# Tensor-networks for High-order Polynomial Approximation: A Many-body Physics Perspective

arXiv: https://arxiv.org/abs/2204.07743 · HTML: https://mlsanity.com/paper/2204.07743/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 10 references that are in the map.
- First posted on arXiv 16 Apr 2022.

## Builds on (most cited first, 1 of 1)

- [Entanglement Entropy of Target Functions for Image Classification and Convolutional Neural Network](https://mlsanity.com/paper/1710.05520/) (2017)

## Abstract (arXiv)

We analyze the problem of high-order polynomial approximation from a many-body physics perspective, and demonstrate the descriptive power of entanglement entropy in capturing model capacity and task complexity. Instantiated with a high-order nonlinear dynamics modeling problem, tensor-network models are investigated and exhibit promising modeling advantages. This novel perspective establish a connection between quantum information and functional approximation, which worth further exploration in future research.