Strawberry Fields

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

Python library for the design, simulation, and optimization of continuous-variable quantum optical circuits. It has high-level functions for solving problems including graph and network optimization, machine learning, and chemistry, and can perform training and optimization of quantum programs using the TensorFlow backend.

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SMASH

  • Level of openness 3 ★★★
  • Document quality 1 ★☆☆

Open source software for massively parallel quantum chemistry calculations. Energies and geometries of nano-sized molecules can be calculated without fragmentation. The program supports Hartree-Fock, density functional theory, and second-order Møller-Plesset perturbation theory calculations. The input format, execution method, and program structure are simple, and frequently used routines can be easily extracted.

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SHRY

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

A python tool for generating symmetry-inequivalent supercell structures from a CIF file containing site occupancy information. SHRY can be used as a command-line tool as well as a module in a python script.

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snake-dmrg

  • Level of openness 3 ★★★
  • Document quality 1 ★☆☆

An open-source application for simulation of low-dimensional interacting electron models based on density-matrix renormalization group (DMRG). For effective models of one-dimensional quantum systems and impurity systems, this application can treat not only physical quantities of ground states but also time evolution and finite-temperature physical quantities. The program is coded in C++, and can be called from MATLAB scripts.

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SIMPLE-NN

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

Software package to implement Behler-Parinello neural network potentials. Potentials can be trained from structure-energy/ interatomic forces/stress data, and molecular dynamics calculations using LAMMPS can also be performed using learned potentials. A prediction uncertainty measure can also be calculated simultaneously.

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SPINPACK

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

A free software library for numerical diagonalization of quantum spin systems. Although the programs are based on TITPACK, they have been completely rewritten in C/C++ and several extensions have been added. It can handle, for example, the Heisenberg model, the Hubbard model, and the t-J model. This library supports dimension reduction of matrices exploiting symmetries, and it can run in parallel computing environments.

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SpM

  • Level of openness 3 ★★★
  • Document quality 1 ★☆☆

A sparse-modeling tool for computing the spectral function from the imaginary-time Green function. It removes statistical errors in quantum Monte Carlo data, and performs a stable analytical continuation. The obtained spectral function fulfills the non-negativity and the sum rule. The computation is fast and free from tuning parameters.

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SPRKKR

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

A open-source application of first-principles calculation for the electronic structure, using the KKR method, a variant of Green’s function method. It is based on the density functional theory and is applicable to crystals and surfaces. The coherent potential approximation (CPA) is adopted, so it can handle not only periodic systems, but also disordered alloys. It can also handle spin-orbit interaction and non-collinear magnetism.

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Superconducting Toolkit (sctk)

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

An open-source application for evaluating superconducting gaps from resutls of the first-principles calculation by Quantum ESPRESSO. By calculating electron-phonon interaction and screened Coulomb interaction from the first-principles calculation, superconducting gaps can be obtained from the gap equation. Quasiparticle densities of states and ultrasonic attenuation rates can also be calculated.

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scikit-learn

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

An open-source library for data mining and data analysis. This package implements various methods of machine learning such as supervised learning (data classification, data regression, etc.), unsupervised learning (data clustering, etc.), and data pre-processing. This package is implemented on Python numerical libraries, NumPy and Scipy, and supports parallel computation.

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