Complex Vector Gain-Based Annealer for Minimizing XY Hamiltonians
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866917828023549952 |
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| author | Cummins, James S. Berloff, Natalia G. |
| author_facet | Cummins, James S. Berloff, Natalia G. |
| contents | This paper presents the Complex Vector Gain-Based Annealer (CoVeGA), an analog computing platform designed to overcome energy barriers in XY Hamiltonians through a higher-dimensional representation. Traditional gain-based solvers utilizing optical or photonic hardware typically represent each XY spin with a single complex field. These solvers often struggle with large energy barriers in complex landscapes, leading to relaxation into excited states. CoVeGA addresses these limitations by employing two complex fields to represent each XY spin and dynamically evolving the energy landscape through time-dependent annealing. Operating in a higher-dimensional space, CoVeGA bridges energy barriers in this expanded space during the continuous phase evolution, thus avoiding entrapment in local minima. We introduce several graph structures that pose challenges for XY minimization and use them to benchmark CoVeGA against single-dimension XY solvers, highlighting the benefits of higher-dimensional operation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_02010 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Complex Vector Gain-Based Annealer for Minimizing XY Hamiltonians Cummins, James S. Berloff, Natalia G. Disordered Systems and Neural Networks Other Condensed Matter Emerging Technologies Adaptation and Self-Organizing Systems Optics This paper presents the Complex Vector Gain-Based Annealer (CoVeGA), an analog computing platform designed to overcome energy barriers in XY Hamiltonians through a higher-dimensional representation. Traditional gain-based solvers utilizing optical or photonic hardware typically represent each XY spin with a single complex field. These solvers often struggle with large energy barriers in complex landscapes, leading to relaxation into excited states. CoVeGA addresses these limitations by employing two complex fields to represent each XY spin and dynamically evolving the energy landscape through time-dependent annealing. Operating in a higher-dimensional space, CoVeGA bridges energy barriers in this expanded space during the continuous phase evolution, thus avoiding entrapment in local minima. We introduce several graph structures that pose challenges for XY minimization and use them to benchmark CoVeGA against single-dimension XY solvers, highlighting the benefits of higher-dimensional operation. |
| title | Complex Vector Gain-Based Annealer for Minimizing XY Hamiltonians |
| topic | Disordered Systems and Neural Networks Other Condensed Matter Emerging Technologies Adaptation and Self-Organizing Systems Optics |
| url | https://arxiv.org/abs/2411.02010 |