stim-simulator

Clifford circuit simulation skill using Stim for error correction studies

509 stars

bya5c-ai

View on GitHub Installation ↓

Best use case

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

Clifford circuit simulation skill using Stim for error correction studies

Teams using stim-simulator 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/stim-simulator/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/domains/science/quantum-computing/skills/stim-simulator/SKILL.md"

Manual Installation

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

How stim-simulator Compares

Feature / Agent	stim-simulator	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?

Clifford circuit simulation skill using Stim for error correction studies

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

# Stim Simulator

## Purpose

Provides expert guidance on fast stabilizer circuit simulation using Stim, enabling large-scale quantum error correction studies and noise analysis.

## Capabilities

- Fast stabilizer circuit simulation
- Error injection and propagation
- Detector sampling
- Circuit tableau tracking
- Memory-efficient large-scale simulation
- Monte Carlo error rate estimation
- Detector error model generation
- Pauli frame simulation

## Usage Guidelines

1. **Circuit Construction**: Build Stim circuits with appropriate gates and noise
2. **Detector Definition**: Specify detectors for syndrome measurement
3. **Sampling**: Generate detector samples for decoding analysis
4. **Error Model**: Extract detector error models for decoder training
5. **Statistics**: Collect sufficient samples for statistical significance

## Tools/Libraries

- Stim
- Stimcirq
- PyMatching
- NumPy