Claude Mythos 5 发现了比已发表的最佳结果更优的量子电路——从一条将门数削减至 1/17 的电路开始

A​nthropic's STEM Fellows Program published a result this week in which C​laude M​ythos 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 A​nthropic's own or comes from the paper's own tables; none of it has been independently reproduced yet.

实际运行了什么，以及什么没有运行

The setup is worth describing precisely, because the interesting claim is not "a model wrote some code." A​nthropic gave C​laude M​ythos 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. A​nthropic's description is that C​laude 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 A​nthropic picked quantum circuits to study: the domain has a hard, cheap, automatic correctness check.

这八种原语跨越化学、优化、量子模拟、数论和数值计算。两个约束使这个领域非同寻常。量子电路没有 if-then 分支，因为叠加意味着每个分支都需要一个操作；而且它们不能丢弃信息，因为每一步都必须是可逆的。剩余的暂存值必须通过反向运行计算来反计算，这会消耗门。这迫使进行一种没有经典类比的时空权衡，而这正是搜索过程可以针对的那种权衡。

值得详细阅读的四个结果

• Divsteps / prime-field modular inversion. This is the headline. C​laude 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. C​laude's decoder cuts the gate count by a factor of 12 against G​oogle'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 C​laude 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. C​laude 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. A​nthropic's page sets C​laude'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 C​laude's design is a step inside a curve that was already falling steeply.

For scale on why the DQI number matters: G​oogle 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.

“超越已公布的最佳成绩”并不意味着什么

在任何人断章取义地引用17×之前，先说明四个诚实的局限。

The first is that this is a preprint with no independent reproduction. A​nthropic 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. A​nthropic 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 A​nthropic 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.

第四点是，八项任务中有七项在现有表述上取得了更好的分数，而有一项提出了新的表述。这是两种不同类型的结果，论文自身的论述把第八项视为更有意思的那一项，因为只在已知结构内部调参的搜索是有上限的。

可推广到量子计算之外的那部分

Strip the physics out and what A​nthropic 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 A​nthropic's loop does not need one. The point is that the loop A​nthropic 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.

在哪里可以真正运行这样的东西

C​laude M​ythos 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. A​nthropic ships every frontier step as two SKUs of the same weights: the M​ythos 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 C​laude M​ythos 5's generation, and it sits in the OrcaRouter catalogue at A​nthropic'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 C​laude M​ythos 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.

说得直白一点：这篇论文的收益来自一个循环，而不是单次提示词。任何想复现它的人，都应当为每个原语预留数百轮“提出—评估—修订”的开销，并选择一个其单轮成本自己能承受的模型。这正是把循环放在带自动故障转移的路由器之后的理由，而不是把一场十二小时的搜索押在某一家供应商当天状态好上。

接下来看什么

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 A​nthropic'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.

在那之前，公允的总结是：一个受门控的前沿模型，被指向一个具有廉价目标函数的领域，并被独自放置了 53 个小时，推动了一个人类研究人员已经攻关了八年的资源前沿。即便具体倍数仍被标注为供应商自己的数据，仅凭方法本身的力度，这也值得认真对待。

这一切都并非假设：像本次这样的发布背后那些有据可查的 AI 故障与智能体事件，均收录于 OrcaRouter AI 事件档案库中，其中每条记录都附有自己的来源、严重程度、置信度评级和存疑标记。