Quantum-Inspired Chemical Rule for Discovering Topological Materials

Topological materials exhibit unique electronic structures that underpin both fundamental quantum phenomena and next-generation technologies, yet their discovery remains constrained by the high computational cost of first-principles calculations and the slow, resource-intensive nature of experimental synthesis. Recent machine-learning approaches, such as the heuristic topogivity rule, offer a data-driven prescreening tool by quantifying each element's intrinsic tendency toward topological behavior. Here, we develop a hybrid quantum-classical neural network (HQCNN) that extends this rule into a quantum-inspired formulation. Within this framework, the HQCNN maps compositional descriptors to quantum probability amplitudes, naturally introducing pairwise interelement correlations inaccessible to classical heuristics. The physical validity of these correlations is substantiated by constructing an equivalent complex-valued neural network (CVNN), confirming both the consistency and interpretability of the formulation. Retaining the simplicity of chemical reasoning while embedding quantum-native features, our quantum-inspired rule enables efficient and generalizable topological classification. High-throughput screening combined with first-principles (DFT) validation reveals five previously unreported topological compounds, demonstrating the enhanced predictive power and physical insight afforded by quantum-inspired heuristics.