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The work uses IBM hardware as a sampler of electronic configurations, with the diagonalization done classically on a small projected subspace of 184 determinants. That makes it a useful test of hybrid quantum-classical chemistry methods, but the quantum contribution is harder to isolate than the headline comparisons suggest. Readers should treat the reported gains over prior baselines as preprint results pending independent validation, and watch whether the approach scales to bulk materials.
Los Alamos and QC Ware Model Correlated Iron Oxides Using Sample-Based Quantum Diagonalization on IBM Hardware
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. The post Los Alamos and QC Ware Model Correlated Iron Oxides Using Sample-Based Quantum Diagonalization on IBM Hardware appeared first on Quantum Computing Report.
Sample-based Quantum Diagonalization from IBM hardware measurements. In a preprint titled Towards Quantum Computation of Bulk FeO, researchers from Los Alamos National Laboratory (LANL) and QC Ware present a hybrid workflow simulating strongly correlated…
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