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• W4313593847 endingPage "128454" @default.
• W4313593847 startingPage "128454" @default.
• W4313593847 abstract "Kernel logistic regression (KLR) is a powerful machine learning model for classification, which has wide applications in pattern recognition. However, classical KLR algorithm is computationally expensive when dealing with big data sets. Since quantum technique exhibits a computational advantages in tackling machine learning problems, we devise a quantum KLR algorithm. Specifically, our algorithm makes use of quantum inner product estimation to prepare the desired state and then performs quantum singular value transformation based on the block-encoding framework to obtain the optimal model parameters. It is theoretically demonstrated that our algorithm has an exponential speedup over its classical counterpart." @default.
• W4313593847 created "2023-01-06" @default.
• W4313593847 creator A5000629167 @default.
• W4313593847 creator A5026115118 @default.
• W4313593847 creator A5038727912 @default.
• W4313593847 date "2023-02-01" @default.
• W4313593847 modified "2023-10-16" @default.
• W4313593847 title "Quantum kernel logistic regression based Newton method" @default.
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• W4313593847 doi "https://doi.org/10.1016/j.physa.2023.128454" @default.
• W4313593847 hasPublicationYear "2023" @default.
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