Los Alamos and QC Ware Model Correlated Iron Oxides Using Sample-Based Quantum Diagonalization on IBM Hardware
AI-summarised brief · reviewed before publication
Researchers at Los Alamos National Laboratory and QC Ware published a preprint demonstrating a hybrid quantum-classical workflow that accurately models strongly correlated iron oxides. Using sample‑based quantum diagonalization (SQD) on IBM’s ibm_miamisystem, they combined ab initio‑derived tight‑binding Hamiltonians with a one‑layer Local Unitary Cluster Jastrow ansatz. The approach bypassed VQE barren plateaus by optimizing circuit parameters classically and sampling on the QPU. For FeO, the method achieved chemical accuracy, predicting the experimental quintet state with an excitation energy of 2,167 cm⁻¹, far surpassing previous multireference benchmarks.
💡 Why It Matters
- · The technique shows how near‑term quantum hardware can tackle materials that defy conventional density functional theory, opening a path to simulate real‑world Mott insulators.