Exploring the thermodynamics of disordered materials with quantum computing

Alloys, solid solutions, and doped systems are essential in technologies such as energy generation and catalysis, but predicting their properties remains challenging because of compositional disorder. As the concentration of components changes in a binary solid solution A(1−x)Bx, the number of possible configurations becomes computationally intractable. Algorithms used in classical optimization methods cannot avoid assessing high-energy states where, for example, simulated annealing is designed to initially spend computational effort. We introduce a scalable, practical, and accurate approach using quantum annealing to efficiently sample low-energy configurations of disordered materials, avoiding the need for excessive high-energy calculations. Our method includes temperature and simulates large unit cells, producing a Boltzmann-like distribution to identify thermodynamically relevant structures. We demonstrate this by predicting bandgap bowing in Al1−xGaxN and bulk modulus variations in Ta1−xWx, with results in excellent agreement with experiments.