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arXiv 1108.0900cond-mat.str-elphysics.comp-ph
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变分量子蒙特卡罗方法中大参数集的优化

Optimizing large parameter sets in variational quantum Monte Carlo

Eric Neuscamman, C. J. Umrigar, Garnet Kin-Lic Chan

摘要:

我们提出了一种技术,优化几十万的变分量子蒙特卡罗参数。通过引入迭代Krylov子空间求解器,并通过乘以哈密顿和重叠矩阵,因为它们被采样,我们消除了需要构建和存储这些矩阵,从而绕过最昂贵的步骤的随机重新配置和线性方法优化技术。我们证明了这种方法的有效性,通过使用随机重新配置优化相关器产品状态波函数与pfetran四个示例系统的参考。在二维哈伯德模型的两个例子中,我们研究了16和64位晶格,在较小的晶格中恢复能量精确到1%,并预测较大的晶格中的粒子-空穴相分离。在两个涉及从头算哈密顿量的例子中,我们研究了对称解离的4x 4氢晶格的势能曲线以及自由基卟啉的单重态-三重态能隙。在氢系统中,我们在所有几何形状下恢复了98%或更多的相关能,而对于卟吩,我们计算了24轨道活性空间中的间隙,精确结果在0.02eV之内。在这些例子中,变分参数的数量从4x10^3到5x10^5不等,这表明了一种远远超出先前随机重构公式所能达到的能力。

英文摘要:

We present a technique for optimizing hundreds of thousands of variational parameters in variational quantum Monte Carlo. By introducing iterative Krylov subspace solvers and by multiplying by the Hamiltonian and overlap matrices as they are sampled, we remove the need to construct and store these matrices and thus bypass the most expensive steps of the stochastic reconfiguration and linear method optimization techniques. We demonstrate the effectiveness of this approach by using stochastic reconfiguration to optimize a correlator product state wavefunction with a pfaffian reference for four example systems. In two examples on the two dimensional Hubbard model, we study 16 and 64 site lattices, recovering energies accurate to 1% in the smaller lattice and predicting particle-hole phase separation in the larger. In two examples involving an ab initio Hamiltonian, we investigate the potential energy curve of a symmetrically dissociated 4x4 hydrogen lattice as well as the singlet-triplet gap in free base porphin. In the hydrogen system we recover 98% or more of the correlation energy at all geometries, while for porphin we compute the gap in a 24 orbital active space to within 0.02eV of the exact result. The numbers of variational parameters in these examples range from 4x10^3 to 5x10^5, demonstrating an ability to go far beyond the reach of previous formulations of stochastic reconfiguration.

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