Google that.
"Quantum annealers are physical quantum devices designed to solve optimization problems by finding low-energy configurations of an appropriate energy function by exploiting cooperative tunneling effects to escape local minima. Classical annealers use thermal fluctuations for the same computational purpose, and Markov chains based on this principle are among the most widespread optimization techniques. The fundamental mechanism underlying quantum annealing consists of exploiting a controllable quantum perturbation to generate tunneling processes. The computational potentialities of quantum annealers are still under debate, since few ad hoc positive results are known. Here, we identify a wide class of large-scale nonconvex optimization problems for which quantum annealing is efficient while classical annealing gets stuck. These problems are of central interest to machine learning."
https://www.pnas.org/content/115/7/1457
Bam!
No need to appeal to anything qUanTuM though. This is all standard equilibrium statistical mechanics. Quantum annealing may or may not have an advantage over simulated annealing/classical methods (have not kept up with the D-wave literature) - but the underlying physics is all classical. Just a fancy optimization technique.