  The problem of determining whether a given quantum state is entangled lies at
the heart of quantum information processing, which is known to be an NP-hard
problem in general. Despite the proposed many methods such as the positive
partial transpose (PPT) criterion and the k-symmetric extendibility criterion
to tackle this problem in practice, none of them enables a general, effective
solution to the problem even for small dimensions. Explicitly, separable states
form a high-dimensional convex set, which exhibits a vastly complicated
structure. In this work, we build a new separability-entanglement classifier
underpinned by machine learning techniques. Our method outperforms the existing
methods in generic cases in terms of both speed and accuracy, opening up the
avenues to explore quantum entanglement via the machine learning approach.
