  We introduce here a supervised quantum machine learning algorithm for
multi-class classification on NISQ architectures. A parametric quantum circuit
is trained to output a specific bit string corresponding to the class of the
input datapoint. We train and test it on an IBMq 5-qubit quantum computer and
the algorithm shows good accuracy --compared to a classical machine learning
model-- for ternary classification of the Iris dataset and an extension of the
XOR problem. Furthermore, we evaluate with simulations how the algorithm fares
for a binary and a quaternary classification on resp. a known binary dataset
and a synthetic dataset.
