Tensor network is a fundamental mathematical tool with a huge range of applications in
physics, such as condensed matter physics, statistic physics, high energy physics, and …

This brief review introduces the reader to tensor network methods, a powerful theoretical
and numerical paradigm spawning from condensed matter physics and quantum information …

We present a compendium of numerical simulation techniques, based on tensor network
methods, aiming to address problems of many-body quantum mechanics on a classical …

Tensor network algorithms have proven to be very powerful tools for studying one-and two-
dimensional quantum many-body systems. However, their application to three-dimensional …

The full exploitation of non-Abelian symmetries in tensor network states (TNSs) derived from
a given lattice Hamiltonian is attractive in various aspects. From a theoretical perspective, it …

Quantum computing is a promising way to systematically solve the longstanding
computational problem, the ground state of a many-body fermion system. Many efforts have …

We carefully investigate the comprehensive impact of atom-cavity interaction and artificial
magnetic fields on quantum phase transitions of anti-Jaynes-Cummings triangle model in …

We present a general graph-based projected entangled-pair state (gPEPS) algorithm to
approximate ground states of nearest-neighbor local Hamiltonians on any lattice or graph of …

How many particles are necessary to make a quantum system many-body? To answer this
question, we take as reference for the many-body limit a quantum system at half-filling and …

Tensor networks offer a novel and powerful tool for solving a variety of problems in
mathematics, data science, and engineering. One such network is the multiscale …