Quantum entanglement meets machine learning
Entanglement is one of the key features of quantum mechanics, which allows two or more parties to be correlated in a way that is much stronger than they can be in any classical way. Entanglement also plays an important role in many quantum information processing tasks such as teleportation, quantum key distribution, and quantum computing. However, to determine whether a quantum system is entangled or not is challenging, which is in general an NP-hard problem.
The set of entangled states, as a subset of some high-dimensional Euclidean space, has a very complicated structure. Due to its renowned effectiveness in pattern recognition for high-dimensional objects, machine learning is a powerful tool to understand such a structure. In this recent work (full paper attached below and available on arXiv) by group leader Dr. Bei Zeng, a reliable classifier for classifying entangled and non-entangled states is constructed via the supervised learning approach. The idea is to feed our classifier by a large amount of sampled trial states as well as their corresponding class labels, and then train the classifier to predict the class labels of new states that it has not encountered before. Compared to the conventional criteria for entanglement detection, our method can classify an unknown state into the entangled or non-entangled category more precisely and rapidly. We anticipate that our work would provide new insights to employ the machine learning techniques to deal with more quantum information processing tasks in the near future.