arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2401.03400quant-ph

基于计算机视觉的纠缠结构检测

Entanglement Structure Detection via Computer Vision

Rui Li, Junling Du, Zheng Qin, Shikun Zhang, Chunxiao Du, Yang Zhou, Zhisong Xiao

AI总结:

提出一种混合CNN-Transformer模型,利用图像处理技术高效检测多体量子纠缠结构,在GHZ/W态分类中精度优于传统方法。

摘要:

量子纠缠在各种量子信息处理任务中扮演着关键角色。然而,目前仍然缺乏一种通用且有效的方法来检测纠缠结构,特别是对于高维和多体量子系统。注意到多体量子态的常见表示与图像数据结构之间的数学相似性,我们受到启发,采用先进的计算机视觉技术进行数据分析。在这项工作中,我们提出了一种混合CNN-Transformer模型,用于GHZ态和W态的分类以及各种纠缠结构的检测。通过利用CNN的特征提取能力和Transformer的强大建模能力,我们不仅能有效减少训练过程所需的时间和计算资源,还能获得较高的检测精度。通过数值模拟和物理验证,证实了我们的混合模型比传统技术更有效,从而为多体纠缠的独立检测提供了一种强大的工具。

英文摘要:

Quantum entanglement plays a pivotal role in various quantum information processing tasks. However, there still lacks a universal and effective way to detecting entanglement structures, especially for high-dimensional and multipartite quantum systems. Noticing the mathematical similarities between the common representations of many-body quantum states and the data structures of images, we are inspired to employ advanced computer vision technologies for data analysis. In this work, we propose a hybrid CNN-Transformer model for both the classification of GHZ and W states and the detection of various entanglement structures. By leveraging the feature extraction capabilities of CNNs and the powerful modeling abilities of Transformers, we can not only effectively reduce the time and computational resources required for the training process but also obtain high detection accuracies. Through numerical simulation and physical verification, it is confirmed that our hybrid model is more effective than traditional techniques and thus offers a powerful tool for independent detection of multipartite entanglement.

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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