论文标题
量子神经网络自动编码器和分类器应用于工业案例研究
Quantum neural network autoencoder and classifier applied to an industrial case study
论文作者
论文摘要
量子计算技术正在从学术研究到实际工业应用的过程中，近几个月来证明了量子优势的第一批提示。在量子计算机的这些早期实际用途中，与开发对实际工业过程有用的算法有关。在这项工作中，我们提出了一条量子管道，其中包括量子自动编码器，然后是量子分类器，该量子分类器用于首先压缩，然后标记来自分离器的经典数据，即ENI的一家石油处理厂中使用的机器。这项工作代表了在工业管道的实际情况下集成量子计算过程的首次尝试之一，尤其是使用来自物理机器的实际数据，而不是来自基准数据集的教学数据。
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.
