pymatching-decoder

Minimum-weight perfect matching decoder skill for surface code error correction

509 stars

Best use case

pymatching-decoder is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Minimum-weight perfect matching decoder skill for surface code error correction

Teams using pymatching-decoder should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/pymatching-decoder/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/domains/science/quantum-computing/skills/pymatching-decoder/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/pymatching-decoder/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How pymatching-decoder Compares

Feature / Agent	pymatching-decoder	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

Minimum-weight perfect matching decoder skill for surface code error correction

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# PyMatching Decoder

## Purpose

Provides expert guidance on minimum-weight perfect matching decoding for surface codes and other topological quantum error correction codes.

## Capabilities

- MWPM decoding for surface codes
- Weighted edge matching
- Detector error model processing
- Logical error rate calculation
- Integration with Stim simulations
- Custom graph construction
- Belief propagation integration
- Parallelized decoding

## Usage Guidelines

1. **Graph Construction**: Build matching graph from detector error model
2. **Weight Assignment**: Configure edge weights based on error probabilities
3. **Decoding Execution**: Run MWPM algorithm on syndrome data
4. **Error Analysis**: Calculate logical error rates from decoding results
5. **Optimization**: Tune decoder parameters for specific code structures

## Tools/Libraries

- PyMatching
- NetworkX
- Stim
- NumPy

Related Skills

process-builder

509

from a5c-ai/babysitter

Scaffold new babysitter process definitions following SDK patterns, proper structure, and best practices. Guides the 3-phase workflow from research to implementation.

Workflow & Productivity

babysitter

509

from a5c-ai/babysitter

Orchestrate via @babysitter. Use this skill when asked to babysit a run, orchestrate a process or whenever it is called explicitly. (babysit, babysitter, orchestrate, orchestrate a run, workflow, etc.)