QMClaw Skills

可扩展的通用量子比特测控技能框架。

A scalable general-purpose framework for quantum measurement and control, providing reusable instruction bundles centered on SKILL.md.

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🌟 Features

Feature	Description
QMClaw Framework Overview	六层执行栈、三层决策架构、多智能体模型
Three-Layer Decision	L1 RuleEngine / L2 Learned Rules / L3 LLM Fallback
Single-Qubit Calibration	15 步标准校准流程：S21 → Spectroscopy → Rabi → T1
Tool Scheduling	工具白名单、MCP 协议、集中式调度与分布式执行
Data Analysis	T1/Rabi 拟合、XEB/RB 保真度、报告生成
Quality Assessment	SNR、Visibility、T1、保真度等质量指标体系
Experiment Plotter	学术级可视化，支持 Nature/IEEE/APS/Springer 样式

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📦 Skill Index

Skill	Version	Purpose	Trigger Keywords
QMClaw框架概述	v1.0	框架架构、六层栈、三层决策、多智能体	"QMClaw", "框架介绍", "架构"
QMClaw三层决策架构	v1.0	L1/L2/L3 决策层、RuleEngine、规则引擎	"决策", "RuleEngine", "规则"
QMClaw单比特调校工作流	v1.0	15 步校准流程、状态机、Panel 批量校准	"校准", "调校", "calibration", "tune-up"
QMClaw工具调度与后端接口	v1.0	工具白名单、MCP、Center Agent、Backend Adapter	"工具调度", "后端", "tool scheduling"
QMClaw数据分析与质量评估	v1.0	T1/Rabi 拟合、XEB 保真度、质量指标	"数据分析", "保真度", "T1", "quality"
量子比特单比特校准标定	v1.0	完整校准标定流程、参数管理、质量判定	"校准", "标定", "单比特", "qubit"
量子实验绘图与分析	v1.0	一维/二维绘图、学术图表、数据拟合	"绘图", "可视化", "plot", "figure"

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🚀 Quick Start

1. Installation

Claude Code

# Clone the repository
git clone https://github.com/YOUR_USERNAME/qmclaw-skills.git
cd qmclaw-skills

# Install skills
mkdir -p ~/.claude/skills
cp -R skills/* ~/.claude/skills/

Manual Installation

Copy the skills/ directory to your agent's skill directory:

# For Claude Code
~/.claude/skills/

# For Codex
~/.codex/skills/

# For other agents
<your-agent-skill-dir>/

2. Usage

After installation, invoke skills naturally:

Use the QMClaw framework overview skill to introduce the architecture.
执行单比特校准工作流。
如何进行 T1 拟合和 XEB 保真度计算？

3. Example: Single-Qubit Calibration

import sys
# Add your workflow directory path here
WORKFLOW_DIR = '/path/to/your/sq_workflow'  # Linux/Mac
# WORKFLOW_DIR = r'D:\path\to\sq_workflow'  # Windows
sys.path.insert(0, WORKFLOW_DIR)

import labrad
from lqms.pyle.workflow import switchSession
import sq
import numpy as np

# Connect to quantum control system
cxn = labrad.connect()
s = switchSession(cxn, user='YOUR_USER')
qobj = s.YOUR_QUBIT

# Execute 15-step calibration workflow
print("[1/15] S21 scan...")
sq.s21(qobj, update=False)

print("[4/15] Spectroscopy...")
sq.spectroscopy(qobj, freq=np.arange(2.7, 2.9, 0.001))

# ... continue with remaining steps
print("[15/15] T1 measurement...")
sq.t1(qobj, zpa=0)

Note: Adjust WORKFLOW_DIR and connection parameters according to your lab's setup.

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📁 Project Structure

qmclaw-skills/
├── README.md                    # This file
├── LICENSE                      # MIT License
├── install.md                   # Installation guide
├── CONTRIBUTING.md              # Contribution guidelines
├── .gitignore                   # Git ignore patterns
│
├── assets/                      # Project assets
│   └── qmclaw-logo.png          # Logo
│
├── skills/                      # Skills directory
│   ├── _shared/                 # Shared content
│   │   ├── core/
│   │   │   ├── quantum-params.md
│   │   │   ├── quality-standards.md
│   │   │   └── terminology.md
│   │   └── README.md
│   │
│   ├── qmclaw-framework-overview/       # Framework architecture
│   │   ├── SKILL.md
│   │   └── README.md
│   │
│   ├── qmclaw-decision-architecture/    # Three-layer decision
│   ├── qmclaw-tuneup-workflow/          # 15-step calibration
│   ├── qmclaw-tool-scheduling/          # Tool scheduling
│   ├── qmclaw-data-analysis/            # Data analysis
│   ├── quantum-calibration/             # Single-qubit calibration
│   └── quantum-experiment-plotter/      # Visualization
│
└── docs/                        # Documentation (optional)

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🏗️ Architecture

Six-Layer Execution Stack

┌─────────────────────────────────────────────────────────────┐
│  L6: Natural Language Interface    │  User commands         │
├─────────────────────────────────────────────────────────────┤
│  L5: Analysis & Report             │  Data fitting          │
├─────────────────────────────────────────────────────────────┤
│  L4: Workflow Orchestration        │  Workflow execution    │
├─────────────────────────────────────────────────────────────┤
│  L3: Tool Scheduling               │  Tool dispatch         │
├─────────────────────────────────────────────────────────────┤
│  L2: Knowledge & Rules             │  RAG, rule learning    │
├─────────────────────────────────────────────────────────────┤
│  L1: Hardware Interface            │  Instruments/simulators│
└─────────────────────────────────────────────────────────────┘

Three-Layer Decision Architecture

┌─────────────────────────────────────────────────────────────┐
│  L3: LLM Fallback Layer                                    │
│  ├── Use case: New problems, no rule match                 │
│  ├── Speed: ~300ms                                         │
│  └── Cost: API cost                                        │
├─────────────────────────────────────────────────────────────┤
│  L2: Learned Rules Layer                                   │
│  ├── Source: L3 success case promotion                     │
│  ├── Speed: <1ms                                           │
│  └── Cost: $0                                              │
├─────────────────────────────────────────────────────────────┤
│  L1: RuleEngine Layer                                      │
│  ├── Content: Expert knowledge, physics rules              │
│  ├── Speed: <1μs                                           │
│  └── Cost: $0                                              │
└─────────────────────────────────────────────────────────────┘

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📚 Documentation

Document	Description
README	This file - project overview
install.md	Detailed installation guide
skills/*/README.md	Individual skill documentation
SKILL.md	Skill specifications

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🤝 Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Adding a New Skill

See CONTRIBUTING.md for detailed guidelines.

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🙏 Acknowledgments

• QMClaw framework developed by the QMClaw team
• Inspired by nature-skills
• Built for the quantum computing community

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📧 Contact

• GitHub Issues: Issues

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Made with ❤️ for the quantum computing community