QML: A Python Toolkit for Quantum Machine Learning

QML is a Python2/3-compatible toolkit for representation learning of properties of molecules and solids.

Current list of contributors:

• Anders S. Christensen (University of Basel)
• Lars A. Bratholm (University of Bristol)
• Silvia Amabilino (University of Bristol)
• Jimmy C. Kromann (University of Basel)
• Felix A. Faber (University of Basel)
• Bing Huang (University of Basel)
• David R. Glowacki (University of Bristol)
• Alexandre Tkatchenko (University of Luxembourg)
• Klaus-Robert Muller (Technische Universitat Berlin/Korea University)
• O. Anatole von Lilienfeld (University of Basel)

1) Citing QML:

Until the preprint is available from arXiv, please cite this GitHub repository as:

AS Christensen, LA Bratholm, S Amabilino, JC Kromann, FA Faber, B Huang, GR Glowacki, A Tkatchenko, K.R. Muller, OA von Lilienfeld (2018) "QML: A Python Toolkit for Quantum Machine Learning" https://github.com/qmlcode/qml

2) Get help:

Documentation and installation instruction is found at: http://www.qmlcode.org/

3) License:

QML is freely available under the terms of the MIT license.