In the 1990s, the principles of quantum mechanics were leveraged to describe how discrete combinatorial optimization problems could be solved using a method called quantum annealing. D-Wave Systems, Inc. has since implemented commercial embodiments. We focus on the use of that hardware to solve discrete hydrologic inverse problems, which can become computationally intense at even moderate sizes. In particular, we interrogate whether contemporary quantum annealers with more qubits, lower noise, and reverse annealing features can improve performance. We find modest improvements in speed and accuracy are possible; however, those benefits are currently constrained to a regime of very rapid, approximate solutions on small meshes. As quantum annealers continue to improve, we expect further encroachment on the performance of classical numerical solvers.