data-encoder
Classical data encoding skill for quantum machine learning applications
Best use case
data-encoder is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Classical data encoding skill for quantum machine learning applications
Teams using data-encoder 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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/data-encoder/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How data-encoder Compares
| Feature / Agent | data-encoder | 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?
Classical data encoding skill for quantum machine learning applications
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.
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SKILL.md Source
# 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
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