Expand description
Quantum Machine Learning (QML) integration for seamless ML workflows.
This module provides comprehensive integration between quantum simulation backends and machine learning frameworks, enabling hybrid classical-quantum algorithms, variational quantum eigensolvers (VQE), quantum neural networks (QNN), and other QML applications with automatic differentiation and hardware-aware optimization.
Structs§
- Adam
Optimizer - Adam optimizer implementation
- Layer
Config - Layer configuration
- QMLBenchmark
Results - QML benchmark results
- QMLIntegration
- QML integration engine
- QMLIntegration
Config - QML integration configuration
- QMLLayer
- Quantum ML layer definition
- QMLTraining
Stats - QML training statistics
- QMLUtils
- QML utilities
- QNNMetadata
- QNN metadata
- Quantum
Neural Network - Quantum neural network model
- Regularization
Config - Regularization configuration
- SGDOptimizer
- SGD optimizer implementation
- Training
Config - Training configuration
- Training
Example - Training example
- Training
Result - Training result
Enums§
- LRScheduler
- Learning rate schedulers
- Loss
Function - Loss functions
- Optimizer
Type - Optimizer types
- QMLFramework
- QML framework types
- QMLLayer
Type - Quantum machine learning layer types
Traits§
- QMLOptimizer
- QML optimizer trait