Predicting toxicity by quantum machine learning

Abstract

In recent years, parameterized quantum circuits have been regarded as machine learning models within the framework of the hybrid quantum-classical approach. Quantum machine learning (QML) has been applied to binary classification problems and unsupervised learning. However, practical quantum application to nonlinear regression tasks has received considerably less attention. Here, we developQMLmodels designed for predicting the toxicity of 221 phenols on the basis of quantitative structure activity relationship. The results suggest that our data encoding enhanced by quantum entanglement provided more expressive power than the previous ones, implying that quantum correlation could be beneficial for the feature map representation of classical data. OurQMLmodels performed significantly better than the multiple linear regression method. Furthermore, our simulations indicate that theQMLmodels were comparable to those obtained using radial basis function networks, while improving the generalization performance. The present study implies that QMLcould be an alternative approach for nonlinear regression tasks such as cheminformatics.