Quantifying quantum entanglement via machine learning models
Communications in Theoretical Physics(2024)
摘要
Abstract Quantifying entanglement measures for quantum states with unknown density matrices is a chal lenging task. Machine learning offers a new perspective to address this problem. By training
machine learning models using experimentally measurable data, we can predict the target entan glement measures. In this study, we compare various machine learning models and find that the
linear regression and stack models perform better than others. We investigate the model’s impact
on quantum states across different dimensions and find that higher-dimensional quantum states
yield better results. Additionally, we investigate which measurable data has better predictive power
for target entanglement measures. Using correlation analysis and principal component analysis,
we demonstrate that quantum moments exhibit a stronger correlation with coherent information
among these data features.
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