论文标题
自动化的机器学习可以通过断层图将绑定的纠缠状态分类
Automated Machine Learning can Classify Bound Entangled States with Tomograms
论文作者
论文摘要
对于总尺寸大于六的量子系统，阳性部分换位（PPT）标准足够，但不需要决定量子状态的不可分割性。在这里，我们提出了一种自动化的机器学习方法，将两个Qutrits的随机状态分类为可分离或使用足够的数据进行量子状态断层扫描的随机状态，而无需直接测量其纠缠。即使Peres-Horodecki标准失败，我们也可以成功地应用我们的框架。此外，我们还可以通过回归技术来估计纠缠的一般鲁棒性，并使用它来验证分类器。
For quantum systems with a total dimension greater than six, the positive partial transposition (PPT) criterion is sufficient but not necessary to decide the non-separability of quantum states. Here, we present an Automated Machine Learning approach to classify random states of two qutrits as separable or entangled using enough data to perform a quantum state tomography, without any direct measurement of its entanglement. We could successfully apply our framework even when the Peres-Horodecki criterion fails. In addition, we could also estimate the Generalized Robustness of Entanglement with regression techniques and use it to validate our classifiers.
