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...