Entanglement detection with classical deep neural networks

In this study, we introduce an autonomous method for addressing the detection and classification of quantum entanglement, a core element of quantum mechanics that has yet to be fully understood. We employ a multi-layer perceptron to effectively identify entanglement in both two- and three-qubit systems. Our technique yields impressive detection results, achieving nearly perfect accuracy for two-qubit systems and over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>90</mml:mn> <mml:mo>%</mml:mo></mml:mrow> </mml:math> accuracy for three-qubit systems. Additionally, our approach successfully categorizes three-qubit entangled states into distinct groups with a success rate of up to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>77</mml:mn> <mml:mo>%</mml:mo></mml:mrow> </mml:math> . These findings indicate the potential for our method to be applied to larger systems, paving the way for advancements in quantum information processing applications.