Influence Estimation In Multi-Step Process Chains Using Quantum Bayesian Networks

Abstract

Digital representatives of physical assets and process steps play a decisive role in analysing properties and evaluating the quality of the process. So-called digital twins acquire all relevant planning and process data, which provide the basis, for example, to investigate path accuracies in manufacturing. Each single process step aims to perform an ideal machining after the specification of a target geometry. However, the practical implementation of a step usually shows deviations from the targeted shape. The machine-learning based method of probabilistic Bayesian networks enables the quality estimation of the holistic process chain as well as improvements by targeted considerations of single steps and influence factors. However, the handling of large-scale Bayesian networks requires a high computational effort, whereas the processing with quantum algorithms holds potential improvements in storage and performance. Based on the issue of path accuracy, this paper considers the modelling and influence estimation for a milling operation including experiments on superconducting quantum hardware.