Qunova Computing Achieves Chemical Accuracy at 80 Qubits on Iron-Sulfur Cluster Using IBM Quantum System Two and Fugaku

Quantum–classical resource architecture. South Korean quantum algorithm developer Qunova Computing has demonstrated chemical accuracy in an 80-qubit electronic-structure simulation of the [4Fe-4S] iron-sulfur cubane cluster, executing a hybrid workflow across an IBM Quantum System Two processor and the Fugaku supercomputer at RIKEN. Running its Handover Iterative Variational Quantum Eigensolver (HI-VQE) algorithm, the team achieved an extrapolated ground-state energy within 1.6 milliHartree (1 kcal/mol) of classical density-matrix renormalization group (DMRG) benchmarks while cutting classical supercomputing costs by roughly 300× compared to prior quantum diagonalization runs. Qunova HI-VQE 80-Qubit [4Fe-4S] Simulation BenchmarksBenchmark DimensionMeasured Result / SpecificationComparison & SignificanceTarget System[4Fe-4S] iron-sulfur cubane cluster (80 qubits)Catalytic core in nitrogenase and hydrogenase enzymes; intractable for exact classical solvers.Extrapolated Energy−327.2482 ± 0.0007 HartreeMatches classical DMRG reference (−327.2471 Ha) within chemical accuracy (1.6 mHa).Classical HPC Efficiency2,518 Fugaku node-hours (reached −326.9821 Ha by iteration 2)Represents an ~300× reduction in classical compute cost versus prior SQD benchmarks.Compute InfrastructureIBM Quantum System Two & RIKEN Fugaku supercomputerSupported by Japan’s NEDO Post-5G quantum-HPC hybrid platform initiative. Transition-metal complexes such as iron-sulfur clusters govern critical biological and industrial processes, including nitrogen fixation and clean hydrogen synthesis, yet their strongly correlated electrons challenge both standard density functional theory and early quantum algorithms. While prior quantum-centric supercomputing experiments demonstrated subspace sampling on smaller active spaces, reaching quantitative chemical accuracy at 80 qubits has remained an open milestone. HI-VQE reduces the computational complexity of the electronic structure problem by efficiently estimating the ground state with high accuracy. Qunova’s HI-VQE architecture couples quantum state sampling with classical subspace diagonalization without relying on linearity or spatial locality assumptions, enabling application across catalysts, battery electrodes, and pharmaceutical targets. The company plans to integrate orbital rotation and optimization routines into future releases to process between 500 million and 3 billion unique electronic configurations. Official release details are available via Qunova Computing here. Supporting scientific preprints on arXiv detail the [4Fe-4S] iron-sulfur cluster study here, the foundational HI-VQE methodology here, the classical DMRG benchmark for [4Fe-4S] here, and the prior Fugaku SQD comparison study here. Related QCR coverage includes Qunova’s entry into Japan’s JHPC-quantum test user program here, the addition of HI-VQE to IBM’s Qiskit Functions Catalog here, its launch on AWS Marketplace for Amazon Braket here, and the Cleveland Clinic, RIKEN, and IBM 12,635-atom protein-ligand simulation here. In GQI Portal The players behind the news The team that writes QCR tracks every company, deal and technology in the GQI Factory, GQI's verified database of the quantum industry. Next up: every story linked to its players, coming to QCR's paid plans. Players →Companies and institutions across the quantum industry, by segment. Scorecards →How the players compare on hardware, software, funding and more. Newsletter QCR Alerts in your inbox The latest reporting and analysis from Quantum Computing Report. Free, unsubscribe any time. Sign up for QCR Alerts Leave a commentCancel replyAll fields are required. Your email address will not be published.CommentName Email Type in the text displayed above Δ