Tensor network based quantum software framework for the NISQ era

Pytorch differentiable molecular dynamics

Quantum dynamics package based on tensor network states

The next-gen AI-native tensor-network-based quantum software framework

QuGIT is a numerical toolbox in Python for simulation of gaussian quantum states and their time evolution through unconditional and conditional dynamics

A tool to model the bath relaxation dynamics of open quantum systems by Lindblad and Hierarchical Equations of Motion (HEOM) approaches.

Projected time-dependent Variational Monte Carlo (p-tVMC) method based on infidelity optimization for variational simulation of quantum dynamics.

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.

Gutzwiller state for the Bose-Hubbard model, with complex and site-dependent coefficients

A Python package for numerical quantum mechanics of chain-like systems based on tensor trains

MQED-QD: A Python toolkit for computing dyadic Green's functions, dipole–dipole interactions, and open quantum dynamics in layered dielectric media.

Script for data generation and Code for our article "Predicting the future of excitation energy transfer in light-harvesting complex with artificial intelligence-based quantum dynamics"

Code and data for our paper in JPCL "One-Shot Trajectory Learning of Open Quantum Systems Dynamics" https://un5n689pgj7rc.julianrbryant.com/10.1021/acs.jpclett.2c01242

Coefficient based quantum dynamics.

Learning to Optimize for Rothe's method using LSTMs and n-SSMs

Quantum dynamics

Code to evolve the dynamics of excitons on a quantum dot lattice with KMC rates based on the polaron-transformed Redfield equation (PTRE).