Why It Matters
Understanding where classical methods still excel informs realistic expectations for quantum computers and guides funding and research priorities. Chan’s results show that some high‑profile chemistry problems thought to be quantum‑only may still be tractable classically, reshaping the roadmap for quantum advantage in real‑world applications.
Key Takeaways
- •Classical tensor networks solved FeMo cofactor ground state accurately.
- •Study shows nitrogenase simulation not inherently quantum‑only problem.
- •Misleading energy‑saving claims about replacing Haber‑Bosch are inaccurate.
- •Structured chemistry allows low‑entanglement classical algorithms to succeed.
- •Results reshape quantum advantage expectations for quantum chemistry.
Pulse Analysis
In this episode of New Quantum Era, host Sebastian Hassinger sits down with Caltech’s Garnet Chan, a leading computational chemist, to dissect the long‑standing claim that simulating the nitrogenase FeMo cofactor requires quantum computers. Chan explains how the enzyme’s metal‑rich cofactor underpins biological nitrogen fixation—a process often touted as a quantum‑advantage benchmark for future hardware. He also clarifies common misconceptions about energy savings when comparing the natural bacterial pathway to the industrial Haber‑Bosch process, noting that the biological route can be equally energy‑intensive. The conversation grounds these grand narratives in the concrete chemistry that sparked quantum mechanics in the first place.
Chan’s January paper demonstrates a fully classical solution to the FeMo cofactor’s ground‑state energy, achieving the chemical‑accuracy thresholds traditionally used in quantum resource estimates. By exploiting the system’s low entanglement, his team combined tensor‑network techniques with coupled‑cluster corrections, turning a seemingly 150‑qubit Hamiltonian into a tractable problem. The computation ran on a modest cluster for several weeks—potentially minutes on a DOE‑scale supercomputer—yet still delivered results that match experimental standards. This work highlights how structured chemical interactions can be leveraged to bypass the exponential scaling that fuels quantum‑advantage hype.
The broader implication is a call for honest accounting in quantum chemistry. Chan’s findings force the community to reassess which problems truly lie beyond classical reach and to develop rigorous benchmarks that separate model assumptions from demonstrable hardness. While quantum algorithms remain promising, this episode underscores that many near‑term chemistry challenges may still be solved efficiently with advanced classical methods. For businesses and researchers eyeing quantum investments, the takeaway is clear: evaluate claims against proven classical baselines before committing resources to quantum hardware.
Episode Description
Your host, Sebastian Hassinger, is joined on this episode by Garnet Chan, the Bren Professor of Chemistry at Caltech, a member of the National Academy of Sciences, and among the most cited computational chemists in the world (34,000+ Google Scholar citations). Garnet is neither a quantum computing booster nor a dismissive skeptic. He's a theorist who works at the exact boundary between what classical algorithms can and cannot do — and who keeps finding that boundary further out than the quantum computing community has claimed. The FeMo-cofactor has been a flagship quantum computing use case for nearly a decade: a catalytic core of the enzyme that fixes atmospheric nitrogen into ammonia, and a molecule widely described as "beyond classical reach." Chan's January 2026 paper challenges that framing directly. This conversation explains what was actually solved, what wasn't, and what it would genuinely take for quantum computers to contribute to the chemistry of nitrogen fixation. This episode is for researchers, engineers, and informed observers who want an honest, technically grounded view of where quantum computers genuinely help in chemistry — and where classical methods are more capable than the field has admitted.
What You'll Learn
Why the FeMo-cofactor became one of the quantum computing community's favorite benchmark — and why the framing around energy savings from nitrogen fixation is less accurate than it sounds
What "chemical accuracy" (~1 kcal/mol) actually means as a precision target, and why hitting it classically undermines a decade of quantum resource estimates
Why real chemical systems are only "slightly entangled" — and what that means for the general argument that quantum computers are the natural tool for quantum chemistry
The difference between a problem being hard and a problem being exponentially hard — and why that distinction matters enormously for quantum advantage claims
Where the genuine classical wall might be: bridging 15 orders of magnitude in timescale to simulate an enzyme's full catalytic mechanism — and whether quantum computers have anything to say about that
Why Chan wrote a public blog post explaining his own paper — and what that reveals about the state of discourse in quantum chemistry and the quantum computing industry
The broader impact of quantum information science on chemistry — beyond hardware, the conceptual tools of quantum information have genuinely reshaped how chemists think about many-body states
What Chan is actually working toward: a full computational understanding of the nitrogenase reaction mechanism, using machine learning to bridge timescales classically — a decade-long journey he finds genuinely exciting
Resources & Links
The Central Paper & Commentary
Zhai et al. (2026) — "Classical Solution of the FeMo-Cofactor Model to Chemical Accuracy and Its Implications" arXiv:2601.04621 — The January 2026 preprint at the heart of this episode; the classical solution of the standard 76-orbital/152-qubit FeMo-co benchmark.
Chan — Quantum Frontiers Blog Post (March 2026) The FeMo-Cofactor and Classical and Quantum Computing — Chan's own accessible commentary on the paper, written in response to widespread misinterpretation; essential reading alongside the paper.
Key Papers for Context
Chan (2024) — "Spiers Memorial Lecture: Quantum Chemistry, Classical Heuristics, and Quantum Advantage" Faraday Discussions, 254, 11–52 — The formal theoretical framework behind Chan's thinking, including the "classical heuristic cost conjecture"; the deep-dive companion to this episode.
Lee et al. (2023) — "Evaluating the Evidence for Exponential Quantum Advantage in Ground-State Quantum Chemistry" Nature Communications — Chan group's landmark 2023 paper concluding that evidence for exponential quantum advantage across chemical space has yet to be found.
Begušić & Chan (2023/2024) — "Fast Classical Simulation of Evidence for the Utility of Quantum Computing Before Fault Tolerance" Science Advances — The paper showing classical simulation on a single laptop core could reproduce and exceed IBM's 127-qubit "utility" experiment.
Bauer, Bravyi, Motta & Chan (2020) — "Quantum Algorithms for Quantum Chemistry and Quantum Materials Science" arXiv:2001.03685 — A balanced review by Chan and colleagues showing he takes quantum algorithms seriously; useful counterpoint to the skeptical framing.
Babbush et al. (2025) — "The Grand Challenge of Quantum Applications" arXiv:2511.09124 — Google Quantum AI's direct engagement with Chan's skeptical position; argues polynomial speedups may still be practically decisive.
Computational Chemistry Highlights — Review of FeMo-co Paper compchemhighlights.org — Third-party commentary from Jan Jensen (University of Copenhagen).
Tools & Software
PySCF — Python-based Simulations of Chemistry Framework https://pyscf.org — The open-source quantum chemistry package co-stewarded by Chan's group; widely used for electronic structure calculations.
BLOCK — DMRG and Matrix Product State Algorithms https://github.com/sanshar/Block — Chan group's open-source implementation of density matrix renormalization group methods; the tensor network engine underlying much of this work.
Guest Links
Chan Lab at Caltech chan-lab.caltech.edu — Research group homepage with publications, software, and group members.
Garnet Chan — Caltech Faculty Profile cce.caltech.edu/people/garnet-k-chan — Official Caltech Division of Chemistry & Chemical Engineering page.
Google Scholar Profile scholar.google.com — 34,000+ citations across theoretical chemistry and condensed matter physics.
Caltech Science Exchange — Ask a Caltech Expert: Quantum Chemistry scienceexchange.caltech.edu — Accessible overview of Chan's perspective for a general science audience.
Key Quotes
"To a good approximation, you and I are not entangled. That's essentially how people think about molecules — atoms are distinct entities, and you can define each as a local entity because its properties are not intrinsically tied up with some other thing." — Garnet Chan, explaining why most chemical systems are cla...
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