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Colored Jones Polynomials and the Volume Conjecture

arXiv 2502.18575 ↗First posted 25 Feb 2025math.GT cs.LG hep-thcited by 0

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Abstract (arXiv)

Using the vertex model approach for braid representations, we compute polynomials for spin-1 placed on hyperbolic knots up to 15 crossings. These polynomials are referred to as 3-colored Jones polynomials or adjoint Jones polynomials. Training a subset of the data using a fully connected feedforward neural network, we predict the volume of the knot complement of hyperbolic knots from the adjoint Jones polynomial or its evaluations with 99.34% accuracy. A function of the adjoint Jones polynomial evaluated at the phase q=e 8 π i / 15 predicts the volume with nearly the same accuracy as the neural network. From an analysis of 2-colored and 3-colored Jones polynomials, we conjecture the best phase for n-colored Jones polynomials, and use this hypothesis to motivate an improved statement of the volume conjecture. This is tested for knots for which closed form expressions for the n-colored Jones polynomial are known, and we show improved convergence to the volume.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 59 references that are in the map. First posted on arXiv 25 Feb 2025.

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References · 7

  1. 2020Learning to Unknotcited by 40
  2. 2023Is deep learning a useful tool for the pure mathematician?cited by 14
  3. 2020Disentangling a Deep Learned Volume Formulacited by 12
  4. 2021Learning knot invariants across dimensionscited by 12
  5. 2024Rigor with Machine Learning from Field Theory to the Poincar'e Conjecturecited by 12
  6. 2019Big Data Approaches to Knot Theory: Understanding the Structure of the Jones Polynomialcited by 7
  7. 2024Learning BPS Spectra and the Gap Conjecturecited by 3

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