data-encoder
Classical data encoding skill for quantum machine learning applications
npx skills add majiayu000/claude-skill-registry --skill data-encoder --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.
# Data Encoder ## Purpose Provides expert guidance on encoding classical data into quantum states for machine learning applications, balancing expressiveness with circuit complexity. ## Capabilities - Angle encoding - Amplitude encoding - IQP encoding - Hardware-efficient encoding - Encoding expressibility analysis - Data re-uploading strategies - Feature scaling for encoding - Encoding depth optimization ## Usage Guidelines 1. **Feature Analysis**: Understand data dimensionality and structure 2. **Encoding Selection**: Choose encoding based on data type and qubit budget 3. **Scaling**: Apply appropriate normalization for encoding method 4. **Depth Analysis**: Balance encoding expressivity with circuit depth 5. **Verification**: Validate encoded states capture relevant features ## Tools/Libraries - PennyLane - Qiskit Machine Learning - Cirq - TensorFlow Quantum - NumPy
- Purpose
- Capabilities
- Usage Guidelines
- Tools/Libraries
What does the data-encoder skill do?
Classical data encoding skill for quantum machine learning applications
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill data-encoder --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.