EPR實驗的轉向引起了很多討論。評估任意兩個量子態之間的可操縱程度仍然非常困難。量子設定的最佳化需要無數次測量才能做出決定，但可用的運算資源和時間並不多。在本實驗中，我們利用量子電腦的量子特性，採用量子支援向量機 （QSVM）模型來解決此問題，可以快速處理高維度資料集，提高訓練速度和精確度。我們將不同資料大小的訓練資料放入同一個模型中，看看較大的資料大小是否 會影響預測結果。此外，我們還使用兩個不同的可觀測值來預測在不同的測量設定下結果是否會更加最佳化。這是因為量子態是否可操縱很大程度上取決於能否找到最佳的測量設置，也就是最佳化過程。
在這項工作中，我們期望透過識別大量資料中隱藏模式的機器學習能力來解決複雜的物理問題。在這裡，我們應用一種稱為支援向量機（SVM）的機器學習方法來為任何量子態提供有效的量子檢測協議。在先前的工作中，我們客製了SDP計算協 定來產生標籤的訓練資料集。此外，我們對密度矩陣進行了特徵工程，分為五個不 同的特徵來比較性能，並分析了兩種不同類型的特定量子態和隨機量子態，同時預測了在最佳化測量設定情況下的可操縱程度。
根據訓練有素的模型對編碼要識別的狀態的不同物理驅動特徵的響應，我們可以確定最有效的表徵，即愛麗絲轉向鮑勃的情況是否依賴於愛麗絲規則對齊的轉向橢球體，並消除偏差在轉向橢球體中不會影響轉向性能。

The steering in EPR experiments has attracted much discussion. It is still very difficult to evaluate the degree of steerable between any two quantum states. The optimization of the quantum setup requires an infinite number of measurements to decision, but there are not so many computational resources and time available. In this experiment, we use Quantum Support Vector Machine (QSVM) model to solve the problem by exploiting the quantum characteristics of quantum computer, which can quickly process the high-dimensional data set and improve the training speed and accuracy. We put training data of different data sizes into the same model to see if the larger data size affects the prediction results. In addition, we also use two different observables to predict whether the results will be more optimized under different measurement settings. This is because whether a quantum state is steerable or not depends greatly on whether the best measurement settings can be found, which is the optimization process.
In this work, we expect through the machine learning ability of identifying hidden patterns in a large amount of data to solve complex physics problems. Here, we apply a machine learning approach called Support Vector Machine (SVM) to provide an efficient quantum detection protocol for any quantum states. In previous work we have customized the SDP calculation protocol to generate labelled training data sets. In addition, we do feature engineering on the density matrix into five different features to compare the performance and analyzed two different types of specific quantum states and random quantum states, meanwhile predicting the degree of steerability when in situations of optimization measurement settings.
According to the responses of the well-trained models to the different physics driven features encoding the states to be recognized, we can identify the most efficient characterization whether Alice steering Bob situation is dependent on in terms of Alice’s regularly aligned steering ellipsoid and eliminate the skews in steering ellipsoid will not affect the steerability.

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