  Quantum computing technologies are in the process of moving from academic
research to real industrial applications, with the first hints of quantum
advantage demonstrated in recent months. In these early practical uses of
quantum computers it is relevant to develop algorithms that are useful for
actual industrial processes. In this work we propose a quantum pipeline,
comprising a quantum autoencoder followed by a quantum classifier, which are
used to first compress and then label classical data coming from a separator,
i.e., a machine used in one of Eni's Oil Treatment Plants. This work represents
one of the first attempts to integrate quantum computing procedures in a
real-case scenario of an industrial pipeline, in particular using actual data
coming from physical machines, rather than pedagogical data from benchmark
datasets.
