Quantum Machine Learning in Materials Prediction: A Case Study on ABO₃ Perovskite Structures

Quantum machine learning (QML), MLon quantum computers,offersa promising approach for discovering and screening novel materials.This study introduces a hybrid classical-quantum ML method using avariational quantum classifier to identify simple perovskite structureswithin a data set of ABO(3) compounds. The model is trainedusing a data set of 397 known ABO(3) compounds, with 254perovskites and 143 non-perovskite structures labeled as +1 and -1,respectively. By considering feature correlation and eliminating lessimportant features, the QML system achieves an optimal accuracy of88% for training data and 87% for unseen test data. These resultsdemonstrate the potential of QML in materials science classificationtasks, even with limited training data, leveraging the intrinsic propertiesof quantum computation to enhance the investigation of materials.In addition, perspectives on QML applications in materials scienceare discussed.