Arif-PhyChem/MLQD
MLQD is a Python Package for Machine Learning-based Quantum Dissipative Dynamics
This package helps quantum chemists and physicists simulate quantum dissipative dynamics more efficiently. It takes in parameters describing a quantum system (like time, time step, and system type) and outputs the predicted quantum trajectory, specifically the reduced density matrix over time. Researchers in theoretical chemistry, condensed matter physics, and materials science can use this to quickly analyze how quantum systems evolve in complex environments.
No commits in the last 6 months. Available on PyPI.
Use this if you need to rapidly predict the evolution of open quantum systems without performing computationally expensive traditional quantum dynamics simulations.
Not ideal if you require highly precise, ab-initio level accuracy for every time step and are not open to machine learning approximations.
Stars
16
Forks
4
Language
Jupyter Notebook
License
—
Category
Last pushed
Sep 03, 2024
Commits (30d)
0
Dependencies
5
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Arif-PhyChem/MLQD"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
PennyLaneAI/pennylane
PennyLane is an open-source quantum software platform for quantum computing, quantum machine...
qiskit-community/qiskit-machine-learning
An open-source library built on Qiskit for quantum machine learning tasks at scale on quantum...
netket/netket
Machine learning algorithms for many-body quantum systems
tencent-quantum-lab/tensorcircuit
Tensor network based quantum software framework for the NISQ era
mit-han-lab/torchquantum
A PyTorch-based framework for Quantum Classical Simulation, Quantum Machine Learning, Quantum...