发表机构
School of Physics, Beihang University（北京航空航天大学物理学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种以多体不确定关系为先验引导的神经网络方法，用于SLOCC多体纠缠分类，在20量子比特系统中达到99.5%的准确率，兼具高效性、可扩展性和鲁棒性。
AI 中文摘要
量子纠缠是量子信息处理中的关键资源，然而在多体系统中对其进行高效、可扩展且稳健的分类在理论上仍具挑战性。尽管监督式机器学习已被应用于此任务，但现有的大多数方法仍面临测量成本高、计算消耗大以及噪声鲁棒性弱等问题。在本工作中，通过将多体不确定关系作为先验引导，我们提出了一种神经网络方法，基于从局部酉（LU）轨道采样的态来区分不同的随机局域操作与经典通信（SLOCC）多体纠缠类。与传统技术相比，我们的方法降低了实验测量资源需求和计算开销，展现出对大规模系统的高度适应性。该方法在20量子比特系统中的分类准确率达到99.5%。数值验证在由随机LU变换生成的态上进行，这些变换保持SLOCC类不变。在此设置下，所提出的方法为多体纠缠分类提供了强有效性、可扩展性和鲁棒性。
英文摘要
Quantum entanglement is a crucial resource in quantum information processing, yet its efficient, scalable and robust classification in multipartite systems remains theoretically challenging. Although supervised machinelearning has been applied to this task, most existing methods still suffer from high measurement costs, computational consumption, and weak noise robustness. In this work, by incorporating multipartite uncertainty relations as prior guidance, we propose a neural network approach to classify distinct Stochastic Local Operations and Classical Communication (SLOCC) multipartite entanglement classes based on states sampled from their local unitary (LU) orbits. Compared with traditional techniques, our method reduces experimental measurement-resource requirements and computational overhead, showing high adaptability to large-scale systems. The classification accuracy of our method reaches 99.5% in 20-qubit systems. The numerical validation is performed on states generated by random LU transformations, which preserve the SLOCC class. Within this setting, the proposed method offers strong effectiveness, scalability, and robustness for multipartite entanglement classification.
Comments10 pages,6,figures,Front.Phys(2026)in press
