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 vqnet-learninggit 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/vqnet-learning)<a href="https://agentmods.dev/skills/xi-zhao/openquantum/vqnet-learning"><img src="https://agentmods.dev/badge/skills/xi-zhao/openquantum/vqnet-learning/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/vqnet-learning"><img src="https://agentmods.dev/badge/skills/xi-zhao/openquantum/vqnet-learning.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 | $0.00039 | $0.00795 |
| Opus 5.5 | $0.00016 | $0.00318 |
| Sonnet 5.5 | $0.00008 | $0.00159 |
| Haiku 4.5 | $0.00004 | $0.00080 |
Grade A, and why
vqnet-learning 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.
What it actually says
VQNet 可微电路
原生 SDK 路径
本能力同时支持 VQNet 的原生公开 API。用户需要 tensor、量子/经典层、模型、数据与优化器 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 vqnet-learning 定位环境，按完整 SDK 工作流执行。模块导航：pyvqnet；导航不是 API 白名单。
原生程序导入库前，按环境定位器的 bootstrap 执行启动兼容处理：macOS 在导入前设置 wheel 与 Python 动态库路径并重启当前脚本一次。这不限制公开 API，也不替换数值算法。
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。
Agent 经 Harness 调用 vqnet_local 的 differentiate_vqnet_circuit。输入 numQubits、结构化 gates 和实系数 Pauli terms，得到期望值、概率与每个旋转门的自动微分梯度。适用于变分电路单点梯度与参数敏感性检查，不执行训练流程。
支持 H/S/T/X/Y/Z、CX/CZ、RX/RY/RZ。旋转角单位 rad；CX 首个 target 为控制位。Pauli 字符串和输出概率的位串均以 qubit 0 为最左位；字符串必须与量子位数等长。重复 Pauli 项合并，恒等项允许。每个旋转门的角度是独立参数，gradients[].gateIndex 指原始门列表中的位置；调用方复用一个模型参数时，应自行按链式法则合并相应梯度。
使用 VQNet VQC CPU complex128 状态矢量与 SDK 自动微分。无 shots 误差，但有浮点误差，内存随量子位数指数增长。不调用 GPU、云端、真实硬件或外部服务。
固定 pyvqnet 2.18.1，Python 3.12。Python 源文件有 Apache-2.0 声明，但本次发行 wheel 未提供完整许可元数据；连接保守地默认关闭，用户应按上游发行许可自行启用。先显式运行 node scripts/setup-paper-tools.mjs vqnet-learning。
当前 macOS ARM wheel 含构建机动态库路径。Worker 在读取输入前，用当前 Python 和 SDK 自带 libs 的固定路径重启自身，解决加载问题，不修改 wheel 或宿主文件。SDK 在 macOS 导入时把 OMP 线程数设为 1，因此部署的 execution.threads 不保证覆盖 SDK 内部行为。
最大副作用为 workspace-write（本地缓存）。当前 L1，scientificValidation=not_evaluated；开发测试不等于科学验收。
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 · +13 lines 730792eaf8a3
- 18d ago First seen · 19 lines · 39 tokens per session scan A 2edd6c362392
vqnet-learning is a skill published in the GitHub repository xi-zhao/OpenQuantum (75 stars, last pushed yesterday), licensed MIT. It adds 39 tokens to every session and 795 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 2026-09-29.
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