quantum-guide-algorithms-gradients

quantum-guide-algorithms-gradients is a skill for Claude Code from xi-zhao/OpenQuantum. It costs 36 tokens per session (707 once invoked), scanned A, original, MIT.

A guide for calculating derivatives of quantum circuits. A derivative measures how an output changes when a circuit parameter changes, which is needed when training or tuning a quantum model.

In plain words
What is it for?
Use it for finite differences, parameter-shift gradients, reverse-mode gradients, stochastic SPSA gradients, linear-combination gradients, and quantum Fisher information.
Why use it?
It avoids choosing a gradient method without knowing how it estimates change. The available approaches also expose settings such as finite-difference size, batches, and random seeds.

Skill for Claude Code

Written for Claude Code: disable-model-invocation in frontmatter. Also seen: installed under .agents/ (shared by several agents).

Good fit Use it for finite differences, parameter-shift gradients, reverse-mode gradients, stochastic SPSA gradients, linear-combination gradients, and quantum Fisher information.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/xi-zhao/openquantum/quantum-guide-algorithms-gradients
Install

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.

Any agent
npx skills add xi-zhao/OpenQuantum --skill quantum-guide-algorithms-gradients
Clone the repo
git clone --depth 1 https://github.com/xi-zhao/OpenQuantum

Made for: Claude Code.

Wrote 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.

agentmods badge for quantum-guide-algorithms-gradients

README.md
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Your own site
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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.

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Your own site · 80×15
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Per session 36 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 707 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00036 $0.00707
Opus 5.5 $0.00014 $0.00283
Sonnet 5.5 $0.00007 $0.00141
Haiku 4.5 $0.00004 $0.00071

Measured 4d ago against content hash ac86d600be5a, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

quantum-guide-algorithms-gradients 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.

.agents/skills/quantum-guide-algorithms-gradients/SKILL.md · 34 lines

What it actually says

algorithms/gradients

本地开源适配。上游指南 ID:algorithms/gradients。

分类导航:保留用户显式调用;自动任务直接选择叶子方法 Skill,或使用 quantum-algorithms 查找。

选择工作流

按用户的数学问题、输入表示、计算规模和需要的输出选择一个叶子 Skill。知识解释可直接使用原理;计算任务加载叶子 Skill,使用其中的开源实现和现有代码执行 Tool。无需为阅读指南启动额外服务。

  • quantum-finite-difference:Qiskit FiniteDiffEstimatorGradient,中心差分与显式 epsilon。
  • quantum-linear-combination:Qiskit LinCombEstimatorGradient,实际生成线性组合导数电路。
  • quantum-parameter-shift:Qiskit ParamShiftEstimatorGradient,RY 参数移位规则。
  • quantum-qfi:Qiskit QFI/ReverseQGT,纯态量子 Fisher 信息;不将其等同于任意测量的经典 Fisher 信息。
  • quantum-reverse:Qiskit ReverseEstimatorGradient,反向态矢量导数。
  • quantum-spsa:Qiskit SPSAEstimatorGradient,显式 epsilon、批量与随机种子;单次估计有统计误差。

全量条目见覆盖表。如果用户描述跨领域,按实际依赖组合相关叶子 Skill;不要要求用户先阅读整条目录链。

来源与边界

上游 MIT 指南:algorithms/gradients。原文作为参考保存在固定检索库,本文件将执行路线改为开源 SDK。来源摘要和算法模块对应关系见coverage.json,许可证与改动说明见NOTICE。

自定义 SDK 程序

示例用于解释和接口核验,不是可执行任务的边界。用户需要其他模型、数据、算法步骤、梯度或后端时,按原生 SDK 工作台在固定环境中编写并执行自定义代码;由 Agent 完成程序与工件交付。使用 quantum-algorithms 环境定位相关 SDK,保留本算法的数学假设与数值误差说明。

Changes

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.

  1. 4d ago Changed · +4 lines ac86d600be5a
  2. 22d ago First seen · 30 lines · 36 tokens per session scan A bd2c463fc8f7

Subscribe to this mod's changes

quantum-guide-algorithms-gradients is a skill published in the GitHub repository xi-zhao/OpenQuantum (75 stars, last pushed yesterday), licensed MIT. It adds 36 tokens to every session and 707 once invoked, about $0.0001 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 2026-09-25.

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