  We propose a new technique for the automatic generation of optimal ad-hoc
ans\"atze for classification by using quantum support vector machine (QSVM).
This efficient method is based on NSGA-II multiobjective genetic algorithms
which allow both maximize the accuracy and minimize the ansatz size. It is
demonstrated the validity of the technique by a practical example with a
non-linear dataset, interpreting the resulting circuit and its outputs. We also
show other application fields of the technique that reinforce the validity of
the method, and a comparison with classical classifiers in order to understand
the advantages of using quantum machine learning.
