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.
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.
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.
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.
A low-energy solver for a wide ranger of quantum lattice models (multi-orbital Hubbard model, Heisenberg model, Kondo-lattice model) by using variational Monte Carlo method. User can obtain high-accuracy wave functions for ground states of above models. Users flexibly choose the correlation factors in wavefunctions such as Gutzwiller, Jastrow, and doublon-holon binding factors and optimize more the ten thousand variational parameters. It is also possible to obtain the low-energy excited states by specifying the quantum number using the quantum number projection.
A python package for the tight-binding method. PythTB supports tight-binding calculations of electronic structures and Berry phase in various kinds of systems. Users can use ab initio parameters obtained by Wannier90.
Python wrapper to manage jobs for the ab initio Monte Carlo package TurboRVB. By combining with a workflow management application, TurboWorkflows, users can perform high-throughput calculations based on TurboRVB.
Server for computing exact ground state of Ising model with random interacitons (Ising spin glasses). Users can specify the distributions of the interactions and the geometry of lattices. By inputting the informaiont of the model, users will receive the computational results by e-mail from the server.
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.