  Unsupervised vision clustering, a cornerstone in computer vision, has been
studied for decades, yielding significant outcomes across numerous vision
tasks. However, these algorithms involve substantial computational demands when
confronted with vast amounts of unlabeled data. Conversely, quantum computing
holds promise in expediting unsupervised algorithms when handling large-scale
databases. In this study, we introduce QClusformer, a pioneering
Transformer-based framework leveraging quantum machines to tackle unsupervised
vision clustering challenges. Specifically, we design the Transformer
architecture, including the self-attention module and transformer blocks, from
a quantum perspective to enable execution on quantum hardware. In addition, we
present QClusformer, a variant based on the Transformer architecture, tailored
for unsupervised vision clustering tasks. By integrating these elements into an
end-to-end framework, QClusformer consistently outperforms previous methods
running on classical computers. Empirical evaluations across diverse
benchmarks, including MS-Celeb-1M and DeepFashion, underscore the superior
performance of QClusformer compared to state-of-the-art methods.
