Classification using quantum similarity learning

Similarity learning tries to learn a function that measures the likeness of two objects; once trained, the similarity measure can be used to classify new data by comparing them with existing data. We present a classifier whose similarity function takes the form of a parametrized quantum kernel. Our method treats binary and multiclass classification problems in a unified fashion. Numerical experiments from both classical simulations and tests on trapped-ion quantum computing hardware are presented.