Tensor Network Skeletonization
This work provides a new method for numerical computation of tensor networks, potentially benefiting condensed matter physics and quantum computing.
The authors introduce a coarse-graining algorithm for tensor networks that removes short-range correlations at every scale, achieving efficient representations for 1D and 2D quantum Ising models.
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.