Efficient quantum algorithm for the design of complex materials: quantum circuit learning

Validity of the quantum circuit learning (QCL) method for the prediction of physical properties of complex materials has been explored by comparing the prediction results of Vickers hardness of the high entropy alloys with those predicted by the conventional linear and nonlinear types of machine learning methods. The linear models include linear regression (LR), ridge regression (Ridge), Bayesian ridge regression (BR), and linear support vector regression (linear_SVR) for linear models, and the nonlinear models include Gaussian process regression (GBR), support vector regression with an radial basis function kernel (rbf_SVR), random forest (RF), gradient boosting decision trees (GBDT), and multilayer perceptron neural networks (MLP). Our current examinations revealed that the QCL can efficiently predict Vickers hardness of the high entropy alloys for outside applicability domains and extrapolations even with a small number of datasets, which implies that the QCL can be used for the design of new complex materials from an early stage of development with a small dataset.