论文标题
使用机器学习对纠缠的一般分类
General Classification of Entanglement Using Machine Learning
论文作者
论文摘要
引入了量子系统中的多部分纠缠的分类，用于纯状态和混合状态。该分类基于上述纠缠对部分痕量操作的稳健性。然后，我们使用当前的机器学习和深度学习技术来自动对两个，三个和四个量子位的随机状态进行分类，而无需计算每次运行中不同类型的纠缠量的数量；相反，这仅在学习过程中完成。对于纯状态，该技术显示出较高，几乎完美的准确性。正如预期的那样，当与混合国家打交道时，这种准确性或多或少会下降。
A classification of multipartite entanglement in qubit systems is introduced for pure and mixed states. The classification is based on the robustness of the said entanglement against partial trace operation. Then we use current machine learning and deep learning techniques to automatically classify a random state of two, three and four qubits without the need to compute the amount of the different types of entanglement in each run; rather this is done only in the learning process. The technique shows high, near perfect, accuracy in the case of pure states. As expected, this accuracy drops, more or less, when dealing with mixed states and when increasing the number of parties involved.
