Quantum entanglement is a fundamental property commonly used in various
quantum information protocols and algorithms. Nonetheless, the problem of
quantifying entanglement has still not reached general solution for systems
larger than two qubits. In this paper, we investigate the possibility of
detecting entanglement with the use of the supervised machine learning method,
namely the deep convolutional neural networks. We build a model consisting of
convolutional layers, which is able to recognize and predict the presence of
entanglement for any bipartition of the given multi-qubit system. We
demonstrate that training our model on synthetically generated datasets
collecting random density matrices, which either include or exclude challenging
positive-under-partial-transposition entangled states (PPTES), leads to the
different accuracy of the model and its possibility to detect such states.
Moreover, it is shown that enforcing entanglement-preserving symmetry
operations (local operations on qubit or permutations of qubits) by using
triple Siamese network, can significantly increase the model performance and
ability to generalize on types of states not seen during the training stage. We
perform numerical calculations for 3,4 and 5-qubit systems, therefore proving
the scalability of the proposed approach.

使用深度卷积神经网络逐步检测多量子位系统的纠缠状态，结合三分体网络并强制对称操作提高检测准确性，从而可扩展遇到新难题的精确预测。