Predicting toxicity by quantum machine learning
- URL: http://arxiv.org/abs/2008.07715v3
- Date: Wed, 16 Dec 2020 02:34:45 GMT
- Title: Predicting toxicity by quantum machine learning
- Authors: Teppei Suzuki, Michio Katouda
- Abstract summary: We develop QML models for predicting the toxicity of 221 phenols on the basis of quantitative structure activity relationship.
Results suggest that our data encoding enhanced by quantum entanglement provided more expressive power than the previous ones.
- Score: 11.696069523681178
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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