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

quantumcomputingreport.com Nov 13, 2026

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

Curated by Shubham · TechCapsules · Nov 13, 2026

AI-summarised brief · reviewed before publication

South Korean quantum‑software firm Qunova Computing reported that its Handover Iterative Variational Quantum Eigensolver (HI‑VQE) achieved chemical accuracy in an 80‑qubit simulation of a [4Fe‑4S] iron‑sulfur cubane cluster. The hybrid workflow ran on IBM’s Quantum System Two processor while off‑loading subspace diagonalization to RIKEN’s Fugaku supercomputer, delivering a ground‑state energy within 1.6 milliHartree of benchmark density‑matrix renormalization group results. Compared with earlier quantum diagonalization attempts, the approach reduced classical supercomputing cost by roughly 300‑fold. Transition‑metal complexes such as iron‑sulfur clusters are central to nitrogen fixation and hydrogen production, yet they resist conventional density‑functional methods. Qunova’s architecture avoids linearity and spatial‑locality assumptions, positioning it for broader catalyst, battery and drug‑discovery applications. Future releases will add orbital‑rotation optimization to handle billions of electronic configurations.

💡 Why It Matters

· Demonstrating chemical‑level precision on 80 qubits shows that current quantum hardware can meaningfully contribute to the design of catalysts and energy‑relevant materials, moving quantum chemistry from theory toward real‑world impact.

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PsiQuantum and AMD Expand Partnership with Equity Investment to Co-Design Utility-Scale Quantum…

PsiQuantum and AMD announced an expanded partnership that includes an equity investment from AMD to co‑design classical and quantum infrastructure for fault‑tolerant photonic quantum computers. The collaboration builds on PsiQuantum’s use of AMD Versal SoCs, adding AMD Instinct GPUs, EPYC CPUs, and the ROCm software stack. Engineers tailored PsiQuantum’s PRISM decoder to integrate machine‑learning logic with photonic control loops, eliminating latency. The joint effort targets pharmaceutical research and molecular simulation, delivering immediate GPU‑accelerated chemistry while preparing drug‑discovery pipelines for future quantum hardware. The companies will showcase their roadmap at SC26 in Chicago.

💡 The alliance bridges high‑performance classical computing with emerging photonic quantum technology, accelerating practical applications in drug discovery and setting a blueprint for scalable quantum‑classical integration.

quantumcomputingreport.com · Nov 13

Kipu Quantum Launches Rimay Quantum Feature Extraction in IBM Quantum Functions Catalog

Kipu Quantum has added its Rimay quantum feature‑extraction service to the IBM Quantum Functions Catalog, enabling IBM Quantum Flex, Premium, and On‑Prem users to access quantum‑enhanced machine learning via API. Rimay serves as a preprocessing layer that maps normalized numerical features to qubits, encodes mutual information into Hamiltonian couplings, and returns an enriched CSV matrix for use with scikit‑learn, XGBoost, CatBoost, or LightGBM. The service supports repeatable inference by storing the trained Hamiltonian topology and can pair with Kipu’s offline surrogate framework. A Fortune 100 client has deployed Rimay on an IBM Quantum Nighthawk processor for customer‑intent prediction, and IBM Quantum documentation includes a credit‑risk classification tutorial.

💡 By embedding quantum‑derived feature engineering directly into conventional data‑science pipelines, Rimay lowers the barrier for enterprises to experiment with quantum advantages without replacing existing models. This integration demonstrates a practical pathway for quantum hardware to augment, rather than overhaul, current machine‑learning workflows.

quantumcomputingreport.com · Nov 13

LTM Becomes First Global Systems Integrator to Launch Function in IBM Quantum…

Larsen & Toubro’s technology arm, LTM, has become the first global systems integrator to publish a circuit function on IBM’s Quantum Functions Catalog. Its AI‑QEM software, accessible via the lt‑m/ai‑qeminterface, integrates with Qiskit Estimator workflows to reduce hardware noise using deep‑learning inference. The approach bypasses extra quantum sampling by rescaling noisy expectation values with trained neural networks, achieving a mean absolute error of 0.343 across 80‑ to 150‑qubit tests, outperforming traditional mitigation baselines. IBM Quantum provides tutorials and API documentation for the new function.

💡 By eliminating the need for costly circuit ensembles, LTM’s AI‑QEM could lower quantum runtime costs and accelerate practical applications on noisy hardware. The integration demonstrates a scalable, software‑centric path to higher‑fidelity quantum computations.

quantumcomputingreport.com · Nov 13

Cleveland Clinic, RIKEN, and IBM Cross 12,000-Atom Barrier in Heterogeneous Quantum-Classical Drug…

A joint effort by Cleveland Clinic, RIKEN and IBM Quantum has performed the largest heterogeneous quantum‑classical (HQC) electronic‑structure simulation to date, modeling solvated protein‑ligand complexes of more than 12,000 atoms. The team linked two 156‑qubit IBM Heron r2 processors with three leading supercomputers, executing 21,006 circuits and collecting 3 billion measurement shots. Their Embedded Wavefunction (EWF) formalism partitions macromolecules into bounded atomic neighborhoods, reducing fragment‑construction cost from cubic to constant scaling. For fragments containing 13‑45 molecular orbitals, the new TrimSQD algorithm iteratively filters and merges configurations across GPU clusters before a final subspace diagonalization. An automated Prefect workflow coordinated IBM Quantum Runtime and the ROQ supercomputer, processing 608 million samples in 62.4 hours without human intervention. The work, detailed in an arXiv preprint, was funded by NSF, NIH, NEDO and Japan’s MEXT Fugaku program.

💡 By breaking the 12,000‑atom barrier, the study shows quantum‑enhanced simulations can tackle realistic drug‑binding problems that were previously out of reach for classical methods.

thequantuminsider.com · Nov 13

The Geometry of Quantum Memory: How Anyons Rewrite the Rules of Remembering…

Topological quantum computing seeks to encode information in a system’s global topology, making it immune to local noise that plagues conventional qubits. The approach relies on anyons—quasiparticles first theorized in the late 1970s and observed as Abelian varieties in 2020—whose braiding in two‑dimensional media can implement logical operations without disturbing the stored state. While Abelian anyons have been detected, the non‑Abelian anyons required for fault‑tolerant computation remain elusive, keeping their experimental verification a central challenge. If realized, topological qubits would dramatically reduce the physical‑to‑logical qubit ratio, potentially eliminating the need for thousands of physical qubits per logical unit. Researchers trace the concept to early work on particle exchange in two dimensions and to Alexei Kitaev’s 1997 proposal linking anyons to quantum gates.

💡 Demonstrating non‑Abelian anyons would provide a hardware‑level error‑resilience that could accelerate scalable quantum computers far beyond current error‑correction schemes.

thequantuminsider.com · Nov 12

Train the Trainer: Inside MAHE’s Hardware-First Bet on Quantum

Manipal Academy of Higher Education (MAHE) has partnered with quantum‑hardware startup QuantrolOx to launch Q‑Hub@MAHE, a live quantum‑computing facility built on the open‑architecture VIDYAQAR platform. The hub, equipped with a dilution refrigerator and ready for real‑time experiments, will run alongside the Quantum EDGE Academy, a simulator‑based training program aimed at cultivating hardware‑savvy engineers. Faculty member Dr. Chandrakantha Bekal, a mechanical‑engineering professor turned quantum trainer, will lead a “train‑the‑trainer” initiative that introduces first‑ and second‑year undergraduates to hands‑on quantum hardware. Over the next three to five years MAHE plans to scale the hub into a fully operational quantum centre, supplying India’s emerging quantum industry with a domestic workforce capable of operating and maintaining physical quantum systems.

💡 By creating a pipeline of hardware‑competent talent, MAHE directly addresses India’s bottleneck in quantum‑machine operation, accelerating the nation’s shift from software‑only research to indigenous quantum hardware deployment.