Optimal normalization in quantum-classical hybrid models for anti-cancer drug response prediction
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915287988699136 |
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| author | Ito, Takafumi Artem, Lysenko Tsunoda, Tatsuhiko |
| author_facet | Ito, Takafumi Artem, Lysenko Tsunoda, Tatsuhiko |
| contents | Quantum-classical Hybrid Machine Learning (QHML) models are recognized for their robust performance and high generalization ability even for relatively small datasets. These qualities offer unique advantages for anti-cancer drug response prediction, where the number of available samples is typically small. However, such hybrid models appear to be very sensitive to the data encoding used at the interface of a neural network and a quantum circuit, with suboptimal choices leading to stability issues. To address this problem, we propose a novel strategy that uses a normalization function based on a moderated gradient version of the $\tanh$. This method transforms the outputs of the neural networks without concentrating them at the extreme value ranges. Our idea was evaluated on a dataset of gene expression and drug response measurements for various cancer cell lines, where we compared the prediction performance of a classical deep learning model and several QHML models. These results confirmed that QHML performed better than the classical models when data was optimally normalized. This study opens up new possibilities for biomedical data analysis using quantum computers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_10037 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimal normalization in quantum-classical hybrid models for anti-cancer drug response prediction Ito, Takafumi Artem, Lysenko Tsunoda, Tatsuhiko Machine Learning Artificial Intelligence Emerging Technologies Quantum Physics Quantum-classical Hybrid Machine Learning (QHML) models are recognized for their robust performance and high generalization ability even for relatively small datasets. These qualities offer unique advantages for anti-cancer drug response prediction, where the number of available samples is typically small. However, such hybrid models appear to be very sensitive to the data encoding used at the interface of a neural network and a quantum circuit, with suboptimal choices leading to stability issues. To address this problem, we propose a novel strategy that uses a normalization function based on a moderated gradient version of the $\tanh$. This method transforms the outputs of the neural networks without concentrating them at the extreme value ranges. Our idea was evaluated on a dataset of gene expression and drug response measurements for various cancer cell lines, where we compared the prediction performance of a classical deep learning model and several QHML models. These results confirmed that QHML performed better than the classical models when data was optimally normalized. This study opens up new possibilities for biomedical data analysis using quantum computers. |
| title | Optimal normalization in quantum-classical hybrid models for anti-cancer drug response prediction |
| topic | Machine Learning Artificial Intelligence Emerging Technologies Quantum Physics |
| url | https://arxiv.org/abs/2505.10037 |