  Recent theoretical and experimental results suggest the possibility of using
current and near-future quantum hardware in challenging sampling tasks. In this
paper, we introduce free energy-based reinforcement learning (FERL) as an
application of quantum hardware. We propose a method for processing a quantum
annealer's measured qubit spin configurations in approximating the free energy
of a quantum Boltzmann machine (QBM). We then apply this method to perform
reinforcement learning on the grid-world problem using the D-Wave 2000Q quantum
annealer. The experimental results show that our technique is a promising
method for harnessing the power of quantum sampling in reinforcement learning
tasks.
