noise-modeler

Quantum noise modeling skill for simulation and hardware characterization

509 stars

bya5c-ai

View on GitHub Installation ↓

Best use case

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

Quantum noise modeling skill for simulation and hardware characterization

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

Manual Installation

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

How noise-modeler Compares

Feature / Agent	noise-modeler	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?

Quantum noise modeling skill for simulation and hardware characterization

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

# Noise Modeler

## Purpose

Provides expert guidance on quantum noise modeling for realistic simulation and hardware characterization analysis.

## Capabilities

- Depolarizing channel modeling
- Amplitude damping models
- Phase damping models
- Crosstalk noise models
- Readout error modeling
- Custom noise model construction
- Kraus operator representation
- Pauli channel conversion

## Usage Guidelines

1. **Noise Identification**: Determine dominant noise sources from benchmarking data
2. **Model Construction**: Build appropriate noise channels for each error type
3. **Parameter Extraction**: Fit model parameters to experimental data
4. **Simulation Integration**: Apply noise models to circuit simulations
5. **Validation**: Compare noisy simulations with hardware results

## Tools/Libraries

- Qiskit Aer
- Cirq
- PennyLane
- QuTiP
- NumPy

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