Big data comparison of quantum invariants
Fuente:
arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866913907653738496 |
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| author | Tubbenhauer, Daniel Zhang, Victor |
| author_facet | Tubbenhauer, Daniel Zhang, Victor |
| contents | We apply big data techniques, including exploratory and topological data analysis, to investigate quantum invariants. More precisely, our study explores the Jones polynomial's structural properties and contrasts its behavior under four principal methods of enhancement: coloring, rank increase, categorification, and leaving the realm of Lie algebras. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_15810 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Big data comparison of quantum invariants Tubbenhauer, Daniel Zhang, Victor Geometric Topology Machine Learning Quantum Algebra Primary: 57K16, 62R07, secondary: 57K18, 68P05 We apply big data techniques, including exploratory and topological data analysis, to investigate quantum invariants. More precisely, our study explores the Jones polynomial's structural properties and contrasts its behavior under four principal methods of enhancement: coloring, rank increase, categorification, and leaving the realm of Lie algebras. |
| title | Big data comparison of quantum invariants |
| topic | Geometric Topology Machine Learning Quantum Algebra Primary: 57K16, 62R07, secondary: 57K18, 68P05 |
| url | https://arxiv.org/abs/2503.15810 |