We have a new algorithmic approach for doing machine learning with quantum computers. We trained our qmodel for the ternary classification of the Iris flower dataset on IBM quantum computers. It reaches the accuracy level of classical ML.
PolyadicQML, our python library for definition, training and deployment of quantum models, is available on GitHub and ready for install on PyPI. Start your own QML project with a sample qmodel.
> pip install polyadicqml To get a technical grasp of the algorithm see our presentation at IBM Supercomputing virtual event. Otherwise a medium post is available for non-technical public. For more in deep details read the preprint of the research paper to be presented at QCE20.
