Mathematical physics·OpenAI Math·Result 281·Lean formalisation listed
QAOA attains the SK optimum in the thermodynamic-first limit
One of the 372 results in OpenAI's public mathematics catalogue, filed under mathematical physics: 2 preprints, the latest dated 27 September 2026. This page quotes OpenAI's own summary of the claim, links the papers and abstracts, and will embed the animation.
Latest paper
QAOA attains the SK ground-state energy in the thermodynamic-first limit
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What OpenAI claims
Proves that QAOA approaches the ground-state energy of the Gaussian zero-field Sherrington–Kirkpatrick model when system size tends to infinity before circuit depth. For every accuracy, finite depth and deterministic angles independent of size and disorder achieve the required limiting expected energy per spin. This also yields leading-order optimal expected MaxCut values on large-degree random regular graphs, with size tending to infinity before degree. (Lean)
Papers (2)
QAOA attains the SK ground-state energy in the thermodynamic-first limit
We prove that the Quantum Approximate Optimization Algorithm (QAOA) approaches the ground-state energy per spin of the Gaussian zero-field Sherrington–Kirkpatrick model when system size tends to infinity first and circuit depth then increases. For every accuracy, some finite depth and deterministic angles, independent of system size and disorder, achieve that accuracy in the limiting expected energy per spin using the standard cost Hamiltonian and transverse-field mixer. This proves the eventual Parisi-optimality conjecture of Basso, Farhi, Marwaha, Villalonga, and Zhou in its fixed-parameter thermodynamic formulation. We give no quantitative bound on the required depth or efficient angle-selection procedure.
Full support of the zero-temperature Sherrington-Kirkpatrick order parameter
We prove that every admissible integrable minimizer of the zero-temperature Parisi functional for the pure, zero-field Sherrington–Kirkpatrick model has full relative Stieltjes support on [0,1). Thus its support has no gaps at any overlap scale below one. We use the covariance normalization ξ(t)=t2/2.
Status
Preprint. The papers have not been independently reviewed. Treat the result as OpenAI's claim until other mathematicians have checked it. If a video exists, it illustrates the statement; it is not the proof.
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