Accuracy of Entanglement Detection via Artificial Neural Networks and Human-Designed Entanglement Witnesses

The detection of entangled states is essential in both fundamental and applied quantum physics. However, this task proves to be challenging, especially for general quantum states. One can execute full state tomography but this method is time demanding, especially in complex systems. Other approaches use entanglement witnesses: these methods tend to be less demanding but lack reliability. Here, we demonstrate that artificial neural networks (ANNs) provide a balance between the two approaches. In this paper, we make a comparison of ANN performance with witness-based methods for random general two-qubit quantum states without any prior information on the states. Furthermore, we apply our approach to a real experimental data set.