Expand description
Machine Learning-based circuit optimization
This module provides ML-driven optimization techniques for quantum circuits, including reinforcement learning for gate scheduling, neural networks for pattern recognition, and automated hyperparameter tuning.
Structs§
- Circuit
Metrics - Circuit metrics for ML training
- Decision
Tree - Decision tree for random forest
- Feature
Extractor - Feature extractor for circuits
- Improvement
Metrics - Improvement metrics
- Layer
- Neural network layer
- MLCircuit
Optimizer - ML-based optimizer
- MLCircuit
Representation - Circuit representation for ML algorithms
- MLOptimization
Result - ML optimization result
- MLOptimizer
Config - ML optimizer configuration
- Training
Example - Training example for supervised learning
- Tree
Node - Tree node
Enums§
- Acquisition
Function - Acquisition functions for Bayesian optimization
- Activation
Function - Activation functions
- MLModel
- ML model abstraction
- MLStrategy
- ML optimization strategy
- Optimization
Target - Optimization target