arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 2103.15855cond-mat.stat-mechcond-mat.dis-nncond-mat.str-elquant-ph
收藏

一种具有涌现标度行为的相变分类学习算法

A learning algorithm with emergent scaling behavior for classifying phase transitions

Nishad Maskara, Michael Buchhold, Manuel Endres, Evert van Nieuwenburg

摘要:

受机器学习启发的技术已经成为分析量子物质相变的新范式。在这项工作中,我们引入了一种监督学习算法,用于从测量数据中研究关键现象,该算法基于迭代训练复杂度不断增加的卷积网络,并在横向场伊辛链和q=6 Potts模型上进行测试。在连续的伊辛过渡,我们确定在分类精度的缩放行为,从中我们推断出一个特征的分类长度尺度。它在临界点处显示出幂律发散,其标度指数与发散相关长度相匹配。我们的算法正确地识别系统的热力学相,并提取标度行为从投影测量,独立的基础上进行测量。此外,我们发现的分类长度尺度是缺席的$q=6$波茨模型,它有一个一阶过渡,因此缺乏一个发散的相关长度。我们发现的主要直觉是,对于尺寸小于相关长度的测量块,系统似乎处于临界点,因此算法无法识别数据的相位。

英文摘要:

Machine learning-inspired techniques have emerged as a new paradigm for analysis of phase transitions in quantum matter. In this work, we introduce a supervised learning algorithm for studying critical phenomena from measurement data, which is based on iteratively training convolutional networks of increasing complexity, and test it on the transverse field Ising chain and q=6 Potts model. At the continuous Ising transition, we identify scaling behavior in the classification accuracy, from which we infer a characteristic classification length scale. It displays a power-law divergence at the critical point, with a scaling exponent that matches with the diverging correlation length. Our algorithm correctly identifies the thermodynamic phase of the system and extracts scaling behavior from projective measurements, independently of the basis in which the measurements are performed. Furthermore, we show the classification length scale is absent for the $q=6$ Potts model, which has a first order transition and thus lacks a divergent correlation length. The main intuition underlying our finding is that, for measurement patches of sizes smaller than the correlation length, the system appears to be at the critical point, and therefore the algorithm cannot identify the phase from which the data was drawn.

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。

↑