  The analytic continuation of imaginary-time quantum Monte Carlo data to
extract real-frequency spectra remains a key problem in connecting theory with
experiment. Here we present a fast and efficient stochastic optimization method
(FESOM) as a more accessible variant of the stochastic optimization method
introduced by Mishchenko et al. and benchmark the resulting spectra with those
obtained by the standard Maximum Entropy method for three representative test
cases, in- cluding data taken from studies of the two-dimensional Hubbard
model. We generally find that our FESOM approach gives spectra similar to the
Maximum Entropy results. In particular, while the Maximum Entropy method gives
superior results when the quality of the data is strong, we find that FESOM is
able to resolve fine structure with more detail when the quality of the data is
poor. In addition, because of its stochastic nature, the method provides
detailed information on the frequency dependent uncertainty of the resulting
spectra, while the Maximum Entropy method does so only for the spectral weight
integrated over a finite frequency region. We therefore believe that this
variant of the stochastic optimization approach provides a viable alternative
to the routinely used Maximum Entropy method especially for data with poor
quality.
