The theory of entanglement provides a fundamentally new language for describing
interactions and correlations in many-body systems. Its vocabulary consists of qubits and …

Originally developed in the context of condensed-matter physics and based on
renormalization group ideas, tensor networks have been revived thanks to quantum …

Tensor networks represent the state-of-the-art in computational methods across many
disciplines, including the classical simulation of quantum many-body systems and quantum …

We introduce a coarse-graining transformation for tensor networks that can be applied to
study both the partition function of a classical statistical system and the Euclidean path …

Tensor networks (TNs) have become one of the most essential building blocks for various
fields of theoretical physics such as condensed matter theory, statistical mechanics …

We introduce a tensor renormalization group scheme for coarse graining a two-dimensional
tensor network that can be successfully applied to both classical and quantum systems on …

We show that any matrix product state (MPS) can be exactly represented by a recurrent
neural network (RNN) with a linear memory update. We generalize this RNN architecture to …

We introduce an efficient algorithm for reducing bond dimensions in an arbitrary tensor
network without changing its geometry. The method is based on a quantitative …

We propose a general procedure for extracting the running coupling constants of the
underlying field theory of a given classical statistical model on a two-dimensional lattice …

We propose a different tensor renormalization group algorithm, anisotropic tensor
renormalization group (ATRG), for lattice models in arbitrary dimensions. The proposed …