Biazzo I, Ramezanpour A
DISAT and Center for Computational Sciences, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy.
DISAT and Center for Computational Sciences, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy and Department of Physics, University of Neyshabur, P.O. Box 91136-899, Neyshabur, Iran.
Phys Rev E Stat Nonlin Soft Matter Phys. 2014 Jun;89(6):062137. doi: 10.1103/PhysRevE.89.062137. Epub 2014 Jun 26.
Given a locally consistent set of reduced density matrices, we construct approximate density matrices which are globally consistent with the local density matrices we started from when the trial density matrix has a tree structure. We employ the cavity method of statistical physics to find the optimal density matrix representation by slowly decreasing the temperature in an annealing algorithm, or by minimizing an approximate Bethe free energy depending on the reduced density matrices and some cavity messages originated from the Bethe approximation of the entropy. We obtain the classical Bethe expression for the entropy within a naive (mean-field) approximation of the cavity messages, which is expected to work well at high temperatures. In the next order of the approximation, we obtain another expression for the Bethe entropy depending only on the diagonal elements of the reduced density matrices. In principle, we can improve the entropy approximation by considering more accurate cavity messages in the Bethe approximation of the entropy. We compare the annealing algorithm and the naive approximation of the Bethe entropy with exact and approximate numerical simulations for small and large samples of the random transverse Ising model on random regular graphs.
给定一组局部一致的约化密度矩阵,当试验密度矩阵具有树状结构时,我们构造出与起始局部密度矩阵全局一致的近似密度矩阵。我们采用统计物理的腔方法,通过在退火算法中缓慢降低温度,或者通过最小化依赖于约化密度矩阵和一些源自熵的贝叶斯近似的腔信息的近似贝叶斯自由能,来找到最优密度矩阵表示。在腔信息的朴素(平均场)近似下,我们得到了熵的经典贝叶斯表达式,预计它在高温下效果良好。在近似的下一阶,我们得到了另一个仅依赖于约化密度矩阵对角元素的贝叶斯熵表达式。原则上,我们可以通过在熵的贝叶斯近似中考虑更精确的腔信息来改进熵近似。我们将退火算法和贝叶斯熵的朴素近似与随机正则图上随机横向伊辛模型的小样本和大样本的精确及近似数值模拟进行了比较。