Realization of a quantum neural network by repeat-until-success circuits in a superconducting quantum processor*

We present the experimental realization of a quantum neural network capable of non-linear classification. By implementing control-flow feedback in a superconducting quantum processor, we synthesize non-linear activation functions using repeat-until-success circuits, demonstrating the elementary building blocks for quantum machine learning. In particular, we realize a quantum circuit that reproduces a variety of classical feed-forward neural network constructions and can learn from superpositions of training data.