  We address the problem of facial expression recognition and show a possible
solution using a quantum machine learning approach. In order to define an
efficient classifier for a given dataset, our approach substantially exploits
quantum interference. By representing face expressions via graphs, we define a
classifier as a quantum circuit that manipulates the graphs adjacency matrices
encoded into the amplitudes of some appropriately defined quantum states. We
discuss the accuracy of the quantum classifier evaluated on the quantum
simulator available on the IBM Quantum Experience cloud platform, and compare
it with the accuracy of one of the best classical classifier.
