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 deepquantum-differentiablegit 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/deepquantum-differentiable)<a href="https://agentmods.dev/skills/xi-zhao/openquantum/deepquantum-differentiable"><img src="https://agentmods.dev/badge/skills/xi-zhao/openquantum/deepquantum-differentiable/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/deepquantum-differentiable"><img src="https://agentmods.dev/badge/skills/xi-zhao/openquantum/deepquantum-differentiable.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 | $0.00046 | $0.01122 |
| Opus 5.5 | $0.00018 | $0.00449 |
| Sonnet 5.5 | $0.00009 | $0.00224 |
| Haiku 4.5 | $0.00005 | $0.00112 |
Grade A, and why
deepquantum-differentiable 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 — 32 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DeepQuantum 可微分量子线路
原生 SDK 路径
本能力同时支持 DeepQuantum 的原生公开 API。用户需要 自定义 qubit/光学电路、噪声、测量、编码与训练 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 deepquantum-differentiable 定位环境，按完整 SDK 工作流执行。模块导航：deepquantum、torch；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。
当用户需要用 DeepQuantum 计算线路期望、验证变分线路导数或与其他 SDK 交叉对照时，Agent 经 Harness 调用 deepquantum_local 提供的 differentiate_deepquantum_circuit。本 Skill 指导参数选择与结果解释；计算由 QubitCircuit + PyTorch 完成。
numQubits与gates定义全零初态的理想酉线路。支持 H、X、Y、Z、S、T、RX、RY、RZ、CX、CZ、SWAP；双比特门的 targets 为[control,target]（SWAP 为两个交换位），角度单位 rad。observables是与量子位数同长的 I/X/Y/Z 字符串，最左字符对应 q0。例如ZI表示 Z0，XY表示 X0Y1；输出期望保留输入顺序。加权 Hamiltonian 可由调用方按系数组合期望和 Jacobian，勿把 Pauli words 当作系数。trainableGateIndices明确选择 gates 数组中需要求导的 RX/RY/RZ；每项是独立参数。可填空数组只做仿真。Jacobian 行对应 observables，列严格对应该索引数组的顺序，不是所有旋转门的隐含顺序。amplitudes[k]为[real,imag]，概率向量按 q0 在最左的二进制字典序排列。返回 SDK 原始数值，不裁剪、不归一化；stateNormSquared用来检查数值漂移。这些是精确线路仿真的数值期望，不是 finite-shots 频数。- 参数数量、量子位数和门数由调用方决定；完整输出按 2^N 增长，可寻址数组表示限制仍适用。资源预算通过独立
execution参数设置，不改变线路语义。 - OpenQuantum Harness 对大结果自动返回
openquantum-science-summary：先查看summary.result中的期望、Jacobian、范数与单位；需要完整态矢量时，用已有文件或 shell Tool 读取会话工作区内的fullResult.path，再取 JSON 的result.amplitudes/result.probabilities。omitted: true表示数据保存在文件中，不表示零值或计算缺失；不要为了获取被省略的数据重新计算。保存失败时仍返回完整输出，科学状态保持not_evaluated。 - DeepQuantum 的部分固定门（包括 H）先在上游以 complex64 常量构造再提升精度；检查返回的原始范数，勿把舍入漂移当作物理损耗。这里只接可微分量子比特线路，未覆盖该 SDK 的光子/Fock/Gaussian/MPS 和分布式接口。
- 梯度仅对当前输入的理想线路有效，不自动构成优化收敛、抗噪声能力或硬件优势结论。对复杂电路先选择解析可核对例子，必要时与第二 SDK、参数移位或有限差分对照。
- 当前是 L1，
scientificValidation=not_evaluated；成功计算与数值回归不等于最终科学 Acceptance。
首次使用前显式运行 node scripts/setup-paper-tools.mjs deepquantum-differentiable 准备固定依赖。计算不会安装依赖、提交云/QPU 或读取凭据，数值库可能写本地缓存。上游许可为 Apache-2.0，详细版本锁见本目录 pyproject.toml 和 uv.lock；官方接口来源。
What ships with it
6 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 9f397aae1a90
- 5d ago Changed · +1 lines 6e9d88f339f6
- 19d ago First seen · 20 lines · 46 tokens per session scan A 1915efadfa2e
deepquantum-differentiable is a skill published in the GitHub repository xi-zhao/OpenQuantum (75 stars, last pushed yesterday), licensed MIT. It adds 46 tokens to every session and 1,122 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 2026-09-28.
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