Reliable entanglement detection via quantum generative adversarial model

Abstract A quantum adversarial solver has been successfully used for bipartite entanglement detection in both theory and experiments. In this paper, we introduce a versatile objective function for the quantum generative adversarial model and improve the optimization process of the model to avoid false convergence in the original optimization process. The sources of error in the classification of separability and entanglement have been analyzed in detail to increase practicality, and we propose a scheme to fully utilize limited quantum resources to successfully distinguish as many separable states and entangled states as possible. Numerical simulations not only confirm the robustness of our model to false convergence , but also validate its effectiveness in detecting separability and entanglement. Finally, we also confirm the performance of the model for multi-body systems.