VQC Trainer

SkillDev tools

Variational quantum classifier training skill with gradient optimization

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the VQC Trainer skill

What this skill tells your AI

The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/domains/science/quantum-computing/skills/vqc-trainer/SKILL.md and read by ahel’s review.

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

Data Preparation: Preprocess classical data for quantum encoding
Encoding Design: Select appropriate data encoding strategy
Ansatz Design: Build variational circuit with trainable parameters
Training Setup: Configure optimizer, learning rate, and batch size
Evaluation: Assess model on test set with proper metrics

Tools/Libraries

Qiskit Machine Learning
PennyLane
TensorFlow Quantum
PyTorch
scikit-learn

Signals

GitHub stars: 2k
Forks: 112
Last commit: Sep 2026

Advanced

Item type: skill
Key: vqc-trainer
Source: github.com/a5c-ai/babysitter

github.com/a5c-ai/babysitter