Integrations

QCML provides integrations with popular machine learning frameworks to make quantum-enhanced machine learning accessible.

Available Integrations:

• Scikit-learn Integration
  • Classes
  • Core Estimators
    • QCMLRegressor
    • QCMLClassifier
  • Utilities and Hooks
    • Serialization Functions
  • Usage Examples
    • Basic Regression Example
    • Basic Classification Example
    • Model Persistence Example
    • Integration with Scikit-learn Pipelines
  • Parameter Configuration
    • Model Parameters
    • Advanced Configuration
  • Compatibility
  • Performance Tips
  • Troubleshooting

Overview

The integrations module provides wrapper classes and utilities to use QCML’s quantum-enhanced models with existing machine learning workflows. Each integration maintains compatibility with the target framework’s API while adding quantum capabilities.

Currently supported integrations:

• Scikit-learn: Compatible estimators for classification and regression tasks