通过量子机器学习预测毒性
Predicting toxicity by quantum machine learning
摘要：
近年来，参数化的量子线路已被视为混合量子-经典方法框架内的机器学习模型。量子机器学习（QML）已被应用于二进制分类问题和无监督学习。然而，实际的量子应用到非线性回归任务得到了相当少的关注。本文基于定量构效关系建立了221种酚类化合物毒性预测的QML模型。结果表明，我们的数据编码增强的量子纠缠提供了更多的表达能力比以前的，这意味着量子关联可能是有益的经典数据的特征映射表示。我们的QML模型的表现明显优于多元线性回归方法。此外，我们的模拟表明，QML模型与使用径向基函数网络获得的模型相当，同时提高了泛化性能。目前的研究表明，QML可能是一种替代方法的非线性回归任务，如化学信息学。
英文摘要：
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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