  Supervised quantum learning is an emergent multidisciplinary domain bridging
between variational quantum algorithms and classical machine learning. Here, we
study experimentally a hybrid classifier model accelerated by a quantum
simulator - a linear array of four superconducting transmon artificial atoms -
trained to solve multilabel classification and image recognition problems. We
train a quantum circuit on simple binary and multi-label tasks, achieving
classification accuracy around 95%, and a hybrid model with data re-uploading
with accuracy around 90% when recognizing handwritten decimal digits. Finally,
we analyze the inference time in experimental conditions and compare the
performance of the studied quantum model with known classical solutions.
