A package for the auxiliary field Quantum Monte Carlo method, which enables us to calculate finite-temperature properties of the Hubbard-type model. It is also possible to treat the Hubbard model coupled to a transversed Ising field. Many examples such as Hubbard model on the square lattice and the honeycomb lattice are provided in the documentation.
RESPACK is a first-principles calculation software for evaluating the interaction parameters of materials. It is able to calculate the maximally localized Wannier functions, the RPA response functions, and frequency-dependent electronic interaction parameters. RESPACK receives its input data from a band calculation using norm-conserving pseudopotentials with plane-wave basis sets. Utilities which convert a result of xTAPP or Quantum ESPRESSO to an input for RESPACK are prepared. The software has been used successfully for a wide range of materials such as metals, semiconductors, transition-metal compounds, and organic compounds. It supports OpenMP / MPI parallelization.
TC++ is open-source software for ab initio calculations using the transcorrelated (TC) method. In TC++, users can take account of electron correlations in a Jastrow correlation factor based on the TC method. Electronic structures obtained by Quantum ESPRESSO can be used as an initial state of TC++.
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.
DiracQ is a Mathematica nodebook for calculating commutation relations, which frequently appear in the quantum mechanics. DiracQ can treat canonical operators (canonical momentum and canonical position operators), Fermion operators, and Boson operators.
An exact diagonalization package for efficiently solving quantum spin 1/2 lattice models in almost fully spin-polarized sectors. QS3 can treat such systems with quite large system sizes, over 1000 sites. It supports calculations of wavenumber-dependence of energy-dispersion and dynamical spin structure factor.
An open-source application for simulation based on the density-matrix renormalization group (DMRG). This application can perform high-speed calculation of low-dimensional quantum systems with high accuracy. It implements generic programming techniques in the C++ language, and can easily extend simulation to new models and geometries. It is developed putting emphasis on user-friendly interfaces and low dependences on environments.
A tool for performing quantum many-body simulations based on dynamical mean-field theory. In addition to predefined models, one can construct and solve an ab-initio tight-binding model by using wannier 90 or RESPACK. We provide a post-processing tool for computing physical quantities such as the density of state and the momentum resolved spectral function. DCore depends on external libraries such as TRIQS and ALPSCore.
An exact diagonalization package for a wide range of quantum lattice models (e.g. multi-orbital Hubbard model, Heisenberg model, Kondo lattice model). HΦ also supports the massively parallel computations. The Lanczos algorithm for obtaining the ground state and thermal pure quantum state method for finite-temperature calculations are implemented. In addition, dynamical Green’s functions can be calculated using Kω, which is a library of the shifted Krylov subspace method. It is possible to perform simulations for real-time evolution from ver. 3.0.
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.