
Claude Mythos 5 znalazł układy kwantowe lepsze od opublikowanych najlepszych — zaczynając od takiego, który redukuje liczbę bramek 17×
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Anthropic's STEM Fellows Program published a result this week in which Claude Mythos 5 was handed plain-language specifications for eight quantum circuits and, working in a closed loop against an automated evaluator, returned designs that beat the best published ones on seven of them — and on the eighth produced something the literature did not have: a reversible divsteps implementation with no stored branch history, one member of which uses 12% more qubits but 17× fewer gates than the smallest previous design. The paper, Discovering state-of-the-art quantum circuits with large language models, is on arXiv (2610.22367, submitted 15 October 2026, 46 pages), and its lead author is Andi Gu, a fellow in the program, mentored by quantum physicist Alek Kemeny and working with Susanne Yelin and Mikhail Lukin of Harvard. Every number below is Anthropic's own or comes from the paper's own tables; none of it has been independently reproduced yet.
Co zostało faktycznie uruchomione, a co nie
The setup is worth describing precisely, because the interesting claim is not "a model wrote some code." Anthropic gave Claude Mythos 5 a specification for a computational primitive, and the model proposed a circuit as Python. A separate evaluator — a program, not a model — checked the circuit for correctness and scored it on qubit count multiplied by gate count, a proxy for runtime. The score went back to the model, which revised. There was no human in the loop between rounds. Anthropic's description is that Claude worked "largely on its own."
Two things about that loop are doing the heavy lifting. The first is that the evaluator is independent of the proposer, so a plausible-looking wrong answer scores zero rather than winning an argument. The second is that the objective is a number, which means the model can tell whether a revision helped. This is the same shape as a test suite driving a coding agent, and it is the reason Anthropic picked quantum circuits to study: the domain has a hard, cheap, automatic correctness check.
Osiem prymitywów obejmuje chemię, optymalizację, symulację kwantową, teorię liczb i obliczenia numeryczne. Dwa ograniczenia czynią tę dziedzinę nietypową. Obwody kwantowe nie mają rozgałęzień if-then, ponieważ superpozycja oznacza, że operacja jest potrzebna dla każdej gałęzi; nie mogą też odrzucać informacji, bo każdy krok musi być odwracalny. Pozostałe wartości pomocnicze trzeba odwrócić, uruchamiając obliczenie wstecz, co kosztuje bramki. Wymusza to kompromis między przestrzenią a czasem, który nie ma klasycznego odpowiednika, i jest to dokładnie ten rodzaj kompromisu, na który można ukierunkować proces poszukiwań.
Cztery wyniki warte szczegółowego przeczytania
• Divsteps / prime-field modular inversion. This is the headline. Claude produced the first reversible divsteps implementation that stores no branch history — a construction the paper describes as making divsteps, a classical algorithm previously impractical for reversible integer arithmetic, newly workable. The usable outcome is a Pareto shift rather than a single win: a member of the resulting design family spends 12% more qubits and 17× fewer gates than the smallest previous design, so a compiler with a qubit budget to spare can now buy gates with it.
• DQI / Reed–Solomon decoding. Claude's decoder cuts the gate count by a factor of 12 against Google's previous construction, partly by swapping the extended Euclidean algorithm for Berlekamp–Massey decoding. The storage detail is the interesting one: worst-case handling of exceptions across 35 rounds would take 665 qubits, and after a simulator ran 403 million sample decodings Claude instead reserved room for a single value — 19 qubits — accepting that roughly one run in 10,000 needs more and fails, a rate inside the allowed error budget.
• Elliptic-curve point addition. A single 53-hour run with minimal human input — an initial brief plus occasional prompts — produced a new approach. Claude proved the algebraic identities, tested the formulas in large-scale numerical simulations, and formalised the claim in Lean, which is the kind of self-checking that makes the result harder to dismiss as a plausible-looking artifact.
• The FeMoco trajectory, which needs a caveat. Anthropic's page sets Claude's result against the 2017 estimate for simulating the iron–molybdenum cofactor: about 50 trillion Toffoli gates, falling to roughly 340 million, a reduction of some 150,000-fold, which the page describes as a few hours on a future machine with about three million physical qubits. Read that as a field-wide trajectory, not a single result. The page itself attributes the eight-year reduction to many research groups, and the honest reading is that Claude's design is a step inside a curve that was already falling steeply.
For scale on why the DQI number matters: Google Quantum AI's own estimates imply one OPI problem would take about three months on today's Frontier supercomputer against roughly an hour on a future quantum computer running DQI — and most of that hour goes to Reed–Solomon decoding. Cutting decoder gate count by 12× is a cut to the dominant term.

Czego nie oznacza „pobicie opublikowanego najlepszego wyniku”
Cztery uczciwe ograniczenia, przedstawione, zanim ktokolwiek zacytuje 17× poza kontekstem.
The first is that this is a preprint with no independent reproduction. Anthropic ran the evaluator; the comparison baselines come from the paper's own tables, with prior designs sourced to Schrottenloher (2026), Luo et al. (2026) and Khattar et al. (2025). The claimed advantage is a claim until someone reruns the harness.
The second is that circuit design is one component of a quantum computer, and the components are usually designed separately. Anthropic notes the possibility that error correction, compilation and hardware constraints could be optimised jointly by the same method — which is a research direction, not a result.
The third is a failure mode Anthropic reports against itself: in early experiments the model was conservative, repeatedly second-guessing well-supported gains and double- or triple-checking designs that were probably fine. That caution produced a real benefit once — it prompted the Lean proofs on the divsteps result — but it also biased the search toward established approaches. The mitigation was structural: require several qualitatively different approaches developed in parallel, and occasionally prompt the model to widen its search. Anyone building evaluator-driven loops on any frontier model will recognise that failure mode, and the parallel-approaches fix is the transferable part.
Po czwarte, siedem z ośmiu zadań uzyskało lepszy wynik w istniejącej formulacji, a jedno dało nową formulację. To różne rodzaje wyników, a samo ujęcie w artykule traktuje ósmy jako ten interesujący, ponieważ poszukiwanie, które jedynie dostraja parametry wewnątrz znanej struktury, ma pułap.

Część, która uogólnia się poza informatykę kwantową
Strip the physics out and what Anthropic built is a proposer and an independent checker, wired together, with a scalar objective that both sides can see. That pattern is now the standard shape of serious agentic work: one model generates, another verifies, and the loop stops when a test passes rather than when the output reads well. The lesson the quantum paper adds is about honesty of the critic — the evaluator here is not a second model offering an opinion. It is a simulator that either accepts the circuit or does not.
Where a program cannot be the judge, the second-best structure is a different model as judge, and that is a routing problem rather than a research one. OrcaRouter's routing DSL composes several models into a single call, so a cheap model can run the high-volume proposer branch while a stronger one is invoked only on the verification step, and model fusion runs a panel of models against the same prompt when a single opinion is not worth trusting. The point is not that a router designed the circuits — it did not, and Anthropic's loop does not need one. The point is that the loop Anthropic describes is buildable on a single endpoint across 200-plus models with no second contract, and the cost of the loop is dominated by how many proposer rounds you can afford.
Gdzie faktycznie można uruchomić coś takiego
Claude Mythos 5 itself cannot be called. It is restricted to vetted partners and verification-program members, it does not appear on any public API, and it is not routed here — a gated model cannot be routed to the public. What is reachable is its public sibling. Anthropic ships every frontier step as two SKUs of the same weights: the Mythos SKU with cyber and biology safeguards tuned for vetted defenders, and the Fable SKU with the full safeguard stack left on. Claude Fable 5 is the ungated member of Claude Mythos 5's generation, and it sits in the OrcaRouter catalogue at Anthropic's list price of $10 per million input tokens and $50 per million output tokens, passed through at 0% markup, with the newer Claude Fable 5.1 — the public twin of Claude Mythos 5.1 — alongside it at the same rate and a 75% lower cache read. That is the practical route to the capability tier the paper is about, and it costs the vendor's own number rather than a marked-up one.
Warto powiedzieć wprost: zyski z tej pracy pochodziły z pętli, a nie z pojedynczego promptu. Ktokolwiek próbuje to odtworzyć, powinien założyć budżet na setki rund typu zaproponuj–oceń–popraw dla każdego prymitywu i wybrać model, którego koszt za rundę jest do zaakceptowania. To jest argument za umieszczeniem pętli za routerem z automatycznym przełączaniem awaryjnym, zamiast stawiania dwunastogodzinnego przeszukiwania na to, że jednemu dostawcy trafi się dobry dzień.

Co obejrzeć dalej
Three checkable things. An independent rerun of the evaluator on the divsteps and DQI circuits, which is the only way the 17× and 12× become more than Anthropic's numbers. Whether the harness itself is released, because the feedback loop is the reusable contribution and the circuits are the demonstration. And whether the same proposer-and-evaluator pattern shows up on a problem where correctness is not machine-checkable, since that is where the model-as-judge question stops being academic.
Do tego czasu uczciwym podsumowaniem jest to, że model frontierowy z ograniczonym dostępem, skierowany na dziedzinę z tanią funkcją celu i pozostawiony sam sobie na 53 godziny, przesunął granicę zasobową, którą badacze przesuwali przez osiem lat. Warto potraktować to poważnie ze względu na samą metodę, nawet jeśli konkretne mnożniki wciąż są oznaczone jako własne dane dostawcy.
Nic z tego nie jest hipotetyczne: udokumentowane awarie AI i incydenty z agentami stojące za takimi wydaniami jak to są zebrane w the OrcaRouter AI Incident Archive, gdzie każdy wpis zawiera własne źródła, stopień ważności, ocenę pewności i oznaczenie sporne.
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