Variational Quantum Neural Network for Modeling and Solving Heat and Mass Transfer Problems Occurring in Electric Contact Phenomena

In this study we present a hybrid quantum-classical neural network (variational quantum Algorithm) to model and solve heat and mass transfer problems that occur in electrical contacts. The underlying Equations responsible for modeling diverse electric contact phenomena like arcing, bridging, welding etc. are Stefan Type Problems. These problems are nonlinear and hard to solve analytically. We build a Hybrid Quantum-Classical Neural Network for modeling and solving such kind Heat and Mass transfer problems occurring in Electric Contacts. We benefit from computational power of Quantum Computing to solve the Stefan Type problems and utilize neural network as a universal function approximator for the Heat function and Moving Phase boundary. On the basis of the Variational Quantum Neural Network, we have developed methodological framework and software artifact which might be of interest and beneficial for researchers and engineers working in the field of modeling electric contact phenomena. The proposed framework and artifact are beyond the field of modeling and solving Heat and Mass transfer problems occurring in electric contacts and can be applied in diverse phase transient phenomena such as tumor growth and spreading (biomedicine), material deformation and failure under stress (material science) etc. Methodology and source code for the framework is publicly available on our GitHub account https://github.com/Schrodinger-cat-kz/QNN-Framework-for-Electric-Contact-Phenomena.git.