PyMatching Decoder

SkillDev tools

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

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the PyMatching Decoder skill

What this skill tells your AI

The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/domains/science/quantum-computing/skills/pymatching-decoder/SKILL.md and read by ahel’s review.

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

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

Tools/Libraries

PyMatching
NetworkX
Stim
NumPy

Signals

GitHub stars: 2k
Forks: 112
Last commit: Sep 2026

Advanced

Item type: skill
Key: pymatching-decoder
Source: github.com/a5c-ai/babysitter

github.com/a5c-ai/babysitter