变分数值重整化群:弥合NRG与密度矩阵重整化群之间的鸿沟
Variational Numerical Renormalization Group: Bridging the gap between NRG and Density Matrix Renormalization Group
摘要:
将数值重整化群(NRG)转化为一种变分方法,其代价函数由有效低能哈密顿量的所有能量之和给出。这允许通过扫描系统地改进由NRG获得的频谱。随后的算法在针对许多状态时与密度矩阵重整化群(DMRG)有很多相似之处,并且NRG和DMRG的这种协同作用结合了两个世界的最佳效果并扩展了它们的适用性。我们说明了这种方法与模拟的量子自旋链和单个杂质安德森模型(SIAM)的有效本征态的准确性大大提高相比,NRG,特别是在过渡到连续极限。
英文摘要:
The numerical renormalization group (NRG) is rephrased as a variational method with the cost function given by the sum of all the energies of the effective low-energy Hamiltonian. This allows to systematically improve the spectrum obtained by NRG through sweeping. The ensuing algorithm has a lot of similarities to the density matrix renormalization group (DMRG) when targeting many states, and this synergy of NRG and DMRG combines the best of both worlds and extends their applicability. We illustrate this approach with simulations of a quantum spin chain and a single impurity Anderson model (SIAM) where the accuracy of the effective eigenstates is greatly enhanced as compared to the NRG, especially in the transition to the continuum limit.
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。