Recently, my coworkers and I put out a preprint “Classical solution of the FeMo-cofactor model to chemical accuracy and its implications’’ (Zhai et al. 2026). It is a bit unusual to write commentary on one’s own scientific article. However, in this case, given the many inquiries I have had about the work in the context of…

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Garnet Chan provides commentary on a preprint classically solving the FeMo-cofactor (FeMo-co) ground-state energy to chemical accuracy, contextualizing it within quantum computing for chemistry. The FeMo-co is the reaction center of nitrogenase, the enzyme responsible for biological nitrogen fixation. A 2017 paper by Reiher et al. popularized FeMo-co as a benchmark for quantum advantage in chemistry, but the original model used was trivially easy classically. Chan's group developed a more representative 152-qubit LLDUC model, which has since become the standard benchmark. Their new work shows this model can be solved classically using a filtering and enumeration protocol over competing product states, revealing that FeMo-co's complexity stems from multiple competing configurations rather than deep multiconfigurational entanglement. Chan argues this does not undermine enthusiasm for quantum computing in chemistry, but does challenge claims of exponential quantum speedup for such problems. He also corrects the misconception that nitrogenase is more energy-efficient than the Haber-Bosch process. The key lesson is that quantum algorithms should target problems where classical heuristics genuinely fail, and that understanding the physics of a problem is essential for both classical and quantum approaches.

The FeMo-cofactor and classical and quantum computing

Heuristics in the classical solution of the LLDUC FeMo-cofactor model

Implications of the classical solution for chemistry

Implications of the classical solution for quantum computing in chemistry