quantrs2-device 0.1.3

//! Noise characterization and analysis for dynamical decoupling

use scirs2_core::ndarray::{Array1, Array2, Array3};
use std::collections::HashMap;
use std::time::Duration;

use super::{
    config::{DDNoiseConfig, NoiseType},
    performance::DDPerformanceAnalysis,
    sequences::DDSequence,
};
use crate::DeviceResult;

/// Noise analysis results for DD sequences
#[derive(Debug, Clone)]
pub struct DDNoiseAnalysis {
    /// Noise characterization results
    pub noise_characterization: NoiseCharacterization,
    /// Spectral analysis results
    pub spectral_analysis: Option<SpectralAnalysis>,
    /// Temporal correlation analysis
    pub temporal_analysis: Option<TemporalAnalysis>,
    /// Spatial correlation analysis
    pub spatial_analysis: Option<SpatialAnalysis>,
    /// Non-Markovian analysis
    pub non_markovian_analysis: Option<NonMarkovianAnalysis>,
    /// Noise suppression effectiveness
    pub suppression_effectiveness: SuppressionEffectiveness,
}

/// Comprehensive noise characterization
#[derive(Debug, Clone)]
pub struct NoiseCharacterization {
    /// Noise types present
    pub noise_types: HashMap<NoiseType, NoiseCharacteristics>,
    /// Dominant noise sources
    pub dominant_sources: Vec<NoiseSource>,
    /// Noise environment classification
    pub environment_classification: NoiseEnvironmentClassification,
    /// Time-dependent noise parameters
    pub time_dependent_parameters: TimeDependentNoiseParameters,
}

/// Characteristics of a specific noise type
#[derive(Debug, Clone)]
pub struct NoiseCharacteristics {
    /// Noise strength
    pub strength: f64,
    /// Frequency characteristics
    pub frequency_profile: FrequencyProfile,
    /// Temporal characteristics
    pub temporal_profile: TemporalProfile,
    /// Spatial characteristics
    pub spatial_profile: Option<SpatialProfile>,
    /// Statistical properties
    pub statistical_properties: NoiseStatistics,
}

/// Frequency profile of noise
#[derive(Debug, Clone)]
pub struct FrequencyProfile {
    /// Power spectral density
    pub power_spectral_density: Array1<f64>,
    /// Frequency bins
    pub frequency_bins: Array1<f64>,
    /// Dominant frequencies
    pub dominant_frequencies: Vec<f64>,
    /// Spectral shape classification
    pub spectral_shape: SpectralShape,
}

/// Spectral shape classification
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum SpectralShape {
    /// White noise (flat spectrum)
    White,
    /// Pink noise (1/f)
    Pink,
    /// Red noise (1/f²)
    Red,
    /// Blue noise (f)
    Blue,
    /// Violet noise (f²)
    Violet,
    /// Custom spectrum
    Custom,
}

/// Temporal profile of noise
#[derive(Debug, Clone)]
pub struct TemporalProfile {
    /// Autocorrelation function
    pub autocorrelation: Array1<f64>,
    /// Correlation time
    pub correlation_time: Duration,
    /// Non-Gaussianity measures
    pub non_gaussianity: NonGaussianityMeasures,
    /// Intermittency characteristics
    pub intermittency: IntermittencyCharacteristics,
}

/// Non-Gaussianity measures
#[derive(Debug, Clone)]
pub struct NonGaussianityMeasures {
    /// Skewness
    pub skewness: f64,
    /// Kurtosis
    pub kurtosis: f64,
    /// Higher order cumulants
    pub higher_cumulants: Array1<f64>,
    /// Non-Gaussianity index
    pub non_gaussianity_index: f64,
}

/// Intermittency characteristics
#[derive(Debug, Clone)]
pub struct IntermittencyCharacteristics {
    /// Burst duration statistics
    pub burst_durations: Array1<f64>,
    /// Quiet period statistics
    pub quiet_periods: Array1<f64>,
    /// Intermittency factor
    pub intermittency_factor: f64,
    /// Burst intensity distribution
    pub burst_intensity: Array1<f64>,
}

/// Spatial profile of noise
#[derive(Debug, Clone)]
pub struct SpatialProfile {
    /// Spatial correlation matrix
    pub spatial_correlation: Array2<f64>,
    /// Correlation length
    pub correlation_length: f64,
    /// Spatial coherence
    pub spatial_coherence: f64,
    /// Spatial gradient
    pub spatial_gradient: Array1<f64>,
}

/// Statistical properties of noise
#[derive(Debug, Clone)]
pub struct NoiseStatistics {
    /// Mean value
    pub mean: f64,
    /// Variance
    pub variance: f64,
    /// Distribution type
    pub distribution_type: NoiseDistribution,
    /// Distribution parameters
    pub distribution_parameters: Array1<f64>,
    /// Entropy measures
    pub entropy_measures: EntropyMeasures,
}

/// Noise distribution types
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum NoiseDistribution {
    Gaussian,
    Uniform,
    Exponential,
    Gamma,
    Beta,
    LogNormal,
    Levy,
    StudentT,
    Custom,
}

/// Entropy measures
#[derive(Debug, Clone)]
pub struct EntropyMeasures {
    /// Shannon entropy
    pub shannon_entropy: f64,
    /// Renyi entropy
    pub renyi_entropy: Array1<f64>,
    /// Tsallis entropy
    pub tsallis_entropy: f64,
    /// Differential entropy
    pub differential_entropy: f64,
}

/// Noise source identification
#[derive(Debug, Clone)]
pub struct NoiseSource {
    /// Source type
    pub source_type: NoiseSourceType,
    /// Source strength
    pub strength: f64,
    /// Source location
    pub location: Option<NoiseSourceLocation>,
    /// Frequency range
    pub frequency_range: (f64, f64),
    /// Mitigation strategies
    pub mitigation_strategies: Vec<String>,
}

/// Types of noise sources
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum NoiseSourceType {
    /// Environmental electromagnetic interference
    ElectromagneticInterference,
    /// Thermal fluctuations
    ThermalFluctuations,
    /// Voltage fluctuations
    VoltageFluctuations,
    /// Mechanical vibrations
    MechanicalVibrations,
    /// Control system noise
    ControlSystemNoise,
    /// Quantum shot noise
    QuantumShotNoise,
    /// Cross-talk between qubits
    CrossTalk,
    /// Measurement back-action
    MeasurementBackAction,
    /// Unknown source
    Unknown,
}

/// Noise source location
#[derive(Debug, Clone)]
pub struct NoiseSourceLocation {
    /// Physical coordinates
    pub coordinates: Array1<f64>,
    /// Affected qubits
    pub affected_qubits: Vec<quantrs2_core::qubit::QubitId>,
    /// Propagation pattern
    pub propagation_pattern: PropagationPattern,
}

/// Noise propagation pattern
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum PropagationPattern {
    Local,
    Nearest,
    Global,
    Exponential,
    PowerLaw,
    Custom,
}

/// Noise environment classification
#[derive(Debug, Clone)]
pub struct NoiseEnvironmentClassification {
    /// Environment type
    pub environment_type: NoiseEnvironmentType,
    /// Complexity score
    pub complexity_score: f64,
    /// Predictability score
    pub predictability_score: f64,
    /// Stationarity assessment
    pub stationarity: StationarityAssessment,
}

/// Types of noise environments
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum NoiseEnvironmentType {
    /// Simple Markovian noise
    SimpleMarkovian,
    /// Complex Markovian noise
    ComplexMarkovian,
    /// Non-Markovian noise
    NonMarkovian,
    /// Hybrid environment
    Hybrid,
    /// Exotic environment
    Exotic,
}

/// Stationarity assessment
#[derive(Debug, Clone)]
pub struct StationarityAssessment {
    /// Is stationary
    pub is_stationary: bool,
    /// Stationarity confidence
    pub confidence: f64,
    /// Non-stationary components
    pub non_stationary_components: Vec<NonStationaryComponent>,
    /// Trend analysis
    pub trend_analysis: TrendAnalysis,
}

/// Non-stationary component
#[derive(Debug, Clone)]
pub struct NonStationaryComponent {
    /// Component type
    pub component_type: NonStationaryType,
    /// Time scale
    pub time_scale: Duration,
    /// Strength variation
    pub strength_variation: f64,
    /// Frequency drift
    pub frequency_drift: f64,
}

/// Types of non-stationary behavior
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum NonStationaryType {
    LinearTrend,
    PeriodicModulation,
    RandomWalk,
    Switching,
    Burst,
    Drift,
}

/// Trend analysis
#[derive(Debug, Clone)]
pub struct TrendAnalysis {
    /// Linear trend slope
    pub linear_slope: f64,
    /// Trend significance
    pub trend_significance: f64,
    /// Cyclical components
    pub cyclical_components: Vec<CyclicalComponent>,
    /// Change points
    pub change_points: Vec<ChangePoint>,
}

/// Cyclical component
#[derive(Debug, Clone)]
pub struct CyclicalComponent {
    /// Period
    pub period: Duration,
    /// Amplitude
    pub amplitude: f64,
    /// Phase
    pub phase: f64,
    /// Confidence
    pub confidence: f64,
}

/// Change point in noise characteristics
#[derive(Debug, Clone)]
pub struct ChangePoint {
    /// Time of change
    pub time: Duration,
    /// Change magnitude
    pub magnitude: f64,
    /// Change type
    pub change_type: ChangeType,
    /// Detection confidence
    pub confidence: f64,
}

/// Types of changes
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum ChangeType {
    MeanShift,
    VarianceChange,
    DistributionChange,
    CorrelationChange,
    FrequencyShift,
}

/// Time-dependent noise parameters
#[derive(Debug, Clone)]
pub struct TimeDependentNoiseParameters {
    /// Parameter evolution
    pub parameter_evolution: HashMap<String, Array1<f64>>,
    /// Evolution time grid
    pub time_grid: Array1<f64>,
    /// Parameter correlations
    pub parameter_correlations: Array2<f64>,
    /// Prediction model
    pub prediction_model: Option<ParameterPredictionModel>,
}

/// Parameter prediction model
#[derive(Debug, Clone)]
pub struct ParameterPredictionModel {
    /// Model type
    pub model_type: PredictionModelType,
    /// Model parameters
    pub model_parameters: Array1<f64>,
    /// Prediction accuracy
    pub accuracy: f64,
    /// Prediction horizon
    pub horizon: Duration,
}

/// Types of prediction models
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum PredictionModelType {
    AutoRegressive,
    MovingAverage,
    ARIMA,
    StateSpace,
    NeuralNetwork,
    GaussianProcess,
}

/// Spectral analysis results
#[derive(Debug, Clone)]
pub struct SpectralAnalysis {
    /// Power spectral density analysis
    pub psd_analysis: PSDAnalysis,
    /// Cross-spectral analysis
    pub cross_spectral_analysis: CrossSpectralAnalysis,
    /// Coherence analysis
    pub coherence_analysis: CoherenceAnalysis,
    /// Spectral features
    pub spectral_features: SpectralFeatures,
}

/// Power spectral density analysis
#[derive(Debug, Clone)]
pub struct PSDAnalysis {
    /// PSD estimate
    pub psd_estimate: Array1<f64>,
    /// Frequency bins
    pub frequency_bins: Array1<f64>,
    /// Confidence intervals
    pub confidence_intervals: Array2<f64>,
    /// Peak detection
    pub peaks: Vec<SpectralPeak>,
}

/// Spectral peak
#[derive(Debug, Clone)]
pub struct SpectralPeak {
    /// Peak frequency
    pub frequency: f64,
    /// Peak power
    pub power: f64,
    /// Peak width
    pub width: f64,
    /// Peak significance
    pub significance: f64,
}

/// Cross-spectral analysis
#[derive(Debug, Clone)]
pub struct CrossSpectralAnalysis {
    /// Cross-power spectral density
    pub cross_psd: Array2<f64>,
    /// Phase relationships
    pub phase_relationships: Array2<f64>,
    /// Frequency coupling
    pub frequency_coupling: Array2<f64>,
    /// Coherence function
    pub coherence_function: Array1<f64>,
}

/// Coherence analysis
#[derive(Debug, Clone)]
pub struct CoherenceAnalysis {
    /// Coherence matrix
    pub coherence_matrix: Array2<f64>,
    /// Significant coherences
    pub significant_coherences: Vec<(usize, usize, f64)>,
    /// Coherence network
    pub coherence_network: CoherenceNetwork,
}

/// Coherence network
#[derive(Debug, Clone)]
pub struct CoherenceNetwork {
    /// Network adjacency matrix
    pub adjacency_matrix: Array2<f64>,
    /// Network metrics
    pub network_metrics: NetworkMetrics,
    /// Community structure
    pub communities: Vec<Vec<usize>>,
}

/// Network metrics
#[derive(Debug, Clone)]
pub struct NetworkMetrics {
    /// Clustering coefficient
    pub clustering_coefficient: f64,
    /// Average path length
    pub average_path_length: f64,
    /// Network density
    pub density: f64,
    /// Small-world index
    pub small_world_index: f64,
}

/// Spectral features
#[derive(Debug, Clone)]
pub struct SpectralFeatures {
    /// Spectral centroid
    pub spectral_centroid: f64,
    /// Spectral bandwidth
    pub spectral_bandwidth: f64,
    /// Spectral rolloff
    pub spectral_rolloff: f64,
    /// Spectral flatness
    pub spectral_flatness: f64,
    /// Spectral entropy
    pub spectral_entropy: f64,
}

/// Temporal correlation analysis
#[derive(Debug, Clone)]
pub struct TemporalAnalysis {
    /// Autocorrelation analysis
    pub autocorrelation_analysis: AutocorrelationAnalysis,
    /// Memory effects
    pub memory_effects: MemoryEffects,
    /// Temporal scaling
    pub temporal_scaling: TemporalScaling,
    /// Burst statistics
    pub burst_statistics: BurstStatistics,
}

/// Autocorrelation analysis
#[derive(Debug, Clone)]
pub struct AutocorrelationAnalysis {
    /// Autocorrelation function
    pub autocorrelation_function: Array1<f64>,
    /// Time lags
    pub time_lags: Array1<f64>,
    /// Decay time constants
    pub decay_constants: Array1<f64>,
    /// Oscillatory components
    pub oscillatory_components: Vec<OscillatoryComponent>,
}

/// Oscillatory component in autocorrelation
#[derive(Debug, Clone)]
pub struct OscillatoryComponent {
    /// Frequency
    pub frequency: f64,
    /// Amplitude
    pub amplitude: f64,
    /// Decay rate
    pub decay_rate: f64,
    /// Phase
    pub phase: f64,
}

/// Memory effects in noise
#[derive(Debug, Clone)]
pub struct MemoryEffects {
    /// Memory kernel
    pub memory_kernel: Array1<f64>,
    /// Memory time
    pub memory_time: Duration,
    /// Non-Markovianity measure
    pub non_markovianity: f64,
    /// Information backflow
    pub information_backflow: Array1<f64>,
}

/// Temporal scaling properties
#[derive(Debug, Clone)]
pub struct TemporalScaling {
    /// Hurst exponent
    pub hurst_exponent: f64,
    /// Scaling exponents
    pub scaling_exponents: Array1<f64>,
    /// Multifractal spectrum
    pub multifractal_spectrum: MultifractalSpectrum,
    /// Long-range correlations
    pub long_range_correlations: LongRangeCorrelations,
}

/// Multifractal spectrum
#[derive(Debug, Clone)]
pub struct MultifractalSpectrum {
    /// Singularity strengths
    pub singularity_strengths: Array1<f64>,
    /// Fractal dimensions
    pub fractal_dimensions: Array1<f64>,
    /// Multifractality parameter
    pub multifractality_parameter: f64,
}

/// Long-range correlations
#[derive(Debug, Clone)]
pub struct LongRangeCorrelations {
    /// Correlation exponent
    pub correlation_exponent: f64,
    /// Correlation length
    pub correlation_length: f64,
    /// Power-law range
    pub power_law_range: (f64, f64),
    /// Crossover scales
    pub crossover_scales: Array1<f64>,
}

/// Burst statistics
#[derive(Debug, Clone)]
pub struct BurstStatistics {
    /// Burst size distribution
    pub burst_size_distribution: Array1<f64>,
    /// Burst duration distribution
    pub burst_duration_distribution: Array1<f64>,
    /// Inter-burst intervals
    pub inter_burst_intervals: Array1<f64>,
    /// Burstiness parameter
    pub burstiness_parameter: f64,
}

/// Spatial correlation analysis
#[derive(Debug, Clone)]
pub struct SpatialAnalysis {
    /// Spatial correlation functions
    pub spatial_correlations: SpatialCorrelations,
    /// Spatial coherence
    pub spatial_coherence: SpatialCoherence,
    /// Spatial patterns
    pub spatial_patterns: SpatialPatterns,
    /// Propagation analysis
    pub propagation_analysis: PropagationAnalysis,
}

/// Spatial correlations
#[derive(Debug, Clone)]
pub struct SpatialCorrelations {
    /// Correlation matrix
    pub correlationmatrix: Array2<f64>,
    /// Correlation functions
    pub correlation_functions: Array2<f64>,
    /// Distance matrix
    pub distance_matrix: Array2<f64>,
    /// Correlation length scales
    pub length_scales: Array1<f64>,
}

/// Spatial coherence
#[derive(Debug, Clone)]
pub struct SpatialCoherence {
    /// Coherence matrix
    pub coherence_matrix: Array2<f64>,
    /// Coherence length
    pub coherence_length: f64,
    /// Coherence anisotropy
    pub coherence_anisotropy: f64,
    /// Principal coherence directions
    pub principal_directions: Array2<f64>,
}

/// Spatial patterns
#[derive(Debug, Clone)]
pub struct SpatialPatterns {
    /// Pattern types detected
    pub pattern_types: Vec<SpatialPatternType>,
    /// Pattern strength
    pub pattern_strength: Array1<f64>,
    /// Pattern wavelengths
    pub pattern_wavelengths: Array1<f64>,
    /// Pattern orientations
    pub pattern_orientations: Array1<f64>,
}

/// Types of spatial patterns
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum SpatialPatternType {
    Uniform,
    Gradient,
    Periodic,
    Random,
    Clustered,
    Striped,
    Spiral,
    Fractal,
}

/// Propagation analysis
#[derive(Debug, Clone)]
pub struct PropagationAnalysis {
    /// Propagation speed
    pub propagation_speed: f64,
    /// Propagation direction
    pub propagation_direction: Array1<f64>,
    /// Dispersion relation
    pub dispersion_relation: DispersionRelation,
    /// Attenuation characteristics
    pub attenuation: AttenuationCharacteristics,
}

/// Dispersion relation
#[derive(Debug, Clone)]
pub struct DispersionRelation {
    /// Frequency-wavevector relationship
    pub frequency_wavevector: Array2<f64>,
    /// Group velocity
    pub group_velocity: f64,
    /// Phase velocity
    pub phase_velocity: f64,
    /// Dispersion parameter
    pub dispersion_parameter: f64,
}

/// Attenuation characteristics
#[derive(Debug, Clone)]
pub struct AttenuationCharacteristics {
    /// Attenuation coefficient
    pub attenuation_coefficient: f64,
    /// Penetration depth
    pub penetration_depth: f64,
    /// Attenuation anisotropy
    pub attenuation_anisotropy: f64,
    /// Frequency dependence
    pub frequency_dependence: Array1<f64>,
}

/// Non-Markovian analysis
#[derive(Debug, Clone)]
pub struct NonMarkovianAnalysis {
    /// Non-Markovianity measures
    pub non_markovianity_measures: NonMarkovianityMeasures,
    /// Memory effects characterization
    pub memory_characterization: MemoryCharacterization,
    /// Information flow analysis
    pub information_flow: InformationFlowAnalysis,
    /// Non-Markovian models
    pub non_markovian_models: Vec<NonMarkovianModel>,
}

/// Non-Markovianity measures
#[derive(Debug, Clone)]
pub struct NonMarkovianityMeasures {
    /// BLP measure
    pub blp_measure: f64,
    /// RHP measure
    pub rhp_measure: f64,
    /// LLI measure
    pub lli_measure: f64,
    /// Trace distance measure
    pub trace_distance_measure: f64,
    /// Volume measure
    pub volume_measure: f64,
}

/// Memory characterization
#[derive(Debug, Clone)]
pub struct MemoryCharacterization {
    /// Memory kernel reconstruction
    pub memory_kernel: Array1<f64>,
    /// Memory depth
    pub memory_depth: Duration,
    /// Memory strength
    pub memory_strength: f64,
    /// Memory type classification
    pub memory_type: MemoryType,
}

/// Types of memory
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum MemoryType {
    ShortTerm,
    LongTerm,
    Infinite,
    Oscillatory,
    Algebraic,
    Exponential,
}

/// Information flow analysis
#[derive(Debug, Clone)]
pub struct InformationFlowAnalysis {
    /// Information backflow
    pub information_backflow: Array1<f64>,
    /// Transfer entropy
    pub transfer_entropy: f64,
    /// Mutual information
    pub mutual_information: f64,
    /// Causal relationships
    pub causal_relationships: CausalRelationships,
}

/// Causal relationships
#[derive(Debug, Clone)]
pub struct CausalRelationships {
    /// Causal network
    pub causal_network: Array2<f64>,
    /// Causal strength
    pub causal_strength: Array2<f64>,
    /// Causal delays
    pub causal_delays: Array2<f64>,
    /// Feedback loops
    pub feedback_loops: Vec<FeedbackLoop>,
}

/// Feedback loop
#[derive(Debug, Clone)]
pub struct FeedbackLoop {
    /// Loop nodes
    pub nodes: Vec<usize>,
    /// Loop strength
    pub strength: f64,
    /// Loop delay
    pub delay: Duration,
    /// Loop stability
    pub stability: f64,
}

/// Non-Markovian model
#[derive(Debug, Clone)]
pub struct NonMarkovianModel {
    /// Model type
    pub model_type: NonMarkovianModelType,
    /// Model parameters
    pub parameters: Array1<f64>,
    /// Model accuracy
    pub accuracy: f64,
    /// Predictive power
    pub predictive_power: f64,
}

/// Types of non-Markovian models
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum NonMarkovianModelType {
    GeneralizedLangevin,
    FractionalBrownian,
    MemoryKernel,
    StochasticDelay,
    HierarchicalEquations,
}

/// Noise suppression effectiveness
#[derive(Debug, Clone)]
pub struct SuppressionEffectiveness {
    /// Suppression by noise type
    pub suppression_by_type: HashMap<NoiseType, f64>,
    /// Overall suppression factor
    pub overall_suppression: f64,
    /// Frequency-dependent suppression
    pub frequency_suppression: Array1<f64>,
    /// Temporal suppression profile
    pub temporal_suppression: Array1<f64>,
    /// Suppression mechanisms
    pub suppression_mechanisms: Vec<SuppressionMechanism>,
}

/// Suppression mechanism
#[derive(Debug, Clone)]
pub struct SuppressionMechanism {
    /// Mechanism type
    pub mechanism_type: SuppressionMechanismType,
    /// Effectiveness
    pub effectiveness: f64,
    /// Target noise types
    pub target_noise_types: Vec<NoiseType>,
    /// Operating frequency range
    pub frequency_range: (f64, f64),
}

/// Types of suppression mechanisms
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum SuppressionMechanismType {
    Averaging,
    Decoupling,
    Refocusing,
    Cancellation,
    Filtering,
    Coherence,
}

/// DD noise analyzer
pub struct DDNoiseAnalyzer {
    pub config: DDNoiseConfig,
}

impl DDNoiseAnalyzer {
    /// Create new noise analyzer
    pub const fn new(config: DDNoiseConfig) -> Self {
        Self { config }
    }

    /// Analyze noise characteristics
    pub fn analyze_noise_characteristics(
        &self,
        sequence: &DDSequence,
        performance_analysis: &DDPerformanceAnalysis,
    ) -> DeviceResult<DDNoiseAnalysis> {
        println!("Starting DD noise analysis");

        let noise_characterization = self.characterize_noise(sequence, performance_analysis)?;

        let spectral_analysis = if self.config.spectral_analysis {
            Some(self.perform_spectral_analysis(sequence)?)
        } else {
            None
        };

        let temporal_analysis = if self.config.temporal_correlation {
            Some(self.perform_temporal_analysis(sequence)?)
        } else {
            None
        };

        let spatial_analysis = if self.config.spatial_correlation {
            Some(self.perform_spatial_analysis(sequence)?)
        } else {
            None
        };

        let non_markovian_analysis = if self.config.non_markovian_modeling {
            Some(self.perform_non_markovian_analysis(sequence)?)
        } else {
            None
        };

        let suppression_effectiveness = self.analyze_suppression_effectiveness(sequence)?;

        Ok(DDNoiseAnalysis {
            noise_characterization,
            spectral_analysis,
            temporal_analysis,
            spatial_analysis,
            non_markovian_analysis,
            suppression_effectiveness,
        })
    }

    /// Characterize noise
    fn characterize_noise(
        &self,
        sequence: &DDSequence,
        _performance_analysis: &DDPerformanceAnalysis,
    ) -> DeviceResult<NoiseCharacterization> {
        // Simplified noise characterization
        let mut noise_types = HashMap::new();

        for noise_type in &self.config.noise_types {
            let characteristics = NoiseCharacteristics {
                strength: 0.1, // Simplified
                frequency_profile: FrequencyProfile {
                    power_spectral_density: Array1::zeros(100),
                    frequency_bins: Array1::zeros(100),
                    dominant_frequencies: vec![1e6, 10e6],
                    spectral_shape: SpectralShape::Pink,
                },
                temporal_profile: TemporalProfile {
                    autocorrelation: Array1::zeros(100),
                    correlation_time: Duration::from_micros(1),
                    non_gaussianity: NonGaussianityMeasures {
                        skewness: 0.1,
                        kurtosis: 3.2,
                        higher_cumulants: Array1::zeros(5),
                        non_gaussianity_index: 0.05,
                    },
                    intermittency: IntermittencyCharacteristics {
                        burst_durations: Array1::zeros(50),
                        quiet_periods: Array1::zeros(50),
                        intermittency_factor: 0.3,
                        burst_intensity: Array1::zeros(50),
                    },
                },
                spatial_profile: None,
                statistical_properties: NoiseStatistics {
                    mean: 0.0,
                    variance: 0.01,
                    distribution_type: NoiseDistribution::Gaussian,
                    distribution_parameters: Array1::from_vec(vec![0.0, 0.1]),
                    entropy_measures: EntropyMeasures {
                        shannon_entropy: 2.5,
                        renyi_entropy: Array1::from_vec(vec![2.3, 2.5, 2.7]),
                        tsallis_entropy: 2.4,
                        differential_entropy: 1.8,
                    },
                },
            };
            noise_types.insert(noise_type.clone(), characteristics);
        }

        Ok(NoiseCharacterization {
            noise_types,
            dominant_sources: vec![NoiseSource {
                source_type: NoiseSourceType::ThermalFluctuations,
                strength: 0.05,
                location: None,
                frequency_range: (1e3, 1e9),
                mitigation_strategies: vec!["Temperature control".to_string()],
            }],
            environment_classification: NoiseEnvironmentClassification {
                environment_type: NoiseEnvironmentType::ComplexMarkovian,
                complexity_score: 0.6,
                predictability_score: 0.7,
                stationarity: StationarityAssessment {
                    is_stationary: true,
                    confidence: 0.85,
                    non_stationary_components: Vec::new(),
                    trend_analysis: TrendAnalysis {
                        linear_slope: 0.001,
                        trend_significance: 0.1,
                        cyclical_components: Vec::new(),
                        change_points: Vec::new(),
                    },
                },
            },
            time_dependent_parameters: TimeDependentNoiseParameters {
                parameter_evolution: HashMap::new(),
                time_grid: Array1::zeros(100),
                parameter_correlations: Array2::eye(3),
                prediction_model: None,
            },
        })
    }

    /// Perform spectral analysis (simplified)
    fn perform_spectral_analysis(&self, _sequence: &DDSequence) -> DeviceResult<SpectralAnalysis> {
        Ok(SpectralAnalysis {
            psd_analysis: PSDAnalysis {
                psd_estimate: Array1::zeros(100),
                frequency_bins: Array1::zeros(100),
                confidence_intervals: Array2::zeros((100, 2)),
                peaks: Vec::new(),
            },
            cross_spectral_analysis: CrossSpectralAnalysis {
                cross_psd: Array2::zeros((100, 100)),
                phase_relationships: Array2::zeros((100, 100)),
                frequency_coupling: Array2::zeros((100, 100)),
                coherence_function: Array1::zeros(100),
            },
            coherence_analysis: CoherenceAnalysis {
                coherence_matrix: Array2::eye(10),
                significant_coherences: Vec::new(),
                coherence_network: CoherenceNetwork {
                    adjacency_matrix: Array2::eye(10),
                    network_metrics: NetworkMetrics {
                        clustering_coefficient: 0.3,
                        average_path_length: 2.5,
                        density: 0.2,
                        small_world_index: 1.2,
                    },
                    communities: Vec::new(),
                },
            },
            spectral_features: SpectralFeatures {
                spectral_centroid: 1e6,
                spectral_bandwidth: 0.5e6,
                spectral_rolloff: 0.85,
                spectral_flatness: 0.3,
                spectral_entropy: 3.2,
            },
        })
    }

    /// Perform temporal analysis (simplified)
    fn perform_temporal_analysis(&self, _sequence: &DDSequence) -> DeviceResult<TemporalAnalysis> {
        Ok(TemporalAnalysis {
            autocorrelation_analysis: AutocorrelationAnalysis {
                autocorrelation_function: Array1::zeros(100),
                time_lags: Array1::zeros(100),
                decay_constants: Array1::from_vec(vec![1e-6, 10e-6]),
                oscillatory_components: Vec::new(),
            },
            memory_effects: MemoryEffects {
                memory_kernel: Array1::zeros(100),
                memory_time: Duration::from_micros(1),
                non_markovianity: 0.2,
                information_backflow: Array1::zeros(100),
            },
            temporal_scaling: TemporalScaling {
                hurst_exponent: 0.55,
                scaling_exponents: Array1::from_vec(vec![0.5, 0.6, 0.7]),
                multifractal_spectrum: MultifractalSpectrum {
                    singularity_strengths: Array1::zeros(20),
                    fractal_dimensions: Array1::zeros(20),
                    multifractality_parameter: 0.1,
                },
                long_range_correlations: LongRangeCorrelations {
                    correlation_exponent: 0.8,
                    correlation_length: 1e-5,
                    power_law_range: (1e-9, 1e-6),
                    crossover_scales: Array1::from_vec(vec![1e-8, 1e-7]),
                },
            },
            burst_statistics: BurstStatistics {
                burst_size_distribution: Array1::zeros(50),
                burst_duration_distribution: Array1::zeros(50),
                inter_burst_intervals: Array1::zeros(50),
                burstiness_parameter: 0.4,
            },
        })
    }

    /// Perform spatial analysis (simplified)
    fn perform_spatial_analysis(&self, _sequence: &DDSequence) -> DeviceResult<SpatialAnalysis> {
        let n_qubits = 10; // Simplified

        Ok(SpatialAnalysis {
            spatial_correlations: SpatialCorrelations {
                correlationmatrix: Array2::eye(n_qubits),
                correlation_functions: Array2::zeros((n_qubits, 100)),
                distance_matrix: Array2::zeros((n_qubits, n_qubits)),
                length_scales: Array1::from_vec(vec![1e-3, 5e-3, 1e-2]),
            },
            spatial_coherence: SpatialCoherence {
                coherence_matrix: Array2::eye(n_qubits),
                coherence_length: 2e-3,
                coherence_anisotropy: 0.1,
                principal_directions: Array2::eye(3),
            },
            spatial_patterns: SpatialPatterns {
                pattern_types: vec![SpatialPatternType::Random, SpatialPatternType::Clustered],
                pattern_strength: Array1::from_vec(vec![0.3, 0.5]),
                pattern_wavelengths: Array1::from_vec(vec![1e-3, 2e-3]),
                pattern_orientations: Array1::from_vec(vec![0.0, 1.57]),
            },
            propagation_analysis: PropagationAnalysis {
                propagation_speed: 1e8,
                propagation_direction: Array1::from_vec(vec![1.0, 0.0, 0.0]),
                dispersion_relation: DispersionRelation {
                    frequency_wavevector: Array2::zeros((100, 2)),
                    group_velocity: 0.8e8,
                    phase_velocity: 1e8,
                    dispersion_parameter: 0.1,
                },
                attenuation: AttenuationCharacteristics {
                    attenuation_coefficient: 0.01,
                    penetration_depth: 1e-2,
                    attenuation_anisotropy: 0.05,
                    frequency_dependence: Array1::zeros(100),
                },
            },
        })
    }

    /// Perform non-Markovian analysis (simplified)
    fn perform_non_markovian_analysis(
        &self,
        _sequence: &DDSequence,
    ) -> DeviceResult<NonMarkovianAnalysis> {
        Ok(NonMarkovianAnalysis {
            non_markovianity_measures: NonMarkovianityMeasures {
                blp_measure: 0.15,
                rhp_measure: 0.12,
                lli_measure: 0.18,
                trace_distance_measure: 0.14,
                volume_measure: 0.16,
            },
            memory_characterization: MemoryCharacterization {
                memory_kernel: Array1::zeros(100),
                memory_depth: Duration::from_micros(5),
                memory_strength: 0.3,
                memory_type: MemoryType::Exponential,
            },
            information_flow: InformationFlowAnalysis {
                information_backflow: Array1::zeros(100),
                transfer_entropy: 0.25,
                mutual_information: 0.8,
                causal_relationships: CausalRelationships {
                    causal_network: Array2::zeros((10, 10)),
                    causal_strength: Array2::zeros((10, 10)),
                    causal_delays: Array2::zeros((10, 10)),
                    feedback_loops: Vec::new(),
                },
            },
            non_markovian_models: vec![NonMarkovianModel {
                model_type: NonMarkovianModelType::GeneralizedLangevin,
                parameters: Array1::from_vec(vec![0.1, 0.5, 1.0]),
                accuracy: 0.85,
                predictive_power: 0.75,
            }],
        })
    }

    /// Analyze suppression effectiveness
    fn analyze_suppression_effectiveness(
        &self,
        sequence: &DDSequence,
    ) -> DeviceResult<SuppressionEffectiveness> {
        let suppression_by_type = sequence.properties.noise_suppression.clone();

        let overall_suppression =
            suppression_by_type.values().sum::<f64>() / suppression_by_type.len() as f64;

        Ok(SuppressionEffectiveness {
            suppression_by_type,
            overall_suppression,
            frequency_suppression: Array1::zeros(100),
            temporal_suppression: Array1::zeros(100),
            suppression_mechanisms: vec![
                SuppressionMechanism {
                    mechanism_type: SuppressionMechanismType::Decoupling,
                    effectiveness: 0.8,
                    target_noise_types: vec![NoiseType::PhaseDamping],
                    frequency_range: (1e3, 1e9),
                },
                SuppressionMechanism {
                    mechanism_type: SuppressionMechanismType::Refocusing,
                    effectiveness: 0.6,
                    target_noise_types: vec![NoiseType::AmplitudeDamping],
                    frequency_range: (1e6, 1e8),
                },
            ],
        })
    }
}