Hybrid Machine Learning Models
Pre-built models combining quantum processing with classical neural networks for pattern recognition and optimization.
🌐 Explore Hybrid ML ArchitecturesMachine Learning Templates
Quantum Feature Engineering
Use quantum circuits to create new features from classical data for neural network inputs.
POST /api/v1/hybrid/ml-features
{
"input_type": "image",
"encoder": "angle_embedding"
}
Quantum-Enhanced Training
Classical neural networks with quantum-optimized gradient calculations.
POST /api/v1/hybrid/quantum-gradient
{
"model": "cnn",
"layers": 3
}
Advanced ML Methods
Quantum Embedding
Transform data into quantum state space before classical processing.
Quantum Activation Functions
Replace traditional activations with quantum state transformations.
Quantum Optimization
Optimized gradient descent using quantum tunnelinging enhancements.
Implementation Example
Quantum-Enhanced Image Classifier
Quantum-enhanced ML model for image recognition with classical post-processing.
const quantumModel = newHybridML('image-classifier', {
quantum_layers: 2,
post_processor: 'softmax'
});
const result = quantumModel.train({
data_size: 1000
});