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.
An open-source application for obtaining optimized many-body wavefunctions expressed by matrix product states (MPS). By using a second-generation density matrix renormalization group (DMRG) algorithm, many-body wave functions can be efficiently optimized. The quantum-chemical operators are represented by matrix product operators (MPOs), which provides flexibility to accommodate various symmetries and relativistic effects.
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.
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.
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.
An open-source application for quantum chemical calculation based on the density-matrix renormalization group (DMRG). For systems with a number of atomic orbitals, low-lying energy eigenvalues can be calculated in high accuracy of order of 1kcal/mol. This application is suitable especially to calculation of multi-orbital systems with one-dimensional topology such as chain-like or circular-like configuration of orbits.
GPU library for pdgemm and pzgemm, which are functions of matrix-matrix operations in ScaLAPACK.
peps-torch is a python library for calculation of quantum many-body problems on two dimensional lattices. Variational principles calculation is used with an infinite PEPS (iPEPS) as the trial wave function. Therefore, the ground state is obtained in the form of the element tensor of the iPEPS. The energy of the trial state is estimated by the corner transfer matrix method (CTM), and its gradient with respect to the element tensor is computed through automatic differentiation provided by pytorch. Functions/classes for exploiting the system’s symmetry are provided for reducing the computational cost if possible. While general models and lattices are not supported, many examples of stand-alone codes would make it relatively easy for users to write their own codes to suit their needs. pytorch is required.
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.
Automatic generation tool for codes of tensor contraction. This tool can automatically generate codes of an optimal computing sequence for construction of a single tensor from a tensor network composed of tensors. Netcon algorithm proposed by Pfeifer et al. is used, and it is possible to search optimal solution quickly. Generated codes are available in Numpy and mptensor in Python.