The Argonne-JPMorganChase QAOA Result Hit the Quantum Wires This Week. The Peer-Reviewed Paper Was Published on June 16

by Nikolai Petrov · September 4, 2026

Technology Reporter, Frontier Tech Wire

A collaboration between Argonne National Laboratory and JPMorganChase's applied research group appeared across quantum trade coverage on Thursday under the description of a new method for studying the Quantum Approximate Optimization Algorithm at scale. It is a genuinely useful result. It is also not new in the sense that this week's dateline implies, and the numbers being attached to it come from a document that most of the coverage does not name.

Here is the paper trail, in order. The preprint is arXiv:2505.07929; its first version carries a 12 May 2025 date stamp and the title "Evidence that the Quantum Approximate Optimization Algorithm Optimizes the Sherrington-Kirkpatrick Model Efficiently in the Average Case." The peer-reviewed version was published in Physical Review Letters on 16 June 2026, in issue 24 of volume 136 — an issue dated 19 June — as article 240601, under the different title "Spin-Boson Mapping of the Quantum Approximate Optimization Algorithm." The arXiv record now displays that journal title as well. Argonne's own communications release went out on 1 September 2026. The Quantum Insider wrote it up on 3 September.

The title change is worth noting without over-reading. The preprint version led with the claim — that QAOA appears to solve this class of problem efficiently on average. The journal version leads with the method — the mapping that made the calculation tractable. That is a common and entirely defensible editorial move at a letters journal, and there is nothing here resembling a retracted claim or a scientific disagreement between the two versions. But it does mean that the paper of record is titled after a technique, while the number people will quote is an efficiency exponent that sits in the preprint's abstract.

The authors, on both versions, are Sami Boulebnane, Abid Khan, Minzhao Liu, Jeffrey Larson, Dylan Herman, Ruslan Shaydulin and Marco Pistoia, spanning Argonne and JPMorganChase.

What the work does: QAOA's solution quality improves with circuit depth, conventionally written as p, and the standard way to find out how good it gets is to simulate the whole circuit, which becomes impossible quickly. The team showed that in the infinite-size limit, a high-depth QAOA state applied to the Sherrington-Kirkpatrick spin-glass model is mathematically equivalent to a single quantum spin coupled to bosonic modes. A spin-boson system of that kind can be simulated on classical hardware using matrix product states at manageable cost. The expensive object is replaced by a cheap one, and the question of how well QAOA would have done can be answered without running QAOA.

Now the numbers, and where they come from. The following figures are read from the abstract of the first arXiv version of 2505.07929, and not from the Physical Review Letters version: the authors report numerical evidence that QAOA obtains a (1 − ε) approximation to the optimal energy with circuit depth of order n/ε to the power 1.13 in the average case; that ε falls to roughly 2.2 per cent or better at p = 160 in the infinite-size limit; and that optimised parameters applied to finite Sherrington-Kirkpatrick instances of up to 30 qubits converge towards the infinite-size prediction. The same three figures appear on Argonne's own leadership-computing case-study page for the project, which is filed under the preprint title. The abstract characterises the result as numerical evidence rather than proof.

Those are preprint figures. Argonne's 1 September release contains none of them, and neither does the trade write-up; both cite the journal article without repeating its numbers. This desk has been burned before by treating a preprint's headline figure as the peer-reviewed finding, so the distinction is stated rather than assumed: the depth-scaling exponent, the 2.2 per cent figure and the 30-qubit finite-size check are cited here to the arXiv version, not to the Physical Review Letters version of record, which was not read for this piece. Nothing here should be read as a claim that the two versions say different things about the science.

The second discipline this result demands is about what kind of claim it is. Nothing in the documents reviewed for this piece describes a run on a quantum processor; the work is classical simulation of what a quantum algorithm would produce, on one specific problem family, in a limit where the number of variables goes to infinity, with the answer expressed as numerical evidence about average-case behaviour. It is not a hardware demonstration, not a proof, and not a statement about any commercially available quantum processor.

The computation ran at the Argonne Leadership Computing Facility and the Department of Energy's National Energy Research Scientific Computing Center, under an allocation from the DOE's INCITE programme. Jeffrey Larson, a computational mathematician in Argonne's mathematics and computer science division, is quoted in the release saying that "progress in quantum computing is not just about building larger devices."

That framing cuts two ways, and the second way is the one investors should sit with. A cheap classical method for evaluating QAOA quality at high depth is a research accelerant — but it is also a raised bar. If matrix product states on a supercomputer can tell you what QAOA would return at p = 160 on the Sherrington-Kirkpatrick model, then any future claim of quantum advantage on a problem in that neighbourhood has to clear a classical baseline that just got considerably better characterised. The history of this field is substantially a history of classical simulation catching up with quantum demonstrations after the press release.

The caveats the authors and the laboratory both flag are real. The mapping is specific to the SK model, and specific to the infinite-size limit; the finite-size work extends only to 30 qubits. The result is a benchmarking and parameter-setting tool, not a substitute for running the algorithm on hardware, and it says nothing directly about the messier constrained optimisation problems that quantum vendors most often put in front of financial-services customers.

There is also a plain publishing-calendar lesson in this one. A paper that cleared peer review in mid-June surfaced in the quantum trade press in early September because a laboratory communications office chose September to write about it. Neither the science nor the date of record changed in between. For readers who track the listed quantum names and their partners, an item appearing on this week's news feed is not, by itself, evidence that anything happened this week.

The specific things worth watching from here are whether other groups reproduce the spin-boson mapping on problem families beyond the Sherrington-Kirkpatrick model, whether the depth-scaling exponent survives independent numerical work, and whether any hardware demonstration is published at circuit depths approaching the p values studied here. Nothing in the documents reviewed for this piece points to one.