Einstein-Podolsky-Rosen (EPR) steering is a kind of powerful nonlocal quantum resource in quantum information processing such as quantum cryptography and quantum communication. Many criteria have been proposed in the past few years to detect the steerability both analytically and numerically. Supervised machine learning such as support vector machines and neural networks have also been trained to detect the EPR steerability. To implement supervised machine learning, one needs a lot of labeled quantum states by using the semidefinite programming, which is very time consuming. We present a semisupervised support vector machine method which only uses a small portion of labeled quantum states in detecting quantum steering. We show that our approach can significantly improve the accuracies by detailed examples.

中文翻译：

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基于半监督机器学习的 Einstein-Podolsky-Rosen 转向

爱因斯坦-波多尔斯基-罗森（EPR）转向是量子密码学、量子通信等量子信息处理中一种强大的非局域量子资源。在过去的几年中，已经提出了许多标准来通过分析和数值检测可操纵性。支持向量机和神经网络等监督机器学习也被训练来检测 EPR 可操纵性。为了实现有监督的机器学习，需要使用半定规划的大量标记量子态，这是非常耗时的。我们提出了一种半监督支持向量机方法，该方法仅使用一小部分标记的量子态来检测量子转向。我们通过详细的例子表明我们的方法可以显着提高准确性。