mitiq-error-mitigator
Error mitigation skill using Mitiq for NISQ device noise reduction
Profile →npx skills add a5c-ai/babysitter --skill mitiq-error-mitigator --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- Purpose
- Capabilities
- Usage Guidelines
- Tools/Libraries
What does the mitiq-error-mitigator skill do?
Error mitigation skill using Mitiq for NISQ device noise reduction
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill mitiq-error-mitigator --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.