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arXiv · 2606.01826

Revisiting the Quantum-Guided Cluster Algorithm: Improvements and Numerical Experiments

Peter J. Eder·Sarah Braun
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Abstract

We study correlation-guided cluster algorithms for solving the Max-Cut problem that iteratively try to improve solutions by updating clusters of nodes. Building on the recently proposed quantum-guided cluster algorithm (QGCA) [arXiv:2508.10656], which leverages precomputed two-point correlations to guide collective updates, we extend the cluster construction by incorporating next-nearest-neighbor (NNN) information. We evaluate this extension across different correlation sources on random regular graphs and non-degenerate tile-planted instances. Notably, we observe particularly strong performance on non-degenerate instances and provide a scaling analysis for this class. Finally, we outline an extension toward a correlation-guided Markov-chain Monte Carlo algorithm, whose detailed analysis remains an open direction for future work.

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Peter J. Eder, Sarah Braun. 2026-06-01. Revisiting the Quantum-Guided Cluster Algorithm: Improvements and Numerical Experiments. https://arxiv.org/abs/2606.01826

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