NCON

  • Level of openness 2 ★★☆
  • Document quality 1 ★☆☆

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.

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DDMRG

  • Level of openness 1 ★☆☆
  • Document quality 1 ★☆☆

DDMRG (DynamicalDMRG) is a program for analyzing the dynamical properties of one-dimensional electron systems by using the density matrix renormalization group method. It simulates excited or photo-induced quantum phenomena in Mott insulators, spin-Peierls materials, organic materials, etc. Parallel computational procedures for linear and non-linear responses in low dimensional electron systems and analyzing routines for relaxation processes of excited states induced by photo-irradiation are available.

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MateriApps Installer

  • Level of openness 3 ★★★
  • Document quality 2 ★★☆

A collection of shell scripts for installing open-source applications and tools for computational materials science to macOS, Linux PC, cluster workstations, and major supercomputer systems in Japan. Major applications are preinstalled to the nation-wide joint-use supercomputer system at Institute for Solid State Physics, University of Tokyo by using MateriApps Installer.

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ALPSCore

  • Level of openness 3 ★★★
  • Document quality 2 ★★☆

Open-source software for building computational physics applications. Common C++ auxiliary modules required for various methods in computational physics such as the quantum Monte Carlo method are prepared. This software helps to build reusable codes and to reduce development time for complex computational science applications. It also supports parallel programming based on MPI or OpenMP.

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ITensor

  • Level of openness 3 ★★★
  • Document quality 2 ★★☆

A C++ library for implementing a tensor product wavefunction method to simulate many-body electron systems. This library provides a useful environment for simple definition of tensors in programs, and supports functions of linear algebras and quantum number conservation needed in a tensor network method. This library keeps excellent flexibility and efficiency in maintenance, and can easily make a solver of one-dimensional electron systems such as density-matrix renormalization group (DMRG).

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Open Source MPS

  • Level of openness 2 ★★☆
  • Document quality 2 ★★☆

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.

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Uni10

  • Level of openness 3 ★★★
  • Document quality 2 ★★☆

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.

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TeNPy

  • Level of openness 3 ★★★
  • Document quality 2 ★★☆

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.

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mptensor

  • Level of openness 3 ★★★
  • Document quality 2 ★★☆

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.

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TensorNetwork

  • Level of openness 3 ★★★
  • Document quality 2 ★★☆

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.

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