Quantum neural network autoencoder and classifier applied to an industrial case study
This work addresses the challenge of moving quantum computing from academic research to practical industrial applications, specifically for oil treatment processes, though it appears incremental as an early attempt.
The authors tackled the problem of applying quantum computing to industrial processes by proposing a quantum pipeline with an autoencoder and classifier for compressing and labeling classical data from an oil treatment plant, achieving one of the first integrations of quantum algorithms with real industrial data.
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.