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arXiv 2401.16481quant-ph
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学习矩阵乘积态的稳定子群

Learning the stabilizer group of a Matrix Product State

Guglielmo Lami, Mario Collura

AI总结:

该研究提出一种基于泡利基有偏采样的经典算法,可学习矩阵乘积态的稳定子群以获取非稳定子单调量,在高纠缠MPS上表现准确且标度良好,为非平衡量子多体物理研究提供了有效工具。

摘要:

我们提出了一种新颖的经典算法,用于学习给定矩阵乘积态(Matrix Product State, MPS)的稳定子群——即使得该态为其±1本征矢的泡利字符串群。该算法基于泡利(或贝尔)基中一种巧妙且有理论依据的有偏采样方法。其输出是一组独立的稳定子生成元,生成元的总数与稳定子零化度直接相关,而稳定子零化度是一种公认的非稳定子单调量。我们在通过克利福德酉动力学随机置乱的T掺杂态上对方法进行了基准测试,结果表明,对于键维数χ~10³的高纠缠MPS,该方法仍能给出非常准确的估计。得益于极为有利的O(χ³)标度,我们的方法是首个能为MPS获取真正的魔法单调量的有效方法,可用于系统研究非平衡量子多体物理。

英文摘要:

We present a novel classical algorithm designed to learn the stabilizer group -- namely the group of Pauli strings for which a state is a $\pm 1$ eigenvector -- of a given Matrix Product State (MPS). The algorithm is based on a clever and theoretically grounded biased sampling in the Pauli (or Bell) basis. Its output is a set of independent stabilizer generators whose total number is directly associated with the stabilizer nullity, notably a well-established nonstabilizer monotone. We benchmark our method on $T$-doped states randomly scrambled via Clifford unitary dynamics, demonstrating very accurate estimates up to highly-entangled MPS with bond dimension $χ\sim 10^3$. Our method, thanks to a very favourable scaling $\mathcal{O}(χ^3)$, represents the first effective approach to obtain a genuine magic monotone for MPS, enabling systematic investigations of quantum many-body physics out-of-equilibrium.

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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