This paper presents an application of a novel quadratic unconstrained binary optimization (QUBO) formulation to the minimum loss problem in distribution networks. The proposed QUBO formulation was conceived to be employed in quantum annealing—a quantum computing paradigm useful for solving combinatorial optimization problems. Quantum annealing is expected to provide better and/or faster solutions to optimization problems when compared to the ones provided by classical computers. With the problem at stake, better solutions result in lower energy losses, and faster solutions contribute to the same outcome given the future need for frequent reconfiguration of distribution networks to accommodate highly volatile demand, as anticipated by recent low-carbon solutions. The paper presents the results obtained through a hybrid quantum-classical solver for a standard 33-node test network and compares them with the ones obtained from classical solvers. Our main conclusion is that quantum annealing has potential to show advantage in the near future in terms of solution quality and time-to-solution, as quantum annealers and hybrid solvers continue to improve their performance.