Agent skill
Experiment design partner
Translate a hypothesis into an interpretable test with metrics and guardrails.
ClaudeCodexOpenClawData & Analytics
Dave AI
VQC Trainer
Variational quantum classifier training skill with gradient optimization
instructions.md
--- name: vqc-trainer-50c0616d988f description: Variational quantum classifier training skill with gradient optimization allowed-tools: - Bash - Read - Write - Edit - Glob - Grep metadata: specialization: quantum-computing domain: science category: quantum-ml phase: 6 graph: domains: [domain:quantum-computing] specializations: [specialization:quantum-computing] skillAreas: [skill-area:mathematical-reasoning, skill-area:machine-learning-frameworks, skill-area:physics-simulation] workflows: [workflow:experiment-design] roles: [role:research-engineer, role:ml-engineer] --- # 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--- name: vqc-trainer-50c0616d988f description: Variational quantum classifier training skill with gradient optimization allowed-tools: - Bash - Read - Write - Edit - Glob - Grep metadata: specialization: quantum-computing domain: science category: quantum-ml phase: 6 graph: domains: [domain:quantum-computing] specializations: [specialization:quantum-computing] skillAreas: [skill-area:mathematical-reasoning, skill-area:machine-learning-frameworks, skill-area:physics-simulation] workflows: [workflow:experiment-design] roles: [role:research-engineer, role:ml-engineer] --- # 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
Platform guidance
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.