Citation

Suva, Md Wahiduzzaman and Sajin, Md Imtiaj Alam and Abha, Esm E Moula Chowdhury and Islam, Md Tauhidul and Mridha, M F and Hamid, Md Abdul and Hossen, Md Jakir (2026) A symmetry-aware equivariant quantum clustering approach for enhanced unsupervised learning. Physica Scripta, 101 (2). 026007. ISSN 0031-8949

Text A symmetry-aware equivariant quantum clustering approach for enhanced unsupervised learning - IOPscience.pdf - Published Version Restricted to Repository staff only Download (947kB)

Abstract

Clustering high-dimensional data with inherent geometric symmetries is a key challenge in unsupervised learning. While traditional algorithms like K-Means struggle with complex data structures, existing quantum methods often lack equivariance and face scalability issues on Noisy Intermediate-Scale Quantum (NISQ) hardware. This creates a need for a clustering approach that can effectively handle structured data. We introduce the p4m Equivariant Quantum Clustering (EQC) framework, a novel approach that integrates equivariant quantum neural networks with quantum kernel methods. Our model utilizes an 8-qubit quantum circuit with p4m symmetry-preserving operations to ensure robust feature extraction. We employ a hybrid quantum-classical optimization strategy to tune the model parameters. We evaluated the EQC framework on the MNIST and Quark-Gluon datasets using a noise-free quantum simulator. Under simulated depolarizing noise, EQC maintains stable performance on MNIST (Silhouette Score: 0.471; Accuracy: 53.3%), demonstrating robustness suitable for near-term quantum hardware. On the Quark-Gluon dataset, EQC achieved a Silhouette Score of 0.512 and 74.8% accuracy, a significant improvement over classical K-Means (Silhouette Score: 0.183; Accuracy: 58.4%). The model also demonstrated state-of-the-art performance on MNIST, achieving a Silhouette Score of 0.557 and 76.3% accuracy, consistently surpassing other quantum and classical baselines. Although current evaluations are based on simulated backends, the inclusion of realistic noise modeling highlights EQC’s resilience and scalability potential. These results suggest that incorporating symmetry-aware design principles in quantum machine learning can offer tangible advantages for structured data analysis on NISQ-era systems.

Item Type:	Article
Uncontrolled Keywords:	Machine learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	02 Mar 2026 01:53
Last Modified:	02 Mar 2026 01:53
URII:	http://shdl.mmu.edu.my/id/eprint/15398

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