vqnet-learning SKILL.md skillA
vqnet-learning from xi-zhao/openquantum is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add xi-zhao/openquantum/vqnet-learning@git:20261012.5ce3b3ePins git:20261012.5ce3b3e in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- xi-zhao/openquantum
- Path
.agents/skills/vqnet-learning/SKILL.md- Versions recorded
- 2
- Latest version
- git:20261012.5ce3b3e, committed Oct 12, 2026
- First version recorded
- Sep 28, 2026
- Size
- 2,975 bytes
- Repository stars
- 71
- License
- MIT
用本源 VQNet 的 CPU 变分电路模拟器计算 Pauli 能量、计算基概率，以及旋转参数的自动微分梯度。.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# VQNet 可微电路 <!-- BEGIN NATIVE SDK ACCESS --> ## 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](../quantum-sdk-workbench/references/workflow.md)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。 <!-- END NATIVE SDK ACCESS --> 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。 …
Read the whole file at its exact version.
How to install
mdr add xi-zhao/openquantum/vqnet-learning@sha256:730792eaf8a389dcThe line at the top of this page pins the label git:20261012.5ce3b3e, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_q4opyn6z65jm23p3)
1 badge image fetches in 30 days. What the badge shows.
Versions
The registry has recorded 2 versions of this file, between Sep 28, 2026 and Oct 12, 2026, about one every 14 days.
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20261012.5ce3b3e latest | 2026-10-12 | 5ce3b3e | 2,975 B | A | view · diff |
| git:20260928.4386e3d | 2026-09-28 | 4386e3d | 1,932 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (2975 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
xi-zhao/openquantum · 71 stars · license MIT · pushed 2026-11-05 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_q4opyn6z65jm23p3 GET https://markdownregistry.com/api/v1/resolve?ref=xi-zhao/openquantum/vqnet-learning GET https://markdownregistry.com/api/v1/blob/730792eaf8a389dc7797b68fda08e79a591d532bedee019ca1bd0598ed9ce5e3
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.