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Article Dans Une Revue Journal of Statistical Physics Année : 2022

Tensor RG Approach to High-Temperature Fixed Point

Résumé

We study a renormalization group (RG) map for tensor networks that include two-dimensional lattice spin systems such as the Ising model. Numerical studies of such RG maps have been quite successful at reproducing the known critical behavior. In those numerical studies the RG map must be truncated to keep the dimension of the legs of the tensors bounded. Our tensors act on an infinite-dimensional Hilbert space, and our RG map does not involve any truncations. Our RG map has a trivial fixed point which represents the high-temperature fixed point. We prove that if we start with a tensor that is close to this fixed point tensor, then the iterates of the RG map converge in the Hilbert-Schmidt norm to the fixed point tensor. It is important to emphasize that this statement is not true for the simplest tensor network RG map in which one simply contracts four copies of the tensor to define the renormalized tensor. The linearization of this simple RG map about the fixed point is not a contraction due to the presence of so-called CDL tensors. Our work provides a first step towards the important problem of the rigorous study of RG maps for tensor networks in a neighborhood of the critical point.

Dates et versions

hal-03318641 , version 1 (10-08-2021)

Identifiants

Citer

Tom Kennedy, Slava Rychkov. Tensor RG Approach to High-Temperature Fixed Point. Journal of Statistical Physics, 2022, 187 (3), pp.33. ⟨10.1007/s10955-022-02924-4⟩. ⟨hal-03318641⟩
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