Claude wrote quantum circuits that beat published designs on all eight tasks

Anthropic says its Claude model designed quantum circuits that improve on the best published versions for eight computing tasks, “in some cases by an order of magnitude or more.” The most concrete result: an elliptic-curve design with 17x fewer gates than the smallest earlier one, found in a single 53-hour run.

The claim reached Reddit’s r/ClaudeAI community in a thread whose title cites a figure of up to 39x. That number does not appear in Anthropic’s research post or in the abstract of the accompanying paper, both of which we read. This article therefore relies only on those two documents.

Eight tasks, one automated scoring loop

According to Anthropic’s post, published October 16, 2026, Claude received plain-language specifications for eight computational building blocks drawn from chemistry, optimization, quantum simulation, number theory and numerical computing. It wrote each circuit as Python code. An automated evaluator checked correctness and scored every design by qubits multiplied by gates, which Anthropic uses as a proxy for runtime.

The loop ran without human input between rounds. Anthropic notes this works because the target algorithms can be checked efficiently on ordinary computers. The work was led by Andi Gu in Anthropic’s STEM Fellows Program, with mentorship from Alek Kemeny and collaboration with Harvard’s Susanne Yelin and Mikhail Lukin, who is also linked to QuEra Computing. The post names the model as Claude Mythos 5.

Fewer gates for elliptic curves and error decoding

Three results get detail. For point addition on elliptic curves, one of the designs uses 12% more qubits but 17x fewer gates than the smallest previous design, according to Anthropic. For modular inversion with the divsteps algorithm, Claude produced what the company describes as the first reversible implementation that stores no branch history; the decoding rule was checked numerically and formalized in Lean.

The third concerns a Reed-Solomon decoder used in decoded quantum interferometry, an optimization approach. Claude swapped the extended Euclidean algorithm for Berlekamp-Massey and cut gate counts about 12x versus Google’s construction. It also shrank the reserve of saved values from a worst case of 665 qubits to 19, based on 403 million simulated decodings. Anthropic says about one run in 10,000 fails. The post does not detail the other five circuits.

A paper-backed claim with no hardware test yet

The accompanying arXiv paper describes an evolutionary framework in which language models propose designs and an independent evaluator verifies them. Its abstract says seven of the eight tasks show significantly lower Toffoli (a costly three-qubit gate) and qubit counts than the state of the art, while the eighth yields a new architecture for prime-field modular inversion. That is slightly more nuanced than the “all eight” framing in Anthropic’s post, where the eighth task is a different kind of gain.

The limits are stated by Anthropic itself. Gains are shown on circuit designs, not on hardware, and circuit design is only one part of a fault-tolerant machine; error correction and decoding also matter. Claude was also conservative, repeatedly second-guessing results and favoring known approaches, so researchers required several parallel approaches and sometimes prompted a broader search. The post does not mention a release of code or data, and independent reproduction has not been reported in the sources we read.

Where it sits in a long run of resource estimates

Anthropic puts the result in context: estimates for the Toffoli gates needed to find the ground-state energy of the FeMoco molecule fell from about 50 trillion in 2017 to about 340 million, roughly a 150,000-fold drop. The post attributes that to many research groups over eight years, not to Claude. The claim here is narrower but notable: a model working in an automated loop reached or beat the published frontier on specific building blocks.

For readers, the practical meaning is limited today. Nothing here runs on a quantum computer, and no product or plan is attached. What changes is the evidence that AI-generated, machine-checked designs can contribute to the field’s resource estimates. Anthropic’s recent corporate news, including its move to put 5% of its shares into a public benefit fund, is unrelated to this research, but both show the company widening its work beyond chat products. Confirmation will depend on outside groups checking the circuits against the original baselines.