Quantum computing for capacity checks of reinforced concrete sections

We explore the application of quantum-enhanced machine learning techniques for the structural analysis of reinforced concrete sections subjected to combined axial force and biaxial bending. A quantum support vector machine framework is developed and employed to approximate the limit state surface that defines the ultimate capacity of Reinforced Concrete cross-sections. Several quantum kernel architectures, namely Fidelity, Mercer-inspired, ZZ-Feature Map, and HE2, are implemented and tested on two representative geometries: a symmetric rectangular section and an asymmetric L-shaped one. Kernel performance is evaluated in terms of classification accuracy, generalization behavior, and decision boundary coherence, showing strengths and limitations of each quantum feature map. Analyses have shown that the Fidelity, Mercer Static, and single-layer HE2 kernels provide the most robust and geometrically sound decision surfaces, thus achieving high classification accuracy and stability. On the contrary, deeper or highly expressive circuits exhibit overfitting and reduced generalization. Despite the limited accuracy observed for some kernel configurations, the results prove that quantum kernels can effectively approximate capacity domains, particularly in symmetric cases. Hence, quantum-enhanced models represent a promising direction for efficient structural verification, even if the technology is still in its early stages.