Tensor Network Skeletonization

Understand

We introduce a new coarse-graining algorithm, tensor network skeletonization, for the numerical computation of tensor networks.

• This approach utilizes a structure-preserving skeletonization procedure to remove short-range correlations effectively at every scale.
• This approach is first presented in the setting of 2D statistical Ising model and is then extended to higher dimensional tensor networks and disordered systems.
• When applied to the Euclidean path integral formulation, this approach also gives rise to new efficient representations of the ground states for 1D and 2D quantum Ising models.