We propose a new method for computing the ground state properties and the time evolution of infinite chains based on a transverse contraction of the tensor network. The method does not require finite size extrapolation and avoids explicit truncation of the bond dimension along the evolution. By folding the network in the time direction prior to contraction, time-dependent expectation values and dynamic correlation functions can be computed after much longer evolution time than with any previous method. Moreover, the algorithm we propose can be used for the study of some noninvariant infinite chains, including impurity models
Recent work has shown the effectiveness of tensor network methods for computing large deviation func...
Recent work has shown the effectiveness of tensor network methods for computing large deviation func...
We introduce a general numerical method to compute dynamics and multi-time correlations of chains of...
We propose a new method for computing the ground state properties and the time evolution of infinite...
Recently developed methods to compute dynamics of strongly coupled non-Markovian open systems are ba...
We propose an environment recycling scheme to speed up a class of tensor network algorithms that pro...
The infinite time-evolving block decimation algorithm [G. Vidal, Phys. Rev. Lett. 98, 070201 (2007)]...
We present a matrix product state (MPS) algorithm to approximate ground states of translationally in...
We propose a formalism to study dynamical properties of a quantum many-body system in the thermodyna...
We introduce a numerical algorithm to simulate the time evolution of a matrix product state under a ...
We propose an efficient algorithm for simulating quantum many-body systems in two spatial dimensions...
We introduce a method based on matrix product states (MPS) for computing spectral functions of (quas...
An extension of the projected entangled-pair states (PEPS) algorithm to infinite systems, known as t...
Combining the time-dependent variational principle (TDVP) algorithm with the parallelization scheme ...
We show that the time-dependent variational principle provides a unifying framework for time-evoluti...
Recent work has shown the effectiveness of tensor network methods for computing large deviation func...
Recent work has shown the effectiveness of tensor network methods for computing large deviation func...
We introduce a general numerical method to compute dynamics and multi-time correlations of chains of...
We propose a new method for computing the ground state properties and the time evolution of infinite...
Recently developed methods to compute dynamics of strongly coupled non-Markovian open systems are ba...
We propose an environment recycling scheme to speed up a class of tensor network algorithms that pro...
The infinite time-evolving block decimation algorithm [G. Vidal, Phys. Rev. Lett. 98, 070201 (2007)]...
We present a matrix product state (MPS) algorithm to approximate ground states of translationally in...
We propose a formalism to study dynamical properties of a quantum many-body system in the thermodyna...
We introduce a numerical algorithm to simulate the time evolution of a matrix product state under a ...
We propose an efficient algorithm for simulating quantum many-body systems in two spatial dimensions...
We introduce a method based on matrix product states (MPS) for computing spectral functions of (quas...
An extension of the projected entangled-pair states (PEPS) algorithm to infinite systems, known as t...
Combining the time-dependent variational principle (TDVP) algorithm with the parallelization scheme ...
We show that the time-dependent variational principle provides a unifying framework for time-evoluti...
Recent work has shown the effectiveness of tensor network methods for computing large deviation func...
Recent work has shown the effectiveness of tensor network methods for computing large deviation func...
We introduce a general numerical method to compute dynamics and multi-time correlations of chains of...