Tensor network based quantum software framework for the NISQ era
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Updated
Jun 13, 2024 - Python
Tensor network based quantum software framework for the NISQ era
Pytorch differentiable molecular dynamics
An efficient Julia framwork for Hierarchical Equations of Motion (HEOM) in open quantum systems
QuGIT is a numerical toolbox in Python for simulation of gaussian quantum states and their time evolution through unconditional and conditional dynamics
Quantum Toolbox in Julia
Type an M x M matrix for your open quantum system Hamiltonian, and give a spectral density (analytic or numerical). FeynDyn gives the density matrix dynamics according to the Leggett-Caldeira bath or the Feynman-Vernon bath at any temperature. Can do up to 16 qubits (65536 levels) and infinitely many bath modes. Email nike@hpqc.org for the lates…
Real-time TDDFT for Quantum-Espresso
A tool to model the bath relaxation dynamics of open quantum systems by Lindblad and Hierarchical Equations of Motion (HEOM) approaches.
This is a package developed in the Mesoscience Lab to perform Hierarchy of Pure State (HOPS) calculations using an adaptive basis that provides size-invariant scaling for large systems.
MLQD is a Python Package for Machine Learning-based Quantum Dissipative Dynamics
Numerical toolbox in MATLAB for Gaussian Quantum Information and time evolution of gaussian states through quantum Langevin and Lyapunov equations
A python package for Gaussian wave packet dynamics
General quantum dynamics code using curvilinear coordinates and a numerical kinetic energy operator (with Tnum) : (i) Vibrational levels, intensities for floppy molecular system (ii) Wave-packet propagation with or witout time dependant Hamiltonian (iii) Quantum gate and optimal control.
DynEMol: tools for studying Dynamics of Electrons in Molecules
Projected time-dependent Variational Monte Carlo (p-tVMC) method based on infidelity optimization for variational simulation of quantum dynamics.
A Python package for numerical quantum mechanics of chain-like systems based on tensor trains
Gutzwiller state for the Bose-Hubbard model, with complex and site-dependent coefficients
This module enables to use potentials extracted from the literature. It has the following features: * One or several degrees of freedom * One or several electronic states * For each electronic state, the energy, gradient and hessian can be obtained in the diabatic or adiabatic representations
C++ code to compute electron trajectories in molecular orbitals using spin-dependent quantum hydrodynamics
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