noise-modeler
Quantum noise modeling skill for simulation and hardware characterization
npx skills add majiayu000/claude-skill-registry --skill noise-modeler --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.
# 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
- Purpose
- Capabilities
- Usage Guidelines
- Tools/Libraries
What does the noise-modeler skill do?
Quantum noise modeling skill for simulation and hardware characterization
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill noise-modeler --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 majiayu000/claude-skill-registry, a repository with 534 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.