# Learning Algebraic Models of Quantum Entanglement

arXiv: https://arxiv.org/abs/1908.10247 · HTML: https://mlsanity.com/paper/1908.10247/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2019.

## Built on by (most cited first, 2 of 2)

- [Activation degree thresholds and expressiveness of polynomial neural networks](https://mlsanity.com/paper/2408.04569/) (2024)
- [Learning entanglement breakdown as a phase transition by confusion](https://mlsanity.com/paper/2202.00348/) (2022)

## Abstract (arXiv)

We review supervised learning and deep neural network design for learning membership on algebraic varieties. We demonstrate that these trained artificial neural networks can predict the entanglement type for quantum states. We give examples for detecting degenerate states, as well as border rank classification for up to 5 binary qubits and 3 qutrits (ternary qubits).