quantrs2-tytan 0.1.3

//! Auto-generated module
//!
//! 🤖 Generated with [SplitRS](https://github.com/cool-japan/splitrs)

use scirs2_core::ndarray::{Array1, Array2, Array3};
use scirs2_core::random::prelude::*;
use scirs2_core::random::prelude::*;
use std::collections::HashMap;

use super::types::{
    AdaptiveCorrectionConfig, AmplificationStrategy, CodeParameters, DecodingAlgorithm,
    ErrorAmplificationConfig, ErrorMitigationConfig, LatticeType, LearningAdaptationConfig,
    LogicalOperators, MeasurementData, MitigatedData, PerformanceAdaptationConfig, QECConfig,
    QECError, QuantumCircuit, QuantumCodeType, QuantumErrorCorrection, ResourceEstimate,
    SyndromeExtractionMethod, ThresholdEstimationConfig, ThresholdEstimationMethod,
};

/// Quantum code trait
pub trait QuantumCode: Send + Sync {
    /// Encode logical qubits into physical qubits
    fn encode(&self, logical_state: &Array1<f64>) -> Result<Array1<f64>, QECError>;
    /// Decode physical qubits to logical qubits
    fn decode(
        &self,
        physical_state: &Array1<f64>,
        syndrome: &Array1<u8>,
    ) -> Result<Array1<f64>, QECError>;
    /// Extract error syndrome
    fn extract_syndrome(&self, physical_state: &Array1<f64>) -> Result<Array1<u8>, QECError>;
    /// Get stabilizer generators
    fn get_stabilizers(&self) -> Vec<Array1<i8>>;
    /// Get code parameters
    fn get_parameters(&self) -> CodeParameters;
    /// Check if error is correctable
    fn is_correctable(&self, error: &Array1<u8>) -> bool;
    /// Get logical operators
    fn get_logical_operators(&self) -> LogicalOperators;
}
/// Prediction model trait
pub trait PredictionModel: Send + Sync + std::fmt::Debug {
    /// Predict next syndrome
    fn predict_syndrome(&self, history: &[Array1<u8>]) -> Result<Array1<u8>, QECError>;
    /// Update model with new data
    fn update(&mut self, history: &[Array1<u8>], actual: &Array1<u8>) -> Result<(), QECError>;
    /// Get prediction confidence
    fn get_confidence(&self) -> f64;
}
/// Error mitigation strategy trait
pub trait ErrorMitigationStrategy: Send + Sync {
    /// Apply error mitigation
    fn mitigate_errors(
        &self,
        measurement_data: &MeasurementData,
    ) -> Result<MitigatedData, QECError>;
    /// Get strategy name
    fn get_strategy_name(&self) -> &str;
    /// Get mitigation parameters
    fn get_parameters(&self) -> HashMap<String, f64>;
    /// Estimate mitigation overhead
    fn estimate_overhead(&self) -> f64;
}
/// Threshold calculator trait
pub trait ThresholdCalculator: Send + Sync + std::fmt::Debug {
    /// Calculate error threshold for a given code
    fn calculate_threshold(&self, code: &dyn QuantumCode) -> Result<f64, QECError>;
    /// Get calculator name
    fn get_calculator_name(&self) -> &str;
    /// Get calculation parameters
    fn get_parameters(&self) -> HashMap<String, f64>;
}
/// Fault propagation model trait
pub trait FaultPropagationModel: Send + Sync + std::fmt::Debug {
    /// Model fault propagation
    fn propagate_faults(
        &self,
        initial_faults: &Array1<u8>,
        circuit: &QuantumCircuit,
    ) -> Result<Array1<u8>, QECError>;
    /// Get model name
    fn get_model_name(&self) -> &str;
    /// Get model parameters
    fn get_parameters(&self) -> HashMap<String, f64>;
}
/// Resource estimator trait
pub trait ResourceEstimator: Send + Sync + std::fmt::Debug {
    /// Estimate resources required
    fn estimate_resources(
        &self,
        code: &dyn QuantumCode,
        computation: &QuantumCircuit,
    ) -> Result<ResourceEstimate, QECError>;
    /// Get estimator name
    fn get_estimator_name(&self) -> &str;
}
/// Create default QEC configuration
pub fn create_default_qec_config() -> QECConfig {
    QECConfig {
        code_type: QuantumCodeType::SurfaceCode {
            lattice_type: LatticeType::Square,
        },
        code_distance: 3,
        correction_frequency: 1000.0,
        syndrome_method: SyndromeExtractionMethod::Standard,
        decoding_algorithm: DecodingAlgorithm::MWPM,
        error_mitigation: ErrorMitigationConfig {
            zero_noise_extrapolation: true,
            probabilistic_error_cancellation: false,
            symmetry_verification: true,
            virtual_distillation: false,
            error_amplification: ErrorAmplificationConfig {
                amplification_factors: vec![1.0, 1.5, 2.0],
                max_amplification: 3.0,
                strategy: AmplificationStrategy::Linear,
            },
            clifford_data_regression: false,
        },
        adaptive_correction: AdaptiveCorrectionConfig {
            adaptive_thresholding: false,
            dynamic_distance: false,
            real_time_code_switching: false,
            performance_adaptation: PerformanceAdaptationConfig {
                error_rate_threshold: 0.01,
                monitoring_window: 100,
                adaptation_sensitivity: 0.1,
                min_adaptation_interval: 10.0,
            },
            learning_adaptation: LearningAdaptationConfig {
                reinforcement_learning: false,
                learning_rate: 0.01,
                replay_buffer_size: 10000,
                update_frequency: 100,
            },
        },
        threshold_estimation: ThresholdEstimationConfig {
            real_time_estimation: false,
            estimation_method: ThresholdEstimationMethod::MonteCarlo,
            confidence_level: 0.95,
            update_frequency: 1000,
        },
    }
}
/// Create QEC system for optimization problems
pub fn create_optimization_qec(num_logical_qubits: usize) -> QuantumErrorCorrection {
    let config = create_default_qec_config();
    QuantumErrorCorrection::new(num_logical_qubits, config)
}
/// Create adaptive QEC configuration
pub fn create_adaptive_qec_config() -> QECConfig {
    let mut config = create_default_qec_config();
    config.adaptive_correction.adaptive_thresholding = true;
    config.adaptive_correction.dynamic_distance = true;
    config.adaptive_correction.real_time_code_switching = true;
    config
        .adaptive_correction
        .learning_adaptation
        .reinforcement_learning = true;
    config.threshold_estimation.real_time_estimation = true;
    config
}
#[cfg(test)]
mod tests {
    use super::*;
    #[test]
    fn test_qec_creation() {
        let mut qec = create_optimization_qec(2);
        assert_eq!(qec.num_logical_qubits, 2);
        assert_eq!(qec.config.code_distance, 3);
    }
    #[test]
    fn test_syndrome_extraction() {
        let mut qec = create_optimization_qec(1);
        let mut quantum_state = Array1::from_vec(vec![1.0, 0.0, 0.0, 0.0]);
        let mut result = qec.extract_syndrome(&quantum_state);
        assert!(result.is_err());
    }
    #[test]
    fn test_mwpm_decoding() {
        let qec = create_optimization_qec(2);
        let syndrome = Array1::from_vec(vec![1, 0, 1, 0]);
        let error_estimate = qec
            .decode_mwpm(&syndrome)
            .expect("MWPM decoding should succeed");
        assert_eq!(error_estimate.len(), qec.num_physical_qubits);
    }
    #[test]
    fn test_belief_propagation_decoding() {
        let qec = create_optimization_qec(2);
        let syndrome = Array1::from_vec(vec![1, 1, 0, 0]);
        let error_estimate = qec
            .decode_belief_propagation(&syndrome)
            .expect("Belief propagation decoding should succeed");
        assert_eq!(error_estimate.len(), qec.num_physical_qubits);
    }
    #[test]
    fn test_pauli_x_application() {
        let mut qec = create_optimization_qec(1);
        let mut state = Array1::from_vec(vec![1.0, 0.0]);
        qec.apply_pauli_x(&mut state, 0);
        assert!((state[0] - 0.0_f64).abs() < 1e-10);
        assert!((state[1] - 1.0_f64).abs() < 1e-10);
    }
    #[test]
    fn test_physical_qubit_estimation() {
        let mut config = create_default_qec_config();
        let num_physical = QuantumErrorCorrection::estimate_physical_qubits(2, &config);
        assert_eq!(num_physical, 18);
    }
}