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

arXiv: https://arxiv.org/abs/2405.19722 · HTML: https://mlsanity.com/paper/2405.19722/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 77 references that are in the map.
- First posted on arXiv 30 May 2024.

## Builds on (most cited first, 30 of 38)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [CoCa: Contrastive Captioners are Image-Text Foundation Models](https://mlsanity.com/paper/2205.01917/) (2022)
- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [Parameterized quantum circuits as machine learning models](https://mlsanity.com/paper/1906.07682/) (2019)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits](https://mlsanity.com/paper/2107.10845/) (2021)
- [Stochastic gradient descent for hybrid quantum-classical optimization](https://mlsanity.com/paper/1910.01155/) (2019)
- [Linkage Based Face Clustering via Graph Convolution Network](https://mlsanity.com/paper/1903.11306/) (2019)
- [Equivariant Similarity for Vision-Language Foundation Models](https://mlsanity.com/paper/2303.14465/) (2023)
- [Learning to Cluster Faces on an Affinity Graph](https://mlsanity.com/paper/1904.02749/) (2019)
- [Variational Quantum Reinforcement Learning via Evolutionary Optimization](https://mlsanity.com/paper/2109.00540/) (2021)
- [Micron-BERT: BERT-based Facial Micro-Expression Recognition](https://mlsanity.com/paper/2304.03195/) (2023)
- [Learning to Cluster Faces via Confidence and Connectivity Estimation](https://mlsanity.com/paper/2004.00445/) (2020)
- [Insect-Foundation: A Foundation Model and Large-scale 1M Dataset for Visual Insect Understanding](https://mlsanity.com/paper/2311.15206/) (2023)
- [Q-Match: Iterative Shape Matching via Quantum Annealing](https://mlsanity.com/paper/2105.02878/) (2021)
- [Clustering Millions of Faces by Identity](https://mlsanity.com/paper/1604.00989/) (2016)
- [Nonnegative/binary matrix factorization with a D-Wave quantum annealer](https://mlsanity.com/paper/1704.01605/) (2017)
- [A Quantum Computational Approach to Correspondence Problems on Point Sets](https://mlsanity.com/paper/1912.12296/) (2019)
- [Structure-Aware Face Clustering on a Large-Scale Graph with bf107 Nodes](https://mlsanity.com/paper/2103.13225/) (2021)
- [Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition](https://mlsanity.com/paper/1809.01407/) (2018)
- [Adiabatic Quantum Graph Matching with Permutation Matrix Constraints](https://mlsanity.com/paper/2107.04032/) (2021)
- [Multi-Camera Multiple 3D Object Tracking on the Move for Autonomous Vehicles](https://mlsanity.com/paper/2204.09151/) (2022)
- [Quantum-soft QUBO Suppression for Accurate Object Detection](https://mlsanity.com/paper/2007.13992/) (2020)
- [Ada-NETS: Face Clustering via Adaptive Neighbour Discovery in the Structure Space](https://mlsanity.com/paper/2202.03800/) (2022)
- [Quantum Motion Segmentation](https://mlsanity.com/paper/2203.13185/) (2022)
- [Two-Dimensional Quantum Material Identification via Self-Attention and Soft-labeling in Deep Learning](https://mlsanity.com/paper/2205.15948/) (2022)

## Built on by (most cited first, 11 of 11)

- [Diffusion-Inspired Quantum Noise Mitigation in Parameterized Quantum Circuits](https://mlsanity.com/paper/2406.00843/) (2024)
- [Hierarchical Quantum Control Gates for Functional MRI Understanding](https://mlsanity.com/paper/2408.03596/) (2024)
- [Quantum-Brain: Quantum-Inspired Neural Network Approach to Vision-Brain Understanding](https://mlsanity.com/paper/2411.13378/) (2024)
- [QMoE: A Quantum Mixture of Experts Framework for Scalable Quantum Neural Networks](https://mlsanity.com/paper/2507.05190/) (2025)
- [Quantum Doubly Stochastic Transformers](https://mlsanity.com/paper/2504.16275/) (2025)
- [COBRA: A Continual Learning Approach to Vision-Brain Understanding](https://mlsanity.com/paper/2411.17475/) (2024)
- [Quantum Graph Attention Network: A Novel Quantum Multi-Head Attention Mechanism for Graph Learning](https://mlsanity.com/paper/2508.17630/) (2025)
- [BRAIN: Bias-Mitigation Continual Learning Approach to Vision-Brain Understanding](https://mlsanity.com/paper/2508.18187/) (2025)
- [Quantum Model Parallelism for MRI-Based Classification of Alzheimer's Disease Stages](https://mlsanity.com/paper/2602.00128/) (2026)
- [QLAM: A Quantum Long-Attention Memory Approach to Long-Sequence Token Modeling](https://mlsanity.com/paper/2605.13833/) (2026)
- [QuPAINT: Physics-Aware Multimodal Reasoning for Quantum Material Characterization](https://mlsanity.com/paper/2609.12202/) (2026)

## Abstract (arXiv)

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.