Separability-entanglement classifier via machine learning

Publication Type:
Journal Article
Physical Review A, 2018, 98 (1)
Issue Date:
Full metadata record
© 2018 American Physical Society. The problem of determining whether a given quantum state is entangled lies at the heart of quantum information processing. Despite the many methods - such as the positive partial transpose criterion and the k-symmetric extendibility criterion - to tackle this problem, none of them enables a general, practical solution due to the problem's NP-hard complexity. Explicitly, separable states form a high-dimensional convex set of vastly complicated structures. In this work, we build a different separability-entanglement classifier underpinned by machine learning techniques. We use standard tools from machine learning to learn the entanglement feature of arbitrary given quantum states. We perform substantial numerical tests on two-qubit and two-qutrit systems, and the results indicate that our method can outperform the existing methods in generic cases in terms of both speed and accuracy. This opens up avenues to explore quantum entanglement via the machine learning approach.
Please use this identifier to cite or link to this item: