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 …

Modern applications in engineering and data science are increasingly based on
multidimensional data of exceedingly high volume, variety, and structural richness …

Quantum computing is a game-changing technology for global academia, research centers
and industries including computational science, mathematics, finance, pharmaceutical …

This is a partly non-technical introduction to selected topics on tensor network methods,
based on several lectures and introductory seminars given on the subject. It should be a …

Differentiable programming is a fresh programming paradigm which composes
parameterized algorithmic components and optimizes them using gradient search. The …

The defining problem in frustrated quantum magnetism, the ground state of the nearest-
neighbor S= 1/2 antiferromagnetic Heisenberg model on the kagome lattice, has defied all …

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 …

We propose a novel coarse-graining tensor renormalization group method based on the
higher-order singular value decomposition. This method provides an accurate but low …

We present a variational renormalization group (RG) approach based on a reversible
generative model with hierarchical architecture. The model performs hierarchical change-of …