Reducing hyperparameter sensitivity in measurement-feedback based Ising machines
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866912942627225600 |
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| author | Sevenants, Toon Van der Sande, Guy Verschaffelt, Guy |
| author_facet | Sevenants, Toon Van der Sande, Guy Verschaffelt, Guy |
| contents | Analog Ising machines have been proposed as heuristic hardware solvers for combinatorial optimization problems, with the potential to outperform conventional approaches, provided that their hyperparameters are carefully tuned. Their temporal evolution is often described using time-continuous dynamics. However, most experimental implementations rely on measurement-feedback architectures that operate in a time-discrete manner. We observe that in such setups, the range of effective hyperparameters is substantially smaller than in the envisioned time-continuous analog Ising machine. In this paper, we analyze this discrepancy and discuss its impact on the practical operation of Ising machines. Next, we propose and experimentally verify a method to reduce the sensitivity to hyperparameter selection of these measurement-feedback architectures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_04093 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Reducing hyperparameter sensitivity in measurement-feedback based Ising machines Sevenants, Toon Van der Sande, Guy Verschaffelt, Guy Machine Learning Applied Physics Computational Physics Data Analysis, Statistics and Probability Analog Ising machines have been proposed as heuristic hardware solvers for combinatorial optimization problems, with the potential to outperform conventional approaches, provided that their hyperparameters are carefully tuned. Their temporal evolution is often described using time-continuous dynamics. However, most experimental implementations rely on measurement-feedback architectures that operate in a time-discrete manner. We observe that in such setups, the range of effective hyperparameters is substantially smaller than in the envisioned time-continuous analog Ising machine. In this paper, we analyze this discrepancy and discuss its impact on the practical operation of Ising machines. Next, we propose and experimentally verify a method to reduce the sensitivity to hyperparameter selection of these measurement-feedback architectures. |
| title | Reducing hyperparameter sensitivity in measurement-feedback based Ising machines |
| topic | Machine Learning Applied Physics Computational Physics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2603.04093 |