Expand description
ML-Driven Circuit Optimization and Hardware Prediction with SciRS2
This module provides comprehensive machine learning-driven circuit optimization and hardware performance prediction using SciRS2’s advanced ML capabilities, statistical analysis, and optimization algorithms for intelligent quantum computing.
Re-exports§
pub use fallback_scirs2::*;pub use config::*;pub use ensemble::*;pub use features::*;pub use hardware::*;pub use monitoring::*;pub use online_learning::*;pub use optimization::*;pub use training::*;pub use transfer_learning::*;pub use validation::*;
Modules§
- config
- ML Optimization Configuration Types
- ensemble
- Ensemble Learning Configuration Types
- fallback_
scirs2 - Fallback implementations for SciRS2 functionality when the feature is not available
- features
- Feature Extraction Configuration Types
- hardware
- Hardware Prediction Configuration Types
- monitoring
- ML Monitoring Configuration Types
- online_
learning - Online Learning Configuration Types
- optimization
- Optimization Strategy Configuration Types
- training
- ML Training Configuration Types
- transfer_
learning - Transfer Learning Configuration Types
- validation
- ML Validation Configuration Types