Expand description
Advanced Error Mitigation for Quantum Machine Learning
This module provides comprehensive error mitigation techniques specifically designed for quantum machine learning applications, including noise-aware training, error correction protocols, and adaptive mitigation strategies.
Structs§
- Adaptive
Config - Adaptive configuration for dynamic error mitigation
- CDRModel
- Calibration
Data - Calibration data for error mitigation
- Classical
Postprocessor - Classical
Preprocessor - Clifford
Circuit - Coherence
Time Model - Coherence time parameters
- Correction
Network - Feedback
Mechanism - Fidelity
Model - GSTData
- Gate
Error Model - Gate error models
- Measurement
Error Model - Measurement error model
- Mitigated
Inference Data - Mitigated
Training Data - Noise
Model - Noise models for quantum devices
- Noise
Predictor Model - Noise
Spectrum - Noise
Statistics - Performance
Metrics - Performance
Tracker - Performance tracker for mitigation strategies
- Process
Matrix - Quantum
Circuit - Quantum
Error Corrector - Quantum
Gate - QuantumML
Error Mitigator - Advanced error mitigation framework for quantum ML
- RBData
- Spectroscopy
Data - State
Matrix - Strategy
Selection Policy - Symmetry
Group - Temporal
Correlation Model - Temporal correlation model for noise
- Temporal
Fluctuation - TrainedCDR
Model - Training
Data Set - Verification
Circuit
Enums§
- Circuit
Folding Method - Correlation
Function - Entanglement
Protocol - Error
Type - Types of quantum errors
- Exponential
Form - Extrapolation
Method - Feature
Extraction Method - Method
Selection - Mitigation
Strategy - Error mitigation strategies for quantum ML
- Readout
Correction Method - Scaling
Function - Switching
Policy