Supervised learning with quantum-enhanced feature spaces

The use of quantum computing for machine learning is among the most exciting prospective applications of quantum technologies. However, machine learning tasks are often complex and it is unclear whether quantum or classical algorithms will provide an advantage. Here we show that quantum computers can be used to enhance machine learning by building classifiers that exploit quantum feature spaces. We use two methods based on the variational quantum eigensolver and quantum kernel estimation. Both are trained on IBM Q 5-qubit processors and evaluated on a dataset designed to be hard for classical SVMs.