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Michio Katouda

Publications and source records attributed to Michio Katouda.

2 recordsLinked to original sources
arXiv2023

Finite temperature effects on the structural stability of Si-doped HfO$_{2}$ using first-principles calculations

The structural stabilities of the monoclinic and tetragonal phases of Si-doped HfO$_{2}$ at finite temperatures were analyzed using a computational scheme to assess the effects of impurity doping. The finite temperature effects considered in this work represented lattice vibration and impurity configuration effects. The results show that 6% Si doping stabilizes the tetragonal phase at room temperature, although a higher concentration of Si is required to stabilize the tetragonal phase at zero temperature. These data indicate that lattice vibration and impurity configuration effects are important factors determining structural stability at finite temperatures.

cond-mat.mtrl-sci↗
arXiv2020

Predicting toxicity by quantum machine learning

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.

quant-ph↗