mitiq-error-mitigator

Error mitigation skill using Mitiq for NISQ device noise reduction

509 stars

Best use case

mitiq-error-mitigator is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Error mitigation skill using Mitiq for NISQ device noise reduction

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

Manual Installation

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

How mitiq-error-mitigator Compares

Feature / Agent	mitiq-error-mitigator	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?

Error mitigation skill using Mitiq for NISQ device noise reduction

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

# Mitiq Error Mitigator

## Purpose

Provides expert guidance on error mitigation techniques for NISQ devices using Mitiq, reducing the impact of noise without full quantum error correction.

## Capabilities

- Zero-noise extrapolation (ZNE)
- Probabilistic error cancellation (PEC)
- Clifford data regression (CDR)
- Digital dynamical decoupling
- Pauli twirling
- Learning-based error mitigation
- Noise scaling methods
- Extrapolation fitting

## Usage Guidelines

1. **Technique Selection**: Choose mitigation method based on noise characteristics
2. **Noise Scaling**: Configure appropriate noise amplification factors
3. **Extrapolation**: Select fitting model for zero-noise extrapolation
4. **Overhead Analysis**: Evaluate sampling overhead vs. accuracy improvement
5. **Validation**: Compare mitigated results with theoretical expectations

## Tools/Libraries

- Mitiq
- Qiskit
- Cirq
- PennyLane
- NumPy

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