A Proposal for Physics-Informed Quantum Graph Neural Networks for Simulating Laser Cutting Processes

Mehrin Ruhi, ZuranaStein, HannahMaaß, WolfgangKlein, MaikeKrupka, DanielWinter, CorneliaWohlgemuth, Volker2023-11-292023-11-292023978-3-88579-731-9https://dl.gi.de/handle/20.500.12116/43111Simulations are crucial for production monitoring and planning in manufacturing. Still, the performance of simulations based on mathematical modeling and machine learning methods is limited and opaque to widespread application. Quantum computing offers the potential for exponential acceleration of these tools, while physically informed neural networks (PINN) improve learning and reduce ambiguity. Objective of this paper is to explore the concept of developing a tool for laser cutting simulation based on a quantum neural network that can be trained on thermal physics principles.enQuantum ComputingPINNGraph Neural NetworkPredictive SimulationA Proposal for Physics-Informed Quantum Graph Neural Networks for Simulating Laser Cutting ProcessesText/Conference Paper10.18420/inf2023_1831617-5468