U-Net architecture that integrates a compact parameterized quantum circuit at the bottleneck of a classical U-Net. The proposed design uses a non-pooling quantum convolutional module to enrich highly compressed encoder features before decoding, while keeping the quantum component shallow and parameter-efficient. Experiments on the LandCover.ai dataset show that HQ-UNet achieves a mean IoU of 0.8050 and an overall accuracy of 94.76%, outperforming the classical U-Net baseline. These results suggest that compact quantum bottlenecks can enhance feature representation for remote sensing image segmentation under near-term quantum constraints. This highlights the potential of hybrid quantum-classical designs as a promising direction for parameter-efficient dense prediction in Earth observation.