  We develop a novel analytic continuation method for self-energies on the
Matsubara domain as computed by quantum Monte Carlo simulations within
dynamical mean field theory (QMC+DMFT). Unlike a maximum entropy (maxEn)
procedure employed for the last thirty years, our approach is based on a
machine learning (ML) technique in combination with the iterative perturbative
theory impurity solver of the dynamical mean field theory self-consistent
process (IPT+DMFT). The input and output training datasets for ML are
simultaneously obtained from IPT+DMFT calculations on Matsubara and real
frequency domains, respectively. The QMC+DMFT self-energy on real frequencies
is determined from the -- usually noisy -- input QMC+DMFT self-energy on the
Matsubara domain and the trained ML kernel. Our approach is free from both,
bias of ML training datasets and from fitting parameters present in the maxEn
method. We demonstrate the efficiency of the method on the testbed frustrated
Hubbard model on the square lattice.
