sparse-ir: Optimal compression and sparse sampling of many-body propagators
sparse-ir: Optimal compression and sparse sampling of many-body propagators
复制标题
DOI：
10.1016/j.softx.2022.101266
复制
发表时间：
2022-06
期刊：
影响因子：
3.4
通讯作者：
M. Wallerberger;Samuel Badr;S. Hoshino;Sebastian Huber;Fumiya Kakizawa;T. Koretsune;Y. Nagai;Kosuke Nogaki;T. Nomoto;Hitoshi Mori;J. Otsuki;S. Ozaki;Thomas Plaikner;Rihito Sakurai;Constanze Vogel;N. Witt;K. Yoshimi;H. Shinaoka
中科院分区：
文献类型：
--
作者：
M. Wallerberger;Samuel Badr;S. Hoshino;Sebastian Huber;Fumiya Kakizawa;T. Koretsune;Y. Nagai;Kosuke Nogaki;T. Nomoto;Hitoshi Mori;J. Otsuki;S. Ozaki;Thomas Plaikner;Rihito Sakurai;Constanze Vogel;N. Witt;K. Yoshimi;H. Shinaoka
我们引入了稀疏-IR，这是一个有效处理虚时间传播器的库集合，虚时间传播器是有限温度量子多体计算中的核心对象。我们利用两个概念：首先，中间表示（IR），具有鲁棒先验误差估计的传播器的最佳压缩，其次，稀疏采样，虚数时间和虚数频率中的接近最优网格，从中可以重建传播器并可以求解图解方程。 IR 和稀疏采样被打包到独立、易于使用的 Python、Julia 和 Fortran 库中，这些库可以轻松包含到现有软件中。我们还提供了一组广泛的示例代码，展示了典型的多体和 ab 启动方法的库。
We introduce sparse-ir, a collection of libraries to efficiently handle imaginary-time propagators, a central object in finite-temperature quantum many-body calculations. We leverage two concepts: firstly, the intermediate representation (IR), an optimal compression of the propagator with robusta priorierror estimates, and secondly, sparse sampling, near-optimal grids in imaginary time and imaginary frequency from which the propagator can be reconstructed and on which diagrammatic equations can be solved. IR and sparse sampling are packaged into stand-alone, easy-to-use Python, Julia and Fortran libraries, which can readily be included into existing software. We also include an extensive set of sample codes showcasing the library for typical many-body andab initiomethods.