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

TL;DR

This paper presents a quantum pipeline combining autoencoder and classifier components to process real industrial data, demonstrating early practical applications of quantum computing in industry.

Contribution

It introduces one of the first real-world quantum pipelines applied to industrial data, integrating quantum autoencoder and classifier for data compression and labeling.

Findings

Successful implementation on actual industrial data

Demonstrates feasibility of quantum algorithms in industrial settings

Provides a foundation for future quantum industrial applications

Abstract

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.