QClusformer: A Quantum Transformer-based Framework for Unsupervised Visual Clustering

TL;DR

QClusformer introduces a quantum-enhanced Transformer framework for unsupervised visual clustering, leveraging quantum computing to improve efficiency and outperform classical methods on large-scale vision datasets.

Contribution

This work pioneers the integration of quantum computing with Transformer architecture specifically for unsupervised vision clustering tasks.

Findings

QClusformer outperforms classical methods on benchmarks like MS-Celeb-1M and DeepFashion.

Quantum-inspired Transformer design improves clustering accuracy.

End-to-end quantum-Transformer framework demonstrates superior scalability.

Abstract

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.