Predicting toxicity by quantum machine learning

TL;DR

This paper demonstrates that quantum machine learning models, enhanced by quantum entanglement, can effectively predict chemical toxicity, outperforming classical regression methods and offering a promising approach for nonlinear regression in cheminformatics.

Contribution

The study introduces quantum machine learning models with entanglement-enhanced data encoding for toxicity prediction, showing improved performance over classical methods.

Findings

QML models outperform multiple linear regression in toxicity prediction.

Quantum entanglement enhances data encoding, increasing model expressiveness.

QML models are comparable to radial basis function networks with better generalization.

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 develop QML models 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. Our QML models performed significantly better than the multiple linear regression method. Furthermore, our simulations indicate that the QML models were comparable to those obtained using radial basis function networks, while improving the generalization performance. The present study implies that QML could be an alternative approach for nonlinear regression tasks such as cheminformatics.