# Pushing Tensor Networks to the Limit

## TL;DR

This paper discusses recent advancements in tensor network theory, specifically the development of continuous tensor network states (cMPS) that extend tensor networks to higher-dimensional continuous systems, overcoming previous limitations.

## Contribution

It highlights the work by Tilloy and Cirac that generalizes tensor networks to the continuum in higher dimensions, introducing a new class of continuous tensor network states.

## Key findings

- Overcame limitations in tensor network generalization to continuum
- Proposed continuous tensor network states for higher dimensions
- Extended tensor network applicability to 2D and higher systems

## Abstract

This $Physics$ viewpoint considers recent work by Tilloy and Cirac [Phys. Rev. X 9, 021040 (2019), arXiv:1808.00976]; those authors overcame several past limitations in the generalization of tensor networks to the continuum and proposed a new class of continuous tensor network states (cMPS) which apply to spatial dimensions of two and higher.