Tensor-networks for High-order Polynomial Approximation: A Many-body Physics Perspective
Quantum Physics
2022-04-19 v1 Disordered Systems and Neural Networks
Machine Learning
Numerical Analysis
Numerical Analysis
Abstract
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.
Cite
@article{arxiv.2204.07743,
title = {Tensor-networks for High-order Polynomial Approximation: A Many-body Physics Perspective},
author = {Tong Yang},
journal= {arXiv preprint arXiv:2204.07743},
year = {2022}
}
Comments
12 pages