Tensor Renormalization of Quantum Many-Body Systems using

Tao Xiang, Z. Y. Xie, Jia-Ning Chen +3 more

Gang Su

We review the development of numerical renormalization group methods,including Wilson's numerical renormalization group,White's density matrix renormalization group,and several recent rapidly developing tensor network state-based algorithms.Among these,particular emphasis is laid on the linearized tensor renormalization group method,which was recently proposed to accurately calculate the thermodynamic properties of quantum many-body lattice systems,and which may have broad applications in both one-and two-dimensional quantum systems.

B. I. S. van der Ventel

Glen Evenbly

We discuss in detail algorithms for implementing tensor network renormalization (TNR) for the study of classical statistical and quantum many-body systems. First, we recall established techniques for how the partition function of a $2D$ classical many-body system or the Euclidean path integral of a $1D$ quantum system can be represented as a network of tensors, before describing how TNR can be implemented to efficiently contract the network via a sequence of coarse-graining transformations. The efficacy of the TNR approach is then benchmarked for the $2D$ classical statistical and $1D$ quantum Ising models; in particular the ability of TNR to maintain a high level of accuracy over sustained coarse-graining transformations, even at a critical point, is demonstrated.

Pierre M. Pfeuty, Rémi Jullien, K. A. Penson

Z. Y. Xie, Jialin Chen, Ji-Feng Yu +3 more

Tensor networks are used to represent the wave functions of quantum many-body systems, but the standard approaches only consider two-body entanglement and do not work well for ``frustrated'' systems, where the underlying lattice geometry makes three- or many-body entanglement also important. A new tensor-network approach based on a novel tensor concept for describing such ``simplex'' entanglement shows great promise.