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Quantum computing technologies are in the process of moving from academicresearch to real industrial applications, with the first hints of quantumadvantage demonstrated in recent months. In these early practical uses ofquantum computers it is relevant to develop algorithms that are useful foractual industrial processes. In this work we propose a quantum pipeline,comprising a quantum autoencoder followed by a quantum classifier, which areused 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 representsone of the first attempts to integrate quantum computing procedures in areal-case scenario of an industrial pipeline, in particular using actual datacoming from physical machines, rather than pedagogical data from benchmarkdatasets.

Quantum neural network autoencoder and classifier applied to an industrial case study