Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add xi-zhao/OpenQuantum --skill mitiq-error-mitigationgit clone --depth 1 https://github.com/xi-zhao/OpenQuantumWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/xi-zhao/openquantum/mitiq-error-mitigation)<a href="https://agentmods.dev/skills/xi-zhao/openquantum/mitiq-error-mitigation"><img src="https://agentmods.dev/badge/skills/xi-zhao/openquantum/mitiq-error-mitigation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/xi-zhao/openquantum/mitiq-error-mitigation"><img src="https://agentmods.dev/badge/skills/xi-zhao/openquantum/mitiq-error-mitigation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00048 | $0.01343 |
| Opus 5.5 | $0.00019 | $0.00537 |
| Sonnet 5.5 | $0.00010 | $0.00269 |
| Haiku 4.5 | $0.00005 | $0.00134 |
Grade A, and why
mitiq-error-mitigation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mitiq 误差缓解实验
原生 SDK 路径
本能力同时支持 Mitiq / Cirq 的原生公开 API。用户需要 用户 executor、缩放与拟合策略、ZNE、PEC、CDR、读出缓解 时,由 Agent 在固定依赖环境中编写和运行任务代码,不限于下文便捷 Tool 的预设模型。
使用 node scripts/sdk-access.mjs inspect mitiq-error-mitigation 定位环境,按完整 SDK 工作流执行。模块导航:mitiq、cirq;导航不是 API 白名单。
下文关于模型、门集、输入与预算的限制描述便捷 Tool 的合同;原生 SDK 另受上游版本、可选依赖、宿主资源与用户授权约束。已禁用或被拒绝的动作不能借代码路径绕过。执行状态和科研验收分别报告。
当用户要评估零噪声外推、读出误差校正、概率误差抵消或 Clifford 数据回归时使用本 Skill。
它负责选择实验和解释结果,计算由 mitiq_local 连接注册的 run_mitiq_experiment Tool 执行。
不要把 Mitiq 的其他算法、任意 SDK 电路、真实硬件或误差纠正解释为本 Tool 已提供的能力。
选择与输入
zne:在局域门噪声下比较全局折叠[1,3,5]与二阶 Richardson 外推;零噪声时也可能增加方差。rem:独立、对称读出翻转;设置readoutProbability,两个校准设置计入缓解预算。门噪声可保留,REM 不消除它。pec:已知局域去极化噪声、理想补偿 Pauli;pecSamples控制准概率电路抽样数。真实噪声表征成本尚未建模。cdr:所有非 Clifford 门必须为RZ,且 observable 只含I/Z。训练成本计入预算;全 Clifford 目标及退化训练会报错。
支持 H/X/Y/Z/S/RZ/CX/CZ 门;RZ angle 单位为弧度,targets 从 0 开始。
Pauli 字符串最左字符是 q0。终端测量基变换按理想操作处理;不接受路径、代码、凭据或云任务。
depolarizingProbability 是 Cirq 的 Pauli 错误概率 p,通道为
(1-p)rho + p/3*(XrhoX+YrhoY+ZrhoZ),不能与 rho -> (1-p)rho+pI/2 的 p 混用。
shotsBudget 是每次重复、每个比较臂的总预算,包含校准和训练;整次调用严格消耗
2 * replicates * shotsBudget 个模拟 shots。默认 8 次完整重复、每臂每次 8192 shots。
seed 固定整个实验,重复之间重新生成采样、训练和校准数据。比较不同方法时保持电路、噪声、预算和重复数一致。
最小示例:
{"method":"zne","numQubits":1,"gates":[{"name":"H","targets":[0]},{"name":"RZ","targets":[0],"angle":0.7},{"name":"H","targets":[0]}],"observable":"Z","shotsBudget":8192,"replicates":8,"seed":7}
执行与解释
- 明确需要评估哪种噪声、观测量和方法;使用 Tool schema 检查支持范围。
- 若连接未开启,告知设置位置;不要绕过用户禁用状态。计算前显式准备锁定环境;计算仍可能写缓存,按
workspace-write处理。 - 读取
idealExpectation、exactNoisyExpectation、每次重复的原始/缓解估计及两臂成本。 - 用
statistics报告经验 bias、variance、RMSE 和跨重复均值标准误。rmseDifference<0仅表示本次有限样本比较改善。 - 保留越界估计与变差结果,不挑选成功 seed;shots 相同不代表门数、深度、运行时间相同。
- 遇到 CDR 训练退化,明确该实验不可辨识;调整电路/观测量或训练数后作为新实验运行,不静默更换 seed。
本能力为 L1,固定返回 scientificValidation=not_evaluated。没有 Acceptance Profile 和中央验收链,
不可宣称科学验收通过、普遍降噪、量子优势或真实硬件有效。
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago Changed · +11 lines ca428c13ac4f
- 22d ago Changed · +4 lines dc9a91d1e16c
- 1mo ago First seen · 50 lines · 48 tokens per session scan A 03b9687334f2
mitiq-error-mitigation is a skill published in the GitHub repository xi-zhao/OpenQuantum (75 stars, last pushed yesterday), licensed MIT. It adds 48 tokens to every session and 1,343 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-15.
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