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Improving the efficiency and accuracy of energy calculations has been ofsignificant and continued interest in the area of materials informatics, afield that applies machine learning techniques to computational materials data.Here, we present a heuristic quantum-classical algorithm to efficiently modeland predict the energies of substitutionally disordered binary crystallinematerials. Specifically, a quantum circuit that scales linearly in the numberof lattice sites is designed and trained to predict the energies of quantumchemical simulations in an exponentially-scaling feature space. This circuit istrained by classical supervised-learning using data obtained fromclassically-computed quantum chemical simulations. As a part of the trainingprocess, we introduce a sub-routine that is able to detect and rectifyanomalies in the input data. The algorithm is demonstrated on the complexlayer-structured of Li-cobaltate system, a widely-used Li-ion battery cathodematerial component. Our results shows that the proposed quantum circuit modelpresents a suitable choice for modelling the energies obtained from suchquantum mechanical systems. Furthermore, analysis of the anomalous dataprovides important insights into the thermodynamic properties of the systemsstudied.

A Heuristic Quantum-Classical Algorithm for Modeling Substitutionally Disordered Binary Crystalline Materials