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arXiv 2407.18014quant-ph
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混合态多体纠缠表征的数据驱动方法

Data-driven approach to mixed-state multipartite entanglement characterisation

Eric Brunner, Aaron Xie, Gabriel Dufour, Andreas Buchleitner

AI总结:

本文提出一种基于流形学习嵌入的统计框架,从量子计算机可测相关数据中提取混合量子态的多体纠缠结构特征,证明测量关联子统计信息足以表征纠缠并量化寄存器状态混合度,且发现纠缠态与可分态间存在清晰边界,边界外纠缠结构对噪声具鲁棒性。

摘要:

我们开发了一种基于流形学习嵌入的统计框架,用于从量子计算机的可测相关数据中提取混合量子态的多体纠缠结构的相关特征。我们证明了测量关联子的统计信息包含足以表征纠缠并量化计算机寄存器状态混合度的信息。在嵌入空间中,向最大混合状态的过渡在纠缠态和可分态之间显示出清晰的边界。远离该边界时,多体纠缠结构对有限噪声具有鲁棒性。

英文摘要:

We develop a statistical framework, based on a manifold learning embedding, to extract relevant features of multipartite entanglement structures of mixed quantum states from the measurable correlation data of a quantum computer. We show that the statistics of the measured correlators contains sufficient information to characterise the entanglement, and to quantify the mixedness of the state of the computer's register. The transition to the maximally mixed regime, in the embedding space, displays a sharp boundary between entangled and separable states. Away from this boundary, the multipartite entanglement structure is robust to finite noise.

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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