[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-slug-claude-quantum-circuit-redesign":3,"topics-all":38,"news-related-a7a8ca05-6134-4b27-8dc0-ae3c0a764630":57},{"id":4,"title":5,"summary":6,"content":7,"original_url":8,"source_id":9,"tags":10,"translations":24,"news_slug":31,"published_at":32,"created_at":33,"modified_at":34,"is_published":35,"publish_type":36,"image_url":14,"view_count":37},"a7a8ca05-6134-4b27-8dc0-ae3c0a764630","Claude 自主设计量子电路:53 小时拿到一个数量级提速","Anthropic 用 Claude Mythos 5 设计 8 个量子电路,53 小时闭循环迭代出比现有最佳实现更省 qubit、更省 gate 的设计。divsteps 给出首个无分支历史存储的可逆实现,DQI Reed-Solomon 解码器门数减少 12 倍。","Claude 用了 53 小时,把 8 个量子电路的关键算子重做了一遍——其中几项比过去几年人类研究者给出的最佳方案还要再快一个数量级。这是 Anthropic 在 10 月 16 日公布的\"用 Claude 设计更高效量子电路\"研究的核心结论,论文同步挂到 arXiv(arXiv:2610.22367)。\n\n## 量子电路为什么难\n\n量子计算机不像经典计算机那样\"if-then\"分叉——所有输入被叠加跑同一段指令,每个回合要执行所有分支。每一步必须可逆,中间结果不能简单丢弃,只能反运算回去,这又吃 qubit 又吃 gate。一个标志性案例:FeMoco 模拟所需 Toffoli 门从 2017 年的 50 万亿次降到 2025 年的 3.4 亿次,约 15 万倍,几乎都来自电路重设计,不是硬件升级。\n\nAnthropic 这次想问的是:把同样的\"设计→评估→迭代\"循环交给一个 LLM,会发生什么。\n\n## 53 小时,8 个电路\n\n团队给 Claude Mythos 5 一段平实的数学描述(比如 mod p 域上椭圆曲线点加法的规则),配一个自动评估器:判定正确性,按\"qubit 数 × gate 数\"打分。Claude 反复提交实现、拿反馈、改下一版,中间几乎不需要人介入。8 个电路——化学、优化、量子模拟、数论、数值计算各占其一——Claude 给出的新设计全部用更少的 qubit 和更少的 gate 跑赢已发表最佳方案。两项最值得关注。\n\n## divsteps:消除分支信息存储\n\n椭圆曲线点加法里最贵的步骤是模逆运算。divsteps 在经典计算机上高效,但搬到量子电路里就卡在\"可逆\"这条规则上:每一轮迭代选了什么分支,都得存下来才能反向计算,等于多出一个巨大的 qubit 账本。Claude 的思路很巧:观察数对结构后认为\"分支信息\"本身可以从数对状态里**直接解码**,不用单独存。早期几条简单解码规则小测例上能用,放到 256 位就崩。Claude 没有继续穷举,而是转向理论——证明了一组新代数恒等式,基于这些恒等式推导出正确的解码规则,然后用大规模数值仿真验证,再把推导形式化到 Lean 里面。结果是首个不需要分支历史存储的可逆 divsteps 实现。一个电路多用 12% qubit,gate 数直接降到原来的 1\u002F17。\n\n## Reed-Solomon 解码器:门数砍到 1\u002F12\n\n第二项是 DQI 算法里最贵的 Reed-Solomon 解码。Google Quantum AI 在 2025 年给出的电路基于扩展欧几里得算法:输入不同就要走不同分支,搬到量子电路里只能把所有路径铺平,gate 数被撑到很大。Claude 换思路用 Berlekamp-Massey——每轮步骤一致,正适合量子的\"单条固定指令\"约束。但反向化又带来新问题:每轮的临时值必须保留或可重建。Claude 同样尝试重建而不是存储;跑 4.03 亿次仿真,发现几乎所有情况下只需要存一个 19 qubit 的值——最坏情况下原本要 665 qubit 才能存下。换算法、改架构、缩资源预算三件事叠在一起,整体让解码器门数减少 12 倍。\n\n## 所以呢\n\n这件事不只在\"AI 又跑赢一项 benchmark\",而在于它是 AI 真的在**没有现成模板的方向上推导出新理论、写出 Lean 证明、把电路跑通并量化提速**的完整闭环。divsteps 那段尤其值得记住:Claude 不是把已有算法微调,而是先识别出\"可逆性\"的本质矛盾,再回头证明一组新数学恒等式,把这套恒等式当作电路重设计的基础——这是常规\"自动优化\"做不到的。\n\n对量子硬件路线图来说,这次工作的具体含义比 headline 更克制:它没有声称\"通用量子计算机要来了\",只是把已发表问题里某些关键算子的开销往下推了一档。但 Anthropic 点出了更宏大的可能——既然电路设计、纠错码、错误解码、控制硬件这些环节都有\"可代码化、能自动判对错、有客观打分\"的特征,那未来是不是可以让 AI 把这些环节**联合优化**?这篇工作大概就是这条路线上第一个可量化的起点。\n\n对不在量子领域的人来说,这件事更直接的提示是:在严肃的科学发现里,AI 当前最现实的定位,是\"能在闭环里持续追问数学结构、直到推出新结论的协作伙伴\",而不仅是写代码的工具。divsteps 那一节里 Claude 写 Lean 验证的环节,就是这套新定位的具体形态。","https:\u002F\u002Fwww.anthropic.com\u002Fresearch\u002Fdesigning-efficient-quantum-circuits","1fa87d30-d9f3-4752-b3be-0373933b3aaf",[11,15,18,21],{"id":12,"name":13,"slug":13,"description":14,"color":14},"5e628969-6d2a-437f-998a-104e4b16cfb1","ai-progress",null,{"id":16,"name":17,"slug":17,"description":14,"color":14},"23544f6a-eea1-4f05-aa8d-749ca862d5d2","anthropic",{"id":19,"name":20,"slug":20,"description":14,"color":14},"dca4d0ab-7994-43a7-839e-7756fc77344a","claude",{"id":22,"name":23,"slug":23,"description":14,"color":14},"01598627-1ea6-4b27-a5d8-874971571a71","llm",[25],{"id":26,"lang":27,"title":28,"summary":29,"content":30},"ae57fd86-db40-4050-830c-c1a8260e09e2","en","Claude redesigns quantum circuits: 12x speedup in 53h","Anthropic used Claude Mythos 5 to redesign 8 quantum circuits. divsteps: first reversible design without branch history. DQI gate count drops 12x.","In 53 hours, Claude reworked the key operators of 8 quantum circuits—on several of them, beating the best published designs from the past few years by another order of magnitude. That is the headline from Anthropic's October 16 study, \"Designing more efficient quantum circuits with Claude,\" with the paper simultaneously posted to arXiv (arXiv:2610.22367).\n\n## Why quantum circuits are hard\n\nA quantum computer cannot branch like a classical one: every input runs through the same fixed instruction sequence, so every branch must be executed in every round. Each step must be reversible; intermediate results cannot simply be discarded, only uncomputed at additional gate cost. Quantum circuit design is therefore a constant trade between qubit count (space) and gate count (time). A landmark case: the Toffoli gate count for FeMoco simulation dropped from 50 trillion in 2017 to 340 million in 2025, roughly 150,000x, almost entirely from circuit redesign rather than hardware improvements.\n\nThe question Anthropic asked: what happens when you hand the same \"design, evaluate, iterate\" loop to an LLM?\n\n## 53 hours, 8 circuits\n\nThe team gave Claude Mythos 5 a plain-language math brief (for example, the rule for elliptic curve point addition over a prime modulus) plus an automated evaluator that checked each candidate circuit for correctness and scored it on qubit count multiplied by gate count. Claude submitted implementations, received feedback, revised, and submitted again, with almost no human intervention. Across 8 circuits spanning chemistry, optimization, quantum simulation, number theory, and numerical computing, Claude's designs beat the best published implementations on both qubit count and gate count. Two stand out.\n\n## divsteps: eliminating branch history storage\n\nThe most expensive step in elliptic curve point addition is modular inversion. divsteps is efficient on classical computers but runs into the reversibility wall on quantum circuits: which branch was taken at each iteration must be stored in order to uncompute, ballooning qubit cost. Claude's idea: instead of storing branch information, decode it directly from the pair state. Early decoding heuristics worked on small cases and broke at 256-bit sizes. Rather than continue enumerating, Claude pivoted to theory, proved a new set of algebraic identities, derived the correct decoding rule from those identities, validated it at scale with numerical simulations, and formalized the derivation in Lean. The result is the first reversible divsteps implementation that needs no branch history storage. One design in the family uses 12 percent more qubits but cuts gate count to 1\u002F17 of the previous smallest.\n\n## Reed-Solomon decoder: 12x fewer gates\n\nThe second highlight is the Reed-Solomon decoder, the most expensive step in Decoded Quantum Interferometry (DQI). Google Quantum AI's 2025 circuit used the extended Euclidean algorithm, where different inputs take different paths, so all paths had to be unrolled in the quantum circuit and gate count grew large. Claude switched to Berlekamp-Massey decoding, where every iteration takes the same kind of step and the same number of rounds, a much better fit for quantum's fixed-instruction requirement. But reversibility introduces a new problem: each round's temporary values must either be stored or reconstructed. Claude again chose reconstruction; running 403 million sample decodings showed that in nearly all cases only a single 19-qubit value needs to be saved, instead of the worst-case 665. Algorithm swap, architectural change, and tighter resource budget together cut decoder gate count by 12x.\n\n## What this means\n\nThis is not another \"AI beats a benchmark\" story. It is a closed loop in which an AI derived new theory on a problem without a ready template, wrote Lean proofs, ran the resulting circuits, and quantified the speedup. The divsteps case is the one to remember: Claude did not micro-optimize an existing algorithm. It identified the reversibility constraint as the core obstacle, proved a new set of algebraic identities from scratch, and used those identities as the foundation for a circuit redesign—something ordinary automated optimization cannot do.\n\nFor quantum hardware roadmaps, the practical implication is more modest than the headline: this work did not claim a universal quantum computer is around the corner. It pushed the cost of a handful of well-studied operators down by another order of magnitude. But Anthropic pointed to the broader possibility: since circuit design, error-correcting codes, error decoders, and hardware control all share the property that designs can be written as code, correctness can be checked automatically, and there is an objective score to optimize, could an AI eventually co-optimize all of them rather than each being tuned separately? This paper is arguably the first quantifiable waypoint on that path.\n\nFor readers outside quantum, the more immediate takeaway is about how AI shows up in serious scientific discovery today: less as a coding tool, more as a collaborator that can stay in a closed loop, keep probing a mathematical structure, and arrive at a new result. The Lean proof Claude wrote as part of the divsteps verification is one concrete shape this new role takes.","claude-quantum-circuit-redesign","2026-10-19T10:00:00Z","2026-10-19T11:14:05.651101Z","2026-10-19T11:14:05.651123Z",true,"agent",73,[39,48],{"slug":40,"tag_slug":40,"title_zh":41,"title_en":42,"intro_zh":43,"intro_en":44,"id":45,"is_active":35,"created_at":46,"modified_at":47},"ai-for-science","AI for Science 2026：从 UniPert 到 GPT-Rosalind 的硬核进化","AI for Science 2026: from UniPert to GPT-Rosalind","生命科学、化学材料、物理世界模型——AI 正在从\"语言工具\"变成\"实验伙伴\"。本专题收录 AI 在三大科学方向的关键节点：UniPert 统一基因与化学扰动空间、GPT-Rosalind 端到端生命科学推理、达摩院 AI 智能体 28 小时找到 4 种超导新材料、Anthropic Claude Science 把工作台做成标准品。","From language tool to lab partner — AI is reshaping life sciences, chemistry\u002Fmaterials, and physical world models. This topic covers the key milestones: UniPert unifying genetic-chemical perturbation spaces, GPT-Rosalind's end-to-end life-sciences reasoning, DAMO's AI agent discovering 4 superconducting materials in 28 hours, and Anthropic's Claude Science workbench going mainstream.","988a4300-5fab-41c4-b5d8-63711a2dc757","2026-09-10T01:34:15.296649Z","2026-09-10T01:34:15.296663Z",{"slug":49,"tag_slug":49,"title_zh":50,"title_en":51,"intro_zh":52,"intro_en":53,"id":54,"is_active":35,"created_at":55,"modified_at":56},"h3-series","MiniMax H3 系列：从开源权重到 35 倍吞吐","MiniMax H3 Series: from open weights to 35x throughput","MiniMax H3 自 2026 年 8 月开源以来节奏密集：官方把生成、参考与编辑收回一个模型；ComfyUI 当天压进 RTX 3060；摩尔线程 3 小时完成国产 GPU 适配；fal 后训练版把吞吐拉到 35 倍；FastH3 蒸馏再砍推理成本。本专题持续追踪 H3 的发布—开源—蒸馏—部署全链路。","Since MiniMax open-sourced H3 in August 2026 the pace has been relentless: one unified omni-modal model, same-day ComfyUI support down to an RTX 3060, a 3-hour Day-0 port to Moore Threads GPUs, fal's post-trained H3 Max at 35x throughput, and FastH3 distillation cutting inference cost further. This topic tracks the full H3 chain — release, open weights, distillation, deployment.","83ef0daa-3c31-4cb3-86ed-e5ee58654d5f","2026-09-08T07:33:19.942193Z","2026-09-08T07:33:19.942209Z",{"items":58},[59,64,69,74,78,83],{"id":60,"title":61,"news_slug":62,"published_at":63},"390c2437-4e4f-45ec-8270-67c5bfa4fa47","ChatGPT、Claude、Grok、Gemini 罕见同时下线,周四早晨全球 AI 集体失声","chatgpt-claude-grok-gemini-thursday-outage","2026-09-05T06:00:00+00:00",{"id":65,"title":66,"news_slug":67,"published_at":68},"39724847-fdc9-4199-ac46-311e7b49d385","Ramp 数据复盘 Fable 5:旗舰上市两月仅占企业 Anthropic 支出 11%,70 倍价差压住前沿模型溢价","ramp-data-fable-5-adoption-plateaus","2026-08-26T08:00:00+00:00",{"id":70,"title":71,"news_slug":72,"published_at":73},"1051d676-8ed9-4448-b0d5-8db4b844f41f","Claude Fable 5 上线两个月,为什么企业只把 11% 的账单花给最强模型","claude-fable-5-11-percent-anthropic-spend","2026-08-25T06:00:00+00:00",{"id":75,"title":76,"news_slug":77,"published_at":73},"e1724d68-bf0d-4b3f-8047-147796d5d52e","Ramp 8 月指数:Fable 5 企业份额停滞 11%,OpenAI 旗舰跑赢两倍","anthropic-fable-5-plateau-11-percent",{"id":79,"title":80,"news_slug":81,"published_at":82},"454f9530-20d7-428c-82d9-9175fa5b883a","Claude 推黎曼 zeta 下界到 67.2%：60 subagent + Lean","claude-zeta-bound-67-percent-multi-agent-lean","2026-08-17T07:00:00+00:00",{"id":84,"title":85,"news_slug":86,"published_at":87},"db4ffdac-3734-41a2-8e32-67feaa7341bd","Claude 冲击黎曼猜想\"失败\",却顺手改写了 37 年没人动过的数学纪录","claude-riemann-zeta-67-percent-record","2026-08-16T23:30:00+00:00"]