mlsanityData to 14 Oct 2026

Big data comparison of quantum invariants

arXiv 2503.15810 ↗First posted 20 Mar 2025math.GT cs.LG math.QAcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 4 of its 70 references that are in the map. First posted on arXiv 20 Mar 2025.

Builds on

4 of 70 references.

References · 4

  1. 2019Big Data Approaches to Knot Theory: Understanding the Structure of the Jones Polynomialcited by 7
  2. 2024Big data approach to Kazhdan-Lusztig polynomialscited by 4
  3. 2021Mapper-type algorithms for complex data and relationscited by 3
  4. 2022Monoidal categories, representation gap and cryptographycited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025On detection probabilities of link invariantscited by 2
  2. 2025On knot detection via picture recognitioncited by 2
  3. 2026RL unknotter, hard unknots and unknotting numbercited by 0