近年來，EPR 操縱性在量子資訊中已經引起越來越多的研究興趣. 儘管已經
提出了許多量子操縱性標準，但對於任意的兩個量子位元狀態， 想要評估可操
縱性的程度仍然相當困難。另一方面，隨著人工智慧技術快速的發展，人們可
以利用它的能力識別大量數據中的隱藏模式來解決複雜的物理問題。在這裡我
們使用深度學習的模型來探索量子操縱性測量設定的層次結構。這邊設計了預
先過濾器和半正定規劃迭代來生成訓練資料集的標籤。我們應用訓練好的深度
學習模型來分析三種不同類型的特殊量子態和隨機量子態，同時預測量子操縱
性測量設定的層次結構和可操縱性與不可操縱性之間的邊界。此外，我們還將
密度矩陣轉換五種不同的特徵，並使用這些不同特徵訓練模型來比較性能。而
我們的結果也進一步深入了解了量子操縱性的層次結構。

In recent years, EPR steering has attracted increasing research interest in the
community of quantum information. Although numerous quantum steering criteria have been proposed, it remains quite difficult to assess the degree of steerability even for an arbitrary two-qubit state. On the other hand, with the extensive development of the technology of artificial intelligence, people have harnessed its capability in the recognition of hidden pattern within a huge amount of data to solve complex physical problems. Here we leverage the power of the deep learning model to explore the hierarchy of steering measurement setting. A pre-filter and semidefinite programming iteration procedure is designed to generate the labelled training data set. We apply the well-trained deep learning model to analyze three different types of specific quantum states and random quantum states, meanwhile predicting the hierarchy of steering measurement setting and the boundary between steerability and unsteerability. In addition, we also encode the density matrices into five different features and compare the performance of the models trained with the different features. Our results provide further insight into the the hierarchical structure of quantum steering.

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