Practical application of quantum neural network to materials informatics
Abstract Quantum neural network (QNN) models have received increasing attention owing to their strong expressibility and resistance to overfitting. It is particularly useful when the size of the training data is small, making it a good fit for materials informatics (MI) problems. However, there are...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-04-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-59276-0 |
| _version_ | 1797209387908988928 |
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| author | Hirotoshi Hirai |
| author_facet | Hirotoshi Hirai |
| author_sort | Hirotoshi Hirai |
| collection | DOAJ |
| description | Abstract Quantum neural network (QNN) models have received increasing attention owing to their strong expressibility and resistance to overfitting. It is particularly useful when the size of the training data is small, making it a good fit for materials informatics (MI) problems. However, there are only a few examples of the application of QNN to multivariate regression models, and little is known about how these models are constructed. This study aims to construct a QNN model to predict the melting points of metal oxides as an example of a multivariate regression task for the MI problem. Different architectures (encoding methods and entangler arrangements) are explored to create an effective QNN model. Shallow-depth ansatzs could achieve sufficient expressibility using sufficiently entangled circuits. The “linear” entangler was adequate for providing the necessary entanglement. The expressibility of the QNN model could be further improved by increasing the circuit width. The generalization performance could also be improved, outperforming the classical NN model. No overfitting was observed in the QNN models with a well-designed encoder. These findings suggest that QNN can be a useful tool for MI. |
| first_indexed | 2024-04-24T09:53:54Z |
| format | Article |
| id | doaj.art-09512e173a12410f8513ba041e2c21f3 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-24T09:53:54Z |
| publishDate | 2024-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-09512e173a12410f8513ba041e2c21f32024-04-14T11:12:55ZengNature PortfolioScientific Reports2045-23222024-04-011411910.1038/s41598-024-59276-0Practical application of quantum neural network to materials informaticsHirotoshi Hirai0Toyota Central R&D Labs., Inc.Abstract Quantum neural network (QNN) models have received increasing attention owing to their strong expressibility and resistance to overfitting. It is particularly useful when the size of the training data is small, making it a good fit for materials informatics (MI) problems. However, there are only a few examples of the application of QNN to multivariate regression models, and little is known about how these models are constructed. This study aims to construct a QNN model to predict the melting points of metal oxides as an example of a multivariate regression task for the MI problem. Different architectures (encoding methods and entangler arrangements) are explored to create an effective QNN model. Shallow-depth ansatzs could achieve sufficient expressibility using sufficiently entangled circuits. The “linear” entangler was adequate for providing the necessary entanglement. The expressibility of the QNN model could be further improved by increasing the circuit width. The generalization performance could also be improved, outperforming the classical NN model. No overfitting was observed in the QNN models with a well-designed encoder. These findings suggest that QNN can be a useful tool for MI.https://doi.org/10.1038/s41598-024-59276-0 |
| spellingShingle | Hirotoshi Hirai Practical application of quantum neural network to materials informatics Scientific Reports |
| title | Practical application of quantum neural network to materials informatics |
| title_full | Practical application of quantum neural network to materials informatics |
| title_fullStr | Practical application of quantum neural network to materials informatics |
| title_full_unstemmed | Practical application of quantum neural network to materials informatics |
| title_short | Practical application of quantum neural network to materials informatics |
| title_sort | practical application of quantum neural network to materials informatics |
| url | https://doi.org/10.1038/s41598-024-59276-0 |
| work_keys_str_mv | AT hirotoshihirai practicalapplicationofquantumneuralnetworktomaterialsinformatics |