  Finding the ground state of the Ising spin-glass is an important and
challenging problem (NP-hard, in fact) in condensed matter physics. However,
its applications spread far beyond physic due to its deep relation to various
combinatorial optimization problems, such as travelling salesman or protein
folding. Sophisticated and promising new methods for solving Ising instances
rely on quantum resources. In particular, quantum annealing is a quantum
computation paradigm, that is especially well suited for Quadratic
Unconstrained Binary Optimization (QUBO). Nevertheless, commercially available
quantum annealers (i.e., D-Wave) are prone to various errors, and their ability
to find low energetic states (corresponding to solutions of superior quality)
is limited. This naturally calls for a post-processing procedure to correct
errors (capable of lowering the energy found by the annealer). As a
proof-of-concept, this work combines the recent ideas revolving around the
DIRAC architecture with the Chimera topology and applies them in a real-world
setting as an error-correcting scheme for quantum annealers. Our preliminary
results show how to correct states output by quantum annealers using
reinforcement learning. Such an approach exhibits excellent scalability, as it
can be trained on small instances and deployed for large ones. However, its
performance on the chimera graph is still inferior to a typical algorithm one
could incorporate in this context, e.g., simulated annealing.
