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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2606.01826 |
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| _version_ | 1866916072405336064 |
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| author | Eder, Peter J. Braun, Sarah |
| author_facet | Eder, Peter J. Braun, Sarah |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_01826 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Revisiting the Quantum-Guided Cluster Algorithm: Improvements and Numerical Experiments Eder, Peter J. Braun, Sarah Quantum Physics 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. |
| title | Revisiting the Quantum-Guided Cluster Algorithm: Improvements and Numerical Experiments |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2606.01826 |