论文标题
在量子接触过程中使用连续变化的指数吸收相变:一种神经网络方法
Absorbing phase transition with a continuously varying exponent in a quantum contact process: a neural network approach
论文作者
论文摘要
耗散量子系统中的相变很吸引人,因为它们是由连贯的量子和不连贯的经典波动之间的相互作用引起的。在这里,我们研究了从量子到量子接触过程(QCP)中产生的经典吸收相跃迁的交叉。 Lindblad方程包含两个参数,分别是$ω$和$κ$,它们分别调整了量子和经典效应的贡献。我们发现,当QCP从具有所有活性站点的均匀状态开始时,该区域中存在一条关键线,$ 0 \ lemκ<κ_*$沿该线路$α$(与活动站点密度相关的指数$α$(与活动站点的密度相关)持续减少对经典的指导性percolation(dp)值的量子。这种行为表明,量子相干效应在一定程度上仍然接近$κ= 0 $。但是,当一个维度的QCP从具有所有不活动位点的异质状态开始时,所有关键指数都具有$κ\ ge 0 $的经典DP值。在二维中,不会发生异常的交叉行为,而经典的DP行为出现在$κ\ ge 0 $的整个区域中,而不管初始配置如何。神经网络机器学习用于识别临界线并确定相关长度指数。使用量子跳跃蒙特卡洛技术和张量网络方法的数值模拟,以确定QCP的所有其他关键指数。
Phase transitions in dissipative quantum systems are intriguing because they are induced by the interplay between coherent quantum and incoherent classical fluctuations. Here, we investigate the crossover from a quantum to a classical absorbing phase transition arising in the quantum contact process (QCP). The Lindblad equation contains two parameters, $ω$ and $κ$, which adjust the contributions of the quantum and classical effects, respectively. We find that in one dimension when the QCP starts from a homogeneous state with all active sites, there exists a critical line in the region $0 \le κ< κ_*$ along which the exponent $α$ (which is associated with the density of active sites) decreases continuously from a quantum to the classical directed percolation (DP) value. This behavior suggests that the quantum coherent effect remains to some extent near $κ=0$. However, when the QCP in one dimension starts from a heterogeneous state with all inactive sites except for one active site, all the critical exponents have the classical DP values for $κ\ge 0$. In two dimensions, anomalous crossover behavior does not occur, and classical DP behavior appears in the entire region of $κ\ge 0$ regardless of the initial configuration. Neural network machine learning is used to identify the critical line and determine the correlation length exponent. Numerical simulations using the quantum jump Monte Carlo technique and tensor network method are performed to determine all the other critical exponents of the QCP.