A program package for numerically solving effective lattice models using matrix product states (MPS). The ground state of a one-dimensional quantum system and its time evolution can be numerically evaluated by using an infinite system algorithm based on MPS. Useful tutorials and examples of calculations are also provided.
Parallel C++ Library for tensor network methods. This library provides common operations, including tensor contraction and singular value decomposition and supports a similar interface as Numpy and Scipy in Python.
A MATLAB function for the contraction process of a tensor network. It takes as input a tensor network and a contraction sequence describing how to contract the network to a single tensor or number. It returns a single tensor or number as output. This function can be obtained by downloading the preprint source.
NetKet is an open-source project delivering cutting-edge methods for the study of many-body quantum systems with artificial neural networks and machine learning techniques. Users can perform machine learning algorithms to find the ground-state of many-body Hamiltonians such as supervised learning of a given state and optimization of neural network states by using the variational Monte Carlo method.
An open-source application for simulation of one-dimensional interacting electron models based on a tensor product wavefunction method. This application supports not only electronic models but also spin and bosonic models, and can evaluate various physical quantities for ground states and low-lying excited states. This application also supports time evolution, and can treat models with long-range interactions.
peps-torch is a python library for calculation of quantum many-body problems on two dimensional lattices. Variational principles calculation is used with an infinite PEPS (iPEPS) as the trial wave function. Therefore, the ground state is obtained in the form of the element tensor of the iPEPS. The energy of the trial state is estimated by the corner transfer matrix method (CTM), and its gradient with respect to the element tensor is computed through automatic differentiation provided by pytorch. Functions/classes for exploiting the system’s symmetry are provided for reducing the computational cost if possible. While general models and lattices are not supported, many examples of stand-alone codes would make it relatively easy for users to write their own codes to suit their needs. pytorch is required.
Python-based simulations of chemistry framework (PySCF) is a general-purpose electronic structure platform written in Python. Users can perform mean-field and post-mean-field methods with standard Gaussian basis functions. This package also provides several interfaces to other software such as BLOCK and Libxc.
An open-source application for obtaining optimized many-body wavefunctions expressed by matrix product states (MPS). By using a second-generation density matrix renormalization group (DMRG) algorithm, many-body wave functions can be efficiently optimized. The quantum-chemical operators are represented by matrix product operators (MPOs), which provides flexibility to accommodate various symmetries and relativistic effects.
A solver program for two dimensional quantum lattice model based on a projected entangled pair state wavefunction and the corner transfer matrix renormalization group method.
This works on a massively parallel machine because tensor operations are OpenMP/MPI parallelized.
A Python library for simulating strongly correlated quantum systems using tensor networks. The goal is to make the algorithms readable and easy to use for beginners, and also powerful and fast for experts. Simple sample code and toy code to illustrate TEBD and DMRG are also provided.