A hybrid physics-informed neural network (HPINN) is proposed and tested for the solution of academic examples in mechanics and piezoelasticity. In the proposed scheme quantum neural network layers are embedded in a classical PINN, with the aim of accelerating the learning process. The HPINN is applied to academic one-dimensional elastic rod benchmarks and piezoelectric rod problems. Two hybrid architectures are introduced. It is shown that hybrid architecture converges faster across multiple configurations and for the tested architecture, the performance gain becomes apparent as the number of qubits in the quantum layer width increases. Piezoelectric materials, shunted with suitably designed circuits, provide a passive damping control technique. Therefore the approximation of relevant direct and inverse problems are of importance for various applications. This work builds upon previous efforts of our group in the area of PINNs [1] and Quantum Neural networks [2] for the approximation of shunted ultiphysics piezoelectric problems for vibration suppression [3]. In particular an open access quantum computing educational platform is provided for experimental evaluation of the proposed scheme [4]. REFERENCES [1] Stavroulakis G.E., Mouratidou A.D. & Drosopoulos G.A., 2025. Physics-Informed Neural Networks. In: Pardalos, P.M., Prokopyev, O.A. (eds) Encyclopedia of Optimization. Springer, Cham. https://doi.org/10.1007/978-3-030-54621-2_907-1. [2] Schetakis, N., Aghamalyan, D., Griffin, P. & Boguslavsky, 2022. M. Review of some existing qml frameworks and novel hybrid classical-quantum neural networks realizing binary classification for noisy datasets. Scientific Reports 12, 11927. https://doi.org/10.1038/s41598-022-14876-6 [3] Mouratidou, A.D., Daraki, MS., Drosopoulos, G.A. et al. 2026. Physics-informed neural networks for shunted piezoelectric systems. Acta Mech https://doi.org/10.1007/s00707-025-04619-9 [4] Bonfini, P., Papoutsakis, N., Litsas, A., Schetakis, N., &Di Iorio, A. (2025). Quantum Machine Learning (QML) Arena (1.0). Zenodo. https://zenodo.org/records/15796747