Machine Learning Approaches to Building Quantum Circuits for Sets of Matrices
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911699613777920 |
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| author | Fedin, Matvei Morozov, Andrei |
| author_facet | Fedin, Matvei Morozov, Andrei |
| contents | Machine learning nowadays becomes a useful instrument in many subjects. In this paper we use interpretable machine learning to build quantum algorithm. By studying the parameters of the machine learning algorithm we were able to construct universal shortest analytic quantum algorithm for arbitrary diagonal matrix of any size. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_06633 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Machine Learning Approaches to Building Quantum Circuits for Sets of Matrices Fedin, Matvei Morozov, Andrei Quantum Physics High Energy Physics - Theory Machine learning nowadays becomes a useful instrument in many subjects. In this paper we use interpretable machine learning to build quantum algorithm. By studying the parameters of the machine learning algorithm we were able to construct universal shortest analytic quantum algorithm for arbitrary diagonal matrix of any size. |
| title | Machine Learning Approaches to Building Quantum Circuits for Sets of Matrices |
| topic | Quantum Physics High Energy Physics - Theory |
| url | https://arxiv.org/abs/2605.06633 |