Machine learning algorithms for many-body quantum systems
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Updated
Nov 24, 2024 - Python
Machine learning algorithms for many-body quantum systems
Quantum Lattice Model Simulator Package
Basics on Exact Diagonalization
Exact diagonalization, Lehmann's representation, Two-particle Green's functions
Lanczos diagonalization of a Heisenberg-like Hamiltonian in Julia.
Exact diagonalization library for general models
Random Integrators for many-body quantum systems
A package to simplify working with symmetry-adapted quantum many-body bases. Provides a good foundation for writing custom exact diagonalization and variational Monte Carlo software
C++ library for Exact Diagonalization of quantum many-body systems
A Julia code for performing exact diagonalization of fractional quantum Hall systems
An Exact Diagonalization Code for the 1D & 2D Hubbard Model
A quantum operator algebra domain-specific language and exact diagonalization toolkit for C++11/14/17
User-friendly exact diagonalization package written in Haskell. Can treat systems of up to 𝒪(42) spins!
Exact diagonalization for finite quantum systems
Exact Diagonalization for Hubbard model/Tight-binding model by MatheMatica
Code for exact diagonalization of BoseHubbard hamiltonian
Equilibrium ED solver for finite fermionic models that can compute Keldysh Green's functions
Quick and dirty TRIQS wrapper around the Pomerol exact diagonalization library
This calculates the minimum eigenvalue in the Hubbard model with the use of the exact diagonalization method.
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