Easy-to-use and fast Python library for simulation of quantum information and quantum many-body systems. It provides Tensor module for tensor network simulations and Matrix module for “exact” quantum simulations.
ALPS is a numerical simulation library for strongly correlated systems such as magnetic materials or correlated electrons. It contains typicalsolvers for strongly correlated systems: Monte Carlo methods, exact diagonalization, the density matrix renormalization group, etc. It can be used to calculate heat capacities, susceptibilities, magnetization processes in interacting spin systems, the density of states in strongly correlated electrons, etc. A highly efficient scheduler for parallel computing is another improvement.
※Related links are temporary changed due to the server maintenance for ALPS project.
Debian Live Linux System that contains OS, editors, materials science application software, visualization tools, etc. An environment needed to perform materials science simulations is provided as a one package. By booting up on VirtualBox virtual machine, one can start simulations, such as the first-principles calculation, molecular dynamics, quantum chemical calculation, lattice model calculation, etc, immediately.
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 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.
Automatic generation tool for codes of tensor contraction. This tool can automatically generate codes of an optimal computing sequence for construction of a single tensor from a tensor network composed of tensors. Netcon algorithm proposed by Pfeifer et al. is used, and it is possible to search optimal solution quickly. Generated codes are available in Numpy and mptensor in Python.
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.
An open source library for implementing tensor networks. It is developed based on TensorFlow and is designed to be easily used by experts in the field of machine learning as well as in the field of physics. In addition to TensorFlow, it includes wrappers for JAX, PyTorch, and Numpy.
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.
An open source C++ library designed for the development of tensor network algorithms. The goal of this library is to provide basic tensor operations with an easy-to-use interface, and it also provides a Network class that handles the graphical representation of networks. A wrapper for calling it from Python is also provided.