Quantum-Assisted Correlation Clustering

• URL: http://arxiv.org/abs/2509.03561v1
• Date: Wed, 03 Sep 2025 12:14:35 GMT
• Title: Quantum-Assisted Correlation Clustering
• Authors: Antonio Macaluso, Supreeth Mysore Venkatesh, Diego Arenas, Matthias Klusch, Andreas Dengel,
• Abstract summary: This work introduces a hybrid quantum-classical method to correlation clustering, a graph-based unsupervised learning task.<n>We adapt GCS-Q, a quantum-assisted solver originally designed for coalition structure generation, to maximize intra-cluster agreement in signed graphs.<n> Empirical evaluations on synthetic signed graphs and real-world hyperspectral imaging data demonstrate that, when adapted for correlation clustering, GCS-Q outperforms classical algorithms in robustness and clustering quality.
• Score: 3.8448698053186843
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work introduces a hybrid quantum-classical method to correlation clustering, a graph-based unsupervised learning task that seeks to partition the nodes in a graph based on pairwise agreement and disagreement. In particular, we adapt GCS-Q, a quantum-assisted solver originally designed for coalition structure generation, to maximize intra-cluster agreement in signed graphs through recursive divisive partitioning. The proposed method encodes each bipartitioning step as a quadratic unconstrained binary optimization problem, solved via quantum annealing. This integration of quantum optimization within a hierarchical clustering framework enables handling of graphs with arbitrary correlation structures, including negative edges, without relying on metric assumptions or a predefined number of clusters. Empirical evaluations on synthetic signed graphs and real-world hyperspectral imaging data demonstrate that, when adapted for correlation clustering, GCS-Q outperforms classical algorithms in robustness and clustering quality on real-world data and in scenarios with cluster size imbalance. Our results highlight the promise of hybrid quantum-classical optimization for advancing scalable and structurally-aware clustering techniques in graph-based unsupervised learning.

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