vqc-trainer
Variational quantum classifier training skill with gradient optimization
Best use case
vqc-trainer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Variational quantum classifier training skill with gradient optimization
Teams using vqc-trainer 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/vqc-trainer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How vqc-trainer Compares
| Feature / Agent | vqc-trainer | 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?
Variational quantum classifier training skill with gradient optimization
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
# VQC Trainer ## Purpose Provides expert guidance on training variational quantum classifiers, including data encoding, circuit design, and gradient-based optimization. ## Capabilities - Data encoding circuit design - Variational layer construction - Gradient-based optimization (SPSA, Adam) - Cross-validation for QML - Hyperparameter tuning - Overfitting detection - Learning curve analysis - Ensemble methods ## Usage Guidelines 1. **Data Preparation**: Preprocess classical data for quantum encoding 2. **Encoding Design**: Select appropriate data encoding strategy 3. **Ansatz Design**: Build variational circuit with trainable parameters 4. **Training Setup**: Configure optimizer, learning rate, and batch size 5. **Evaluation**: Assess model on test set with proper metrics ## Tools/Libraries - Qiskit Machine Learning - PennyLane - TensorFlow Quantum - PyTorch - scikit-learn
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process-builder
Scaffold new babysitter process definitions following SDK patterns, proper structure, and best practices. Guides the 3-phase workflow from research to implementation.
babysitter
Orchestrate via @babysitter. Use this skill when asked to babysit a run, orchestrate a process or whenever it is called explicitly. (babysit, babysitter, orchestrate, orchestrate a run, workflow, etc.)
yolo
Run Babysitter autonomously with minimal manual interruption.
user-install
Install the user-level Babysitter Codex setup.
team-install
Install the team-pinned Babysitter Codex workspace setup.
retrospect
Summarize or retrospect on a completed Babysitter run.
resume
Resume an existing Babysitter run from Codex.