Computing vibrational eigenstates with tree tensor network states (TTNS)
Computing vibrational eigenstates with tree tensor network states (TTNS)
复制标题
DOI：
10.1063/1.5130390
复制
发表时间：
2019-11-28
影响因子：
4.4
通讯作者：
Larsson, Henrik R.
中科院分区：
文献类型：
--
作者：
Larsson, Henrik R.
我们提出了如何计算振动本征态与树张量网络状态（TTNS），背后的多层多组态含时Hartree（ML-MCTDH）方法的基础anterior。本征态的计算是基于密度矩阵重整化群（DMRG）的算法。我们应用此计算乙腈（CH_3CN）的振动光谱，以高精度和比较TTNS与矩阵产物状态（MPS），DMRG背后的anantonymous。所提出的优化方案的收敛速度比ML-MCTDH为基础的优化。对于这个特定的系统，我们发现更一般的TTNS没有比MPS更大的优势。我们强调，对于TTNS和MPS，自适应键尺寸的使用显着减少了所需的参数。此外，我们还提出了一个程序，以找到良好的树木。由AIP Publishing授权出版。
We present how to compute vibrational eigenstates with tree tensor network states (TTNSs), the underlying ansatz behind the multilayer multiconfiguration time-dependent Hartree (ML-MCTDH) method. The eigenstates are computed with an algorithm that is based on the density matrix renormalization group (DMRG). We apply this to compute the vibrational spectrum of acetonitrile (CH3CN) to high accuracy and compare TTNSs with matrix product states (MPSs), the ansatz behind the DMRG. The presented optimization scheme converges much faster than ML-MCTDH-based optimization. For this particular system, we found no major advantage of the more general TTNS over MPS. We highlight that for both TTNS and MPS, the usage of an adaptive bond dimension significantly reduces the amount of required parameters. We furthermore propose a procedure to find good trees. Published under license by AIP Publishing.