A Polynomial-Scaling PDE Solver with Entanglement-Basis Tensor Networks
Abstract
We develop a finite element method (FEM) for partial differential equation (PDE) solver based on the entanglement-basis representation introduced in our companion work. By lifting non-linear finite-element equations into an augmented coefficient space, the governing PDE together with boundary, initial, and inter-element constraints can be expressed through a unified quadratic residual minimization. Although this augmented space grows exponentially with the number of elements, its tensor-product structure allows it to be represented efficiently using tensor networks. Using the matrix product state (MPS) as a concrete example, we show that density matrix renormalization group (DMRG) sweeps enable element-by-element optimization without explicitly constructing the full augmented space. For bounded bond dimension, the resulting computational cost scales polynomially with the number of finite elements. We extend the framework to time-dependent problems through implicit temporal discretization and demonstrate convergence under both mesh and polynomial refinement using diffusion equations.
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BibTeX
@misc{chotrattanapituk2026polynomialscaling,
title = {{A Polynomial-Scaling PDE Solver with Entanglement-Basis Tensor Networks}},
author = {Abhijatmedhi Chotrattanapituk and Michael J. Landry and Chu-Liang Fu and Mingda Li},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.02316},
eprint = {2610.02316},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.02316},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - A Polynomial-Scaling PDE Solver with Entanglement-Basis Tensor Networks
AU - Abhijatmedhi Chotrattanapituk
AU - Michael J. Landry
AU - Chu-Liang Fu
AU - Mingda Li
PY - 2026
DA - 2026/10/01
PB - arXiv
DO - 10.48550/arxiv.2610.02316
UR - https://arxiv.org/abs/2610.02316
AB - We develop a finite element method (FEM) for partial differential equation (PDE) solver based on the entanglement-basis representation introduced in our companion work. By lifting non-linear finite-element equations into an augmented coefficient space, the governing PDE together with boundary, initial, and inter-element constraints can be expressed through a unified quadratic residual minimization. Although this augmented space grows exponentially with the number of elements, its tensor-product structure allows it to be represented efficiently using tensor networks. Using the matrix product state (MPS) as a concrete example, we show that density matrix renormalization group (DMRG) sweeps enable element-by-element optimization without explicitly constructing the full augmented space. For bounded bond dimension, the resulting computational cost scales polynomially with the number of finite elements. We extend the framework to time-dependent problems through implicit temporal discretization and demonstrate convergence under both mesh and polynomial refinement using diffusion equations.
ER -