Researchers from Los Alamos National Laboratory and QC Ware developed a hybrid quantum-classical workflow to simulate strongly correlated transition metal oxides. They used Sample-based Quantum Diagonalization (SQD) on IBM Quantum hardware, coupled with ab initio-informed d–p tight-binding models, to achieve chemical accuracy in modeling diatomic iron monoxide (FeO) and rhombic iron oxide dimers (Fe2O2). This method successfully recovered experimental ground states and outperformed traditional computational methods, establishing a new approach for modeling bulk periodic FeO.
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