论文标题
稀疏IR：多体繁殖器的最佳压缩和稀疏采样
sparse-ir: optimal compression and sparse sampling of many-body propagators
论文作者
论文摘要
我们介绍了稀疏IR，这是一个库的集合来有效处理假想时间传播器，这是有限温度量子多体计算中的中心对象。我们利用两个概念：首先，具有可靠的A-Priori误差估计的传播器的中间表示（IR），其次，稀疏采样，在想象的时间和假想频率中近乎最佳的网格，可以从中可以对传播器进行构建以及在哪些图中进行构造。 IR和稀疏采样包装成独立的，易于使用的Python，Julia和Fortran库，可以很容易地将其包含在现有软件中。我们还包括一组大量的示例代码，展示了库库的典型多体和AB启动方法。
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 robust a-priori error 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 and ab initio methods.
