# Renormalization of tensor networks using graph independent local truncations

@article{Hauru2018RenormalizationOT, title={Renormalization of tensor networks using graph independent local truncations}, author={Markus Hauru and Cl{\'e}ment Delcamp and Sebastian Mizera}, journal={Physical Review B}, year={2018}, volume={97}, pages={045111} }

We introduce an efficient algorithm for reducing bond dimensions in an arbitrary tensor network without changing its geometry. The method is based on a novel, quantitative understanding of local correlations in a network. Together with a tensor network coarse-graining algorithm, it yields a proper renormalization group (RG) flow. Compared to existing methods, the advantages of our algorithm are its low computational cost, simplicity of implementation, and applicability to any network. We… CONTINUE READING

#### Citations

##### Publications citing this paper.

SHOWING 1-10 OF 33 CITATIONS

## Tensor Networks and the Renormalization Group

VIEW 5 EXCERPTS

CITES BACKGROUND

## Tensor network renormalization with fusion charges: applications to 3d lattice gauge theory

VIEW 1 EXCERPT

CITES BACKGROUND

## Computing the renormalization group flow of two-dimensional $\phi^4$ theory with tensor networks

VIEW 9 EXCERPTS

CITES METHODS & BACKGROUND

## Neural Network Renormalization Group

VIEW 1 EXCERPT

CITES BACKGROUND

### FILTER CITATIONS BY YEAR

#### References

##### Publications referenced by this paper.

SHOWING 1-10 OF 70 REFERENCES

## Tensor Network Renormalization.

VIEW 8 EXCERPTS

HIGHLY INFLUENTIAL

## Tensor Network Skeletonization

VIEW 7 EXCERPTS

HIGHLY INFLUENTIAL

## Renormalization Group Flows of Hamiltonians Using Tensor Networks.

VIEW 4 EXCERPTS

HIGHLY INFLUENTIAL