论文标题
通过机器学习对四个Qubit的状态进行分类
Classification of four-qubit entangled states via Machine Learning
论文作者
论文摘要
我们应用支持向量机（SVM）算法来得出一组纠缠证人（EW），以识别四个Qubit State家族的纠缠模式。 SVM对实际EW实施的有效性源于对等效纠缠量子状态家族的粗糙描述。我们工作中的等效标准基于随机的本地操作和经典交流（SLOCC）分类以及对四个Qubited Wernand的Werner国家的描述。我们在数值上验证SVM方法提供了有效的工具来解决纠缠证人问题时，当可用的家族状态的粗粒细胞描述时。我们还讨论并证明了应用于四个Qubit的状态分类的非线性内核SVM方法的效率。
We apply the support vector machine (SVM) algorithm to derive a set of entanglement witnesses (EW) to identify entanglement patterns in families of four-qubit states. The effectiveness of SVM for practical EW implementations stems from the coarse-grained description of families of equivalent entangled quantum states. The equivalence criteria in our work is based on the stochastic local operations and classical communication (SLOCC) classification and the description of the four-qubit entangled Werner states. We numerically verify that the SVM approach provides an effective tool to address the entanglement witness problem when the coarse-grained description of a given family state is available. We also discuss and demonstrate the efficiency of nonlinear kernel SVM methods as applied to four-qubit entangled state classification.
