GTLGQAMar 20, 2025

Big data comparison of quantum invariants

arXiv:2503.15810v14 citationsh-index: 2
Originality Synthesis-oriented
AI Analysis

This work addresses the analysis of quantum invariants for researchers in mathematical physics or topology, but it appears incremental as it applies existing big data methods to a known problem.

The authors applied big data techniques to analyze the structural properties of the Jones polynomial, comparing its behavior under four enhancement methods: coloring, rank increase, categorification, and leaving Lie algebras.

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.

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