论文标题
在边界驱动扩散系统中计算电流统计量的张量网络方法
Tensor-Network Approaches to Counting Statistics for the Current in a Boundary-Driven Diffusive System
论文作者
论文摘要
我们将张量网络应用于计算不平衡扩散系统中随机粒子传输的统计数据。该系统由与末端两个粒子储层接触的一维通道组成。分别实施了两种张量 - 网络算法，即密度矩阵重量化组（DMRG）和时间不断发展的块分解（TEBD）。电流的累积生成函数是数值计算的，然后与分析溶液进行比较。在这种应用中发现了这些方法的有效性，这表明了这些方法的良好一致性。此外，表明电流的波动定理可以保持。
We apply tensor networks to counting statistics for the stochastic particle transport in an out-of-equilibrium diffusive system. This system is composed of a one-dimensional channel in contact with two particle reservoirs at the ends. Two tensor-network algorithms, namely, Density Matrix Renormalization Group (DMRG) and Time Evolving Block Decimation (TEBD), are respectively implemented. The cumulant generating function for the current is numerically calculated and then compared with the analytical solution. Excellent agreement is found, manifesting the validity of these approaches in such an application. Moreover, the fluctuation theorem for the current is shown to hold.
