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QML: A Python Toolkit for Quantum Machine Learning

Build Status doi doi

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 Müller (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.