Optimization of ionic configurations in battery materials by quantum annealing

Configurational sampling is an exponentially scaling problem widely encountered in computational materials research. Quantum computing techniques can provide new solutions to such classically hard-to-solve problems. Here, the authors introduce a method that enables the use of quantum annealing to determine the ionic ground state configuration in a battery material. The method relies on a grand-canonical transformation of the energy cost function that strongly reduces the effective coupling strengths of the interaction graph, which is essential for effectiveness of quantum annealing.