In this study, we present Q-Seg, a novel unsupervised image segmentation
method based on quantum annealing, tailored for existing quantum hardware. We
formulate the pixel-wise segmentation problem, which assimilates spectral and
spatial information of the image, as a graph-cut optimization task. Our method
efficiently leverages the interconnected qubit topology of the D-Wave Advantage
device, offering superior scalability over existing quantum approaches and
outperforming state-of-the-art classical methods. Our empirical evaluations on
synthetic datasets reveal that Q-Seg offers better runtime performance against
the classical optimizer Gurobi. Furthermore, we evaluate our method on
segmentation of Earth Observation images, an area of application where the
amount of labeled data is usually very limited. In this case, Q-Seg
demonstrates near-optimal results in flood mapping detection with respect to
classical supervised state-of-the-art machine learning methods. Also, Q-Seg
provides enhanced segmentation for forest coverage compared to existing
annotated masks. Thus, Q-Seg emerges as a viable alternative for real-world
applications using available quantum hardware, particularly in scenarios where
the lack of labeled data and computational runtime are critical.

本研究提出了一种基于量子退火的新型无监督图像分割方法 Q-Seg，该方法针对现有量子硬件进行了优化，并通过将图像的光谱和空间信息与图剪切优化问题相结合的方式来解决像素级分割问题。经过合成数据集的实证评估，Q-Seg 在运行时间性能方面优于经典优化器 Gurobi。此外，我们还对地球观测图像的分割进行了评估，Q-Seg 在洪灾映射检测方面表现出几乎最优的结果，并提供了比现有注释掩模更好的森林覆盖分割。因此，Q-Seg 是一种适用于现有量子硬件的可行选择，特别适用于缺乏标记数据和计算时间的关键情景中。