  Estimating the density of states of systems with rugged free energy
landscapes is a notoriously difficult task of the utmost importance in many
areas of physics ranging from spin glasses to biopolymers. Density of states
estimation has also recently become an indispensable tool for the benchmarking
of quantum annealers when these function as samplers. Some of the standard
approaches suffer from a spurious convergence of the estimates to metastable
minima, and these cases are particularly hard to detect. Here, we introduce a
sampling technique based on population annealing enhanced with a
multi-histogram analysis and report on its performance for spin glasses. We
demonstrate its ability to overcome the pitfalls of other entropic samplers,
resulting in some cases in large scaling advantages that can lead to the
uncovering of new physics. The new technique avoids some inherent difficulties
in established approaches and can be applied to a wide range of systems without
relevant tailoring requirements. Benchmarking of the studied techniques is
facilitated by the introduction of several schemes that allow us to achieve
exact counts of the degeneracies of the tested instances.

Statistical Mechanics;Disordered Systems and Neural Networks;Computational Physics;Quantum Physics