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arXiv 2404.04005cond-mat.stat-mech
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α-Rényi系综的统计力学与机器学习

The statistical mechanics and machine learning of the $α$-Rényi ensemble

Andrew Jreissaty, Juan Carrasquilla

AI总结:

本文研究经典伊辛模型在α-Rényi系综中的临界行为,结合平均场理论、蒙特卡洛模拟和循环神经网络变分方法,揭示二维下相变连续性与平均场预测的差异,并探讨该系综形式体系的潜力与局限。

摘要:

我们研究了经典伊辛模型在所谓α-Rényi系综中的统计物理,该系综是一种有限温度热态近似,它最小化基于α-Rényi熵的修正自由能。我们首先刻画了其在平均场理论中Rényi指数α不同区域下的临界行为。接着,我们重新引入关联并考虑一维和二维模型,对前者给出解析论证,并为研究后者设计了一种蒙特卡洛方法。值得注意的是,我们发现平均场理论预测在阈值指数α∼1.303以下存在连续相变、在其以上存在一级相变,而二维蒙特卡洛结果则表明在所有α下均为连续相变。最后,我们使用循环神经网络(RNN)拟设对α-Rényi自由能进行变分最小化,发现与蒙特卡洛模拟相比,RNN在二维情形下表现良好。我们的工作凸显了利用α-Rényi系综形式体系探究经典与量子系统热力学平衡性质时的潜在机会与局限性。

英文摘要:

We study the statistical physics of the classical Ising model in the so-called $α$-Rényi ensemble, a finite-temperature thermal state approximation that minimizes a modified free energy based on the $α$-Rényi entropy. We begin by characterizing its critical behavior in mean-field theory in different regimes of the Rényi index $α$. Next, we re-introduce correlations and consider the model in one and two dimensions, presenting analytical arguments for the former and devising a Monte Carlo approach to the study of the latter. Remarkably, we find that while mean-field predicts a continuous phase transition below a threshold index value of $α\sim 1.303$ and a first-order transition above it, the Monte Carlo results in two dimensions point to a continuous transition at all $α$. We conclude by performing a variational minimization of the $α$-Rényi free energy using a recurrent neural network (RNN) ansatz where we find that the RNN performs well in two dimensions when compared to the Monte Carlo simulations. Our work highlights the potential opportunities and limitations associated with the use of the $α$-Rényi ensemble formalism in probing the thermodynamic equilibrium properties of classical and quantum systems.

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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