Quantum kernel logistic regression based Newton method

Tong Ning, Youlong Yang and Zhenye Du

Physica A: Statistical Mechanics and its Applications, 2023, vol. 611, issue C

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.

Keywords: Kernel logistic regression; Quantum algorithm; Block-encoding; Exponential speedup (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:611:y:2023:i:c:s0378437123000092

DOI: 10.1016/j.physa.2023.128454

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