Computational Science

张量网络

Tensor Trains

A tensor network decomposition method used to efficiently represent high-dimensional tensors by decomposing them into a chain of lower-dimensional tensors (core tensors). / 一种张量网络分解方法，用于通过将高维张量分解为一系列低维张量（核心张量）来高效表示高维张量。

Knowledge type: 数学理论
First proposed or discovered
Proposer: Turkey Farm member
Creator: 农场主
Last updated: 2026-11-02 21:05:21

Sub-knowledge points

Matrix Product States (MPS)Quantum-inspired homogenization

Higher-order knowledge points

Tensor Network StatesTensor Decomposition

Main parameters and performance indicators

bond dimension: The maximum rank of the core tensors, controlling the approximation accuracy and computational cost.TT-rank: The rank of the core tensors in the Tensor Train decomposition.

Open problems

Optimizing the bond dimension selection for specific quantum-inspired homogenization problems.Minimizing communication overhead across distributed TPU, GPU, and CPU architectures.

Related literature

ranked by community stars

Performance benchmarking of Tensor Trains for quantum-inspired homogenization on TPU, GPU, and CPU architecturesOpen source

张量网络

Sub-knowledge points

Higher-order knowledge points

Main parameters and performance indicators

Open problems

Related products and links