DCA++ is a software framework to solve correlated electron problems with modern quantum cluster methods. This code provides a state of the art implementation of the dynamical cluster approximation (DCA) and its DCA+ extension. As the cluster solvers, DCA++ provides the continuous-time auxiliary field QMC (CT-AUX) , the continuous-time hybridization expansion (CT-HYB) restricted to single-site problems, the high temperature series expansion (HTS) and the exact diagonalization(ED).
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.
A solver program for two dimensional quantum lattice model based on a projected entangled pair state wavefunction and the corner transfer matrix renormalization group method.
This works on a massively parallel machine because tensor operations are OpenMP/MPI parallelized.
Kω implements large-scale parallel computing of the shifted Krylov subspace method. Using Kω, dynamical correlation functions can be efficiently calculated. This application includes a mini-application for calculating dynamical correlation functions of quantum lattice models such as the Hubbard model, the Kondo model, and the Heisenberg model in combination with the quantum lattice solver of quantum many-body problems, HΦ.
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 application for numerical renormalization group calculations. This application can solve magnetic impurity problems described by the Kondo model and the Anderson model. Input files are prepared for typical impulity models. By modifying input files, one can study more general models of the magnetic impurity problems. A mathematica program for generation of input files are also included.
QuCumber is an open-source Python package that implements neural-network quantum state reconstruction of many-body wavefunctions from measurement data such as magnetic spin projections, orbital occupation number. Given a training dataset of measurements, QuCumber discovers the most likely quantum state compatible with the measurements by finding the optimal set of parameters of a restricted Boltzmann machine (RBM).
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Software framework for training a machine learning model to reproduce first-principles energies and then using the model to perform configurational sampling in disordered systems. It has been developed with an emphasis on multi-component solid-state systems such as metal and oxide alloys. At present, Quantum Espresso, VASP and OpenMX can be used as first-principles energy calculators, and aenet can be used to construct neural network potentials.
An open-source program package for numerical diagonalization based on the Lanczos method, specialized for spin chains with unit spin magnitude, S=1. This package, which uses another open-source program package, TITPACK, calculates eigenenergies and eigenvectors of ground states and low-lying excited states of spin chains with finite length. By the subspace partitioning method, both memory and cpu-time requirements are considerably reduced.
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.