  Quantum machine learning (QML) investigates how quantum phenomena can be
exploited in order to learn data in an alternative way, \textit{e.g.} by means
of a quantum computer. While recent results evidence that QML models can
potentially surpass their classical counterparts' performance in specific
tasks, quantum technology hardware is still unready to reach quantum advantage
in tasks of significant relevance to the broad scope of the computer science
community. Recent advances indicate that hybrid classical-quantum models can
readily attain competitive performances at low architecture complexities. Such
investigations are often carried out for image-processing tasks, and are
notably constrained to modelling \textit{raster images}, represented as a grid
of two-dimensional pixels. Here, we introduce vector-based representation of
sketch drawings as a test-bed for QML models. Such a lower-dimensional data
structure results handful to benchmark model's performance, particularly in
current transition times, where classical simulations of quantum circuits are
naturally limited in the number of qubits, and quantum hardware is not readily
available to perform large-scale experiments. We report some encouraging
results for primitive hybrid classical-quantum architectures, in a canonical
sketch recognition problem.
