Metadata-Version: 2.1
Name: qmlearn
Version: 0.0.1rc1
Summary: QMLearn
Home-page: http://qmlearn.rutgers.edu
Author: Pavanello Research Group
Author-email: m.pavanello@rutgers.edu
License: MIT
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Physics
Provides-Extra: all
License-File: LICENSE

# QMLearn

*Quantum Machine Learning* by learning one-body reduced density matrices in the AO basis.

![QM-Learn code and workflow](./Docs/static/figure3.png)

# Contributors
 - Xuecheng Shao, Lukas Paetow, Md Rajib Khan Musa, Jessica A. Martinez B. and Michele Pavanello @ [PRG](https://sites.rutgers.edu/prg/) at [Rutgers University-Newark](http://sasn.rutgers.edu).
 - Mark E Tuckerman @ [Tuckerman Research Group](https://wp.nyu.edu/tuckerman_group/) at [NYU](https://cas.nyu.edu/)

# Some info

 Code entirely in Python leveraging [PySCF](https://pyscf.org/) and [Psi4Numpy](https://github.com/psi4/psi4numpy) for generating GTO integrals and DFT targets. Regressions are carried out with [scikit-learn](https://scikit-learn.org/stable/) or other ML tools.


