no code implementations • 8 Dec 2023 • Yang-Hui He, Vishnu Jejjala, Challenger Mishra, Max Sharnoff

In this work we employ machine learning to understand structured mathematical data involving finite groups and derive a theorem about necessary properties of generators of finite simple groups.

no code implementations • 17 Nov 2022 • Per Berglund, Giorgi Butbaia, Tristan Hübsch, Vishnu Jejjala, Damián Mayorga Peña, Challenger Mishra, Justin Tan

Finding Ricci-flat (Calabi-Yau) metrics is a long standing problem in geometry with deep implications for string theory and phenomenology.

no code implementations • 19 Sep 2022 • Ismail Yunus Akhalwaya, Shashanka Ubaru, Kenneth L. Clarkson, Mark S. Squillante, Vishnu Jejjala, Yang-Hui He, Kugendran Naidoo, Vasileios Kalantzis, Lior Horesh

In this study, we present NISQ-TDA, a fully implemented end-to-end quantum machine learning algorithm needing only a short circuit-depth, that is applicable to high-dimensional classical data, and with provable asymptotic speedup for certain classes of problems.

no code implementations • 15 Feb 2022 • Vishnu Jejjala, Washington Taylor, Andrew Turner

We review briefly the characteristic topological data of Calabi--Yau threefolds and focus on the question of when two threefolds are equivalent through related topological data.

no code implementations • 16 Dec 2021 • Per Berglund, Ben Campbell, Vishnu Jejjala

Using a fully connected feedforward neural network we study topological invariants of a class of Calabi--Yau manifolds constructed as hypersurfaces in toric varieties associated with reflexive polytopes from the Kreuzer--Skarke database.

1 code implementation • 30 Nov 2021 • Jessica Craven, Mark Hughes, Vishnu Jejjala, Arjun Kar

We find that a two-layer feed-forward neural network can predict $s$ from $\text{Kh}(q,-q^{-4})$ with greater than $99\%$ accuracy.

no code implementations • 31 Dec 2020 • Vishnu Jejjala, Damian Kaloni Mayorga Pena, Challenger Mishra

Ricci flat metrics for Calabi-Yau threefolds are not known analytically.

no code implementations • 7 Dec 2020 • Jessica Craven, Vishnu Jejjala, Arjun Kar

We present a simple phenomenological formula which approximates the hyperbolic volume of a knot using only a single evaluation of its Jones polynomial at a root of unity.

no code implementations • 23 Mar 2020 • Yarin Gal, Vishnu Jejjala, Damian Kaloni Mayorga Pena, Challenger Mishra

Quantum chromodynamics (QCD) is the theory of the strong interaction.

no code implementations • 23 Sep 2019 • John-Antonio Argyriadis, Yang-Hui He, Vishnu Jejjala, Djordje Minic

We study the dynamics of genetic code evolution.

no code implementations • 8 Jun 2018 • Kieran Bull, Yang-Hui He, Vishnu Jejjala, Challenger Mishra

The latest techniques from Neural Networks and Support Vector Machines (SVM) are used to investigate geometric properties of Complete Intersection Calabi-Yau (CICY) threefolds, a class of manifolds that facilitate string model building.

Cannot find the paper you are looking for? You can
Submit a new open access paper.

Contact us on:
hello@paperswithcode.com
.
Papers With Code is a free resource with all data licensed under CC-BY-SA.