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arXiv:2510.12507 · Laser Physics

Detection of quantum information masking via machine learning

Sheng-Ao Mao, Lin Zhang, Bo Li

Published Oct 1, 2025• 0 citations
arXivHTMLPDF

Abstract

Abstract Recently, machine learning has been widely applied in the field of quantum information, notably in tasks such as entanglement detection, steering characterization, and nonlocality verification. However, few studies have focused on utilizing machine learning to detect quantum information masking (QIM). In this work, we investigate supervised machine learning for detecting QIM in both pure and mixed qubit states. For pure qubit states, we randomly generate the corresponding density matrices and train an XGBoost model to detect QIM. For mixed qubit states, we improve the XGBoost method by optimizing the selection of training samples. The experimental results demonstrate that our approach achieves higher classification accuracy. Furthermore, we analyze the area under the curve of the receiver operating characteristic curve for this method, which further confirms its classification performance.

DOI
10.1088/1555-6611/ae0df6

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