Sparse modeling approach to analytical continuation of imaginary-time quantum Monte Carlo data
This work addresses a specific computational bottleneck in quantum physics simulations, offering an incremental improvement for researchers in condensed matter physics.
The authors tackled the ill-conditioned inverse problem in analytical continuation of quantum Monte Carlo data by using a modern regularization technique to eliminate noise-sensitive degrees of freedom, achieving stable spectra with minimal bases and providing a tool to assess required data accuracy for resolving spectral details.
A new approach of solving the ill-conditioned inverse problem for analytical continuation is proposed. The root of the problem lies in the fact that even tiny noise of imaginary-time input data has a serious impact on the inferred real-frequency spectra. By means of a modern regularization technique, we eliminate redundant degrees of freedom that essentially carry the noise, leaving only relevant information unaffected by the noise. The resultant spectrum is represented with minimal bases and thus a stable analytical continuation is achieved. This framework further provides a tool for analyzing to what extent the Monte Carlo data need to be accurate to resolve details of an expected spectral function.