Advanced Quantum Technologies

Quantum State Separability Identification via Neural Networks

Zhenhua Zhu, Chong‐Qiang Ye, Guo-Lin Lv, Xiaoke Wang, Ling-Hui Zhu, Tuo Huang, Xiaoyu Chen

Published Mar 1, 2026• 0 citations

Abstract

ABSTRACT Quantum entanglement is a fundamental resource in quantum information science. In recent years, many researchers have explored the use of machine learning techniques to detect entanglement. However, most existing models still suffer from limited classification and generalization capability. To address these issues, we construct a randomly generated dataset of three‐qubit quantum states covering fully separable, bi‐separable, and genuine entangled states, and train a complex‐valued convolutional neural network to classify these three classes. Numerical results demonstrate that the proposed model accurately identifies all separability classes and exhibits strong generalization performance not only on the constructed dataset but also on various noisy quantum states, including noisy GHZ, W, GHZ‐W mixed, and hypergraph states, achieving an overall accuracy of 98.6%. In addition, we test the ability of the proposed model to identify certain positive‐partial‐transpose entangled states (PPTES), as well as quantum states that undergo local unitary (LU) operations. All of these results indicate that the proposed model can accurately identify the separability of most three‐qubit quantum states and can also serve as a reliable tool, providing highly probable and accurate predictions when theoretical methods fail.

DOI: 10.1002/qute.202500951

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