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arXiv 2602.04366quant-ph

低资源神经网络可解释性下的纠缠分类

Low resource entanglement classification from neural network interpretability

A. García-Velo, R. Puebla, Y. Ban, E. Torrontegui, M. Paraschiv

AI总结:

本研究提出了一种统一且可解释的神经网络框架,用于低资源条件下两比特和三比特态的SLOCC纠缠分类,通过Shapley值分析提升模型可解释性。

摘要:

纠缠是量子信息和量子技术中的核心资源,然而其表征仍然具有挑战性,这既是因为理论复杂性也因为测量要求。机器学习已出现作为有前途的替代方法,使从不完整测量数据中进行纠缠表征成为可能,然而模型可解释性仍是一个挑战。在本工作中,我们引入了一个统一且可解释的框架,用于两比特和三比特态的SLOCC纠缠分类,涵盖纯态和混合态。我们训练密集和卷积神经网络在Pauli测量结果上,为每种架构提供设计指南,并系统地比较它们在不同类型态上的性能。为了解释模型,我们计算Shapley值以量化每个测量的贡献,分析不同系统中的测量重要性模式,并利用这些见解指导测量减少方案。准确率-测量曲线和与分析纠缠标准的比较展示了可靠分类所需的最小资源,并突显了基于Shapley的可解释性在使用机器学习模型进行纠缠检测和分类时的能力和局限性。

英文摘要:

Entanglement is a central resource in quantum information and quantum technologies, yet its characterization remains challenging due to both theoretical complexity and measurement requirements. Machine learning has emerged as a promising alternative, enabling entanglement characterization from incomplete measurement data, however model interpretability remains a challenge. In this work, we introduce a unified and interpretable framework for SLOCC entanglement classification of two- and three-qubit states, encompassing both pure and mixed states. We train dense and convolutional neural networks on Pauli-measurement outcomes, provide design guidelines for each architecture, and systematically compare their performance across types of states. To interpret the models, we compute Shapley values to quantify the contribution of each measurement, analyze measurement-importance patterns across different systems, and use these insights to guide a measurement-reduction scheme. Accuracy-versus-measurement curves and comparisons with analytical entanglement criteria demonstrate the minimal resources required for reliable classification and highlight both the capabilities and limitations of Shapley-based interpretability when using machine learning models for entanglement detection and classification.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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