quantrs2-device 0.1.3

//! Implementation blocks for enhanced noise characterization types.

use quantrs2_core::error::{QuantRS2Error, QuantRS2Result};
use quantrs2_core::qubit::QubitId;
use scirs2_core::ndarray::{Array1, Array2};
use scirs2_core::random::{Distribution, Normal};
use scirs2_core::Complex64;
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, VecDeque};

use super::types::*;

impl RBSequence {
    pub(super) const fn new() -> Self {
        Self { gates: Vec::new() }
    }

    pub(super) fn add_gate(&mut self, gate: CliffordGate) {
        self.gates.push(gate);
    }

    pub(super) fn length(&self) -> usize {
        self.gates.len() - 1 // Exclude recovery gate
    }

    pub(super) fn to_circuit(_qubits: &[QubitId]) -> QuantRS2Result<DynamicCircuit> {
        // Convert to quantum circuit - stub implementation
        Ok(DynamicCircuit::new(1))
    }
}

impl NoiseHistory {
    pub(super) const fn new() -> Self {
        Self {
            measurements: VecDeque::new(),
            max_size: 1000,
            current_trend: None,
        }
    }

    pub(super) fn add_measurement(&mut self, timestamp: f64, data: NoiseData) {
        if self.measurements.len() >= self.max_size {
            self.measurements.pop_front();
        }
        self.measurements.push_back((timestamp, data));
    }

    pub(super) fn len(&self) -> usize {
        self.measurements.len()
    }

    pub(super) fn to_time_series(&self) -> TimeSeries {
        let timestamps: Vec<f64> = self.measurements.iter().map(|(t, _)| *t).collect();
        let values: Vec<f64> = self
            .measurements
            .iter()
            .map(|(_, d)| d.noise_rates.values().sum::<f64>() / d.noise_rates.len() as f64)
            .collect();

        TimeSeries { timestamps, values }
    }

    pub(super) const fn update_trend(&mut self, trend: NoiseTrend) {
        self.current_trend = Some(trend);
    }
}

impl NoiseMLModel {
    pub(super) const fn new() -> Self {
        Self {}
    }

    pub(super) const fn predict(_features: &NoiseFeatures) -> QuantRS2Result<NoisePrediction> {
        // Placeholder implementation
        Ok(NoisePrediction {
            classification: NoiseClassification {
                primary_type: NoiseModel::Depolarizing,
                secondary_types: vec![],
                confidence: 0.9,
            },
            anomaly_score: 0.1,
            evolution: vec![],
            confidence: 0.85,
        })
    }
}

impl NoiseFeatureExtractor {
    pub(super) const fn new() -> Self {
        Self {}
    }

    pub(super) const fn extract_features(
        _result: &NoiseCharacterizationResult,
    ) -> QuantRS2Result<NoiseFeatures> {
        // Extract relevant features for ML analysis
        Ok(NoiseFeatures {
            statistical_features: vec![],
            spectral_features: vec![],
            temporal_features: vec![],
            correlation_features: vec![],
        })
    }
}

impl NoisePredictor {
    pub(super) const fn new() -> Self {
        Self {}
    }

    pub(super) const fn update(_result: &NoiseCharacterizationResult) -> QuantRS2Result<()> {
        // Update prediction model with new data
        Ok(())
    }

    pub(super) const fn predict(horizon: f64) -> QuantRS2Result<NoisePredictions> {
        // Generate predictions
        Ok(NoisePredictions {
            horizon,
            evolution: vec![],
            trend: NoiseTrend::Stable,
            alerts: vec![],
        })
    }
}

impl NoiseCache {
    pub(super) fn new() -> Self {
        Self {
            characterization_results: HashMap::new(),
            analysis_results: HashMap::new(),
        }
    }
}

impl QuantumJob {
    pub(super) fn get_counts(&self) -> QuantRS2Result<HashMap<Vec<bool>, usize>> {
        // Get measurement counts from completed job results
        match &self.status {
            JobStatus::Completed => Ok(self
                .results
                .as_ref()
                .map(|r| r.counts.clone())
                .unwrap_or_default()),
            JobStatus::Failed(msg) => Err(QuantRS2Error::InvalidOperation(format!(
                "Job {} failed: {}",
                self.job_id, msg
            ))),
            _ => Ok(HashMap::new()),
        }
    }
}

impl EnhancedNoiseCharacterizer {
    /// Analyze temporal characteristics
    pub(super) fn analyze_temporal_characteristics(
        _result: &NoiseCharacterizationResult,
    ) -> QuantRS2Result<TemporalAnalysis> {
        // Stub implementation
        Ok(TemporalAnalysis {
            time_series: TimeSeries {
                timestamps: vec![],
                values: vec![],
            },
            trend: TrendAnalysis::default(),
            periodicity: None,
            drift: DriftCharacterization {
                drift_rate: 0.0,
                drift_type: DriftType::Linear,
                time_constant: 1000.0,
            },
        })
    }

    /// Analyze spectral characteristics
    pub(super) fn analyze_spectral_characteristics(
        _result: &NoiseCharacterizationResult,
    ) -> QuantRS2Result<SpectralAnalysis> {
        // Stub implementation
        Ok(SpectralAnalysis {
            dominant_frequencies: vec![],
            spectral_features: SpectralFeatures::default(),
            noise_color: NoiseColor::White,
        })
    }

    /// Analyze correlations
    pub(super) const fn analyze_correlations(
        _result: &NoiseCharacterizationResult,
    ) -> QuantRS2Result<CorrelationAnalysis> {
        // Stub implementation
        Ok(CorrelationAnalysis {
            correlation_summary: CorrelationSummary {
                max_correlation: 0.0,
                mean_correlation: 0.0,
                correlation_radius: 0.0,
            },
            significant_correlations: vec![],
            correlation_network: CorrelationNetwork {
                nodes: vec![],
                edges: vec![],
            },
        })
    }

    /// Generate recommendations
    pub(super) const fn generate_recommendations(
        _result: &NoiseCharacterizationResult,
    ) -> QuantRS2Result<Vec<Recommendation>> {
        // Stub implementation
        Ok(vec![])
    }

    /// Generate visualizations
    pub(super) fn generate_visualizations(
        _result: &NoiseCharacterizationResult,
    ) -> QuantRS2Result<NoiseVisualizations> {
        // Stub implementation
        Ok(NoiseVisualizations {
            rb_decay_plot: PlotData {
                x_data: vec![],
                y_data: vec![],
                error_bars: None,
                metadata: PlotMetadata {
                    title: "RB Decay".to_string(),
                    x_label: "Sequence Length".to_string(),
                    y_label: "Survival Probability".to_string(),
                    plot_type: PlotType::Line,
                },
            },
            spectrum_plot: PlotData {
                x_data: vec![],
                y_data: vec![],
                error_bars: None,
                metadata: PlotMetadata {
                    title: "Noise Spectrum".to_string(),
                    x_label: "Frequency".to_string(),
                    y_label: "Power Density".to_string(),
                    plot_type: PlotType::Line,
                },
            },
            correlation_heatmap: HeatmapData {
                data: Array2::zeros((0, 0)),
                row_labels: vec![],
                col_labels: vec![],
                colormap: "viridis".to_string(),
            },
            temporal_plot: PlotData {
                x_data: vec![],
                y_data: vec![],
                error_bars: None,
                metadata: PlotMetadata {
                    title: "Temporal Evolution".to_string(),
                    x_label: "Time".to_string(),
                    y_label: "Noise Level".to_string(),
                    plot_type: PlotType::Line,
                },
            },
            noise_landscape: Landscape3D {
                x: vec![],
                y: vec![],
                z: Array2::zeros((0, 0)),
                viz_params: Visualization3DParams {
                    view_angle: (30.0, 45.0),
                    color_scheme: "plasma".to_string(),
                    surface_type: SurfaceType::Surface,
                },
            },
        })
    }

    /// Generate random Clifford gate
    pub(super) fn random_clifford_gate(_num_qubits: usize) -> CliffordGate {
        // Stub implementation - return identity gate
        CliffordGate {
            gate_type: CliffordType::Identity,
            target_qubits: vec![0],
        }
    }

    /// Compute recovery gate
    pub(super) fn compute_recovery_gate(_sequence: &RBSequence) -> CliffordGate {
        // Stub implementation - return identity gate
        CliffordGate {
            gate_type: CliffordType::Identity,
            target_qubits: vec![0],
        }
    }

    /// Calculate error bars
    pub(super) const fn calculate_error_bars(_survival_prob: f64, _shots: usize) -> f64 {
        // Stub implementation using standard error
        0.01 // placeholder
    }

    /// Calculate fit confidence interval
    pub(super) const fn calculate_fit_confidence_interval(
        _x: &[f64],
        _y: &[f64],
        _a: f64,
        _p: f64,
        _b: f64,
    ) -> QuantRS2Result<(f64, f64)> {
        // Stub implementation
        Ok((0.0, 1.0)) // placeholder
    }

    /// Calculate survival probability from RB results
    pub(super) fn calculate_survival_probability(
        results: &[QuantRS2Result<RBResult>],
    ) -> QuantRS2Result<f64> {
        let mut total = 0.0;
        let mut count = 0;

        for rb_result in results.iter().flatten() {
            total += rb_result.survival_probability;
            count += 1;
        }

        if count == 0 {
            return Err(QuantRS2Error::RuntimeError(
                "No valid RB results".to_string(),
            ));
        }

        Ok(total / count as f64)
    }

    /// Generate preparation states for process tomography
    pub(super) fn generate_preparation_states(num_qubits: usize) -> Vec<QuantumState> {
        let mut states = Vec::new();

        // Generate standard basis states and superpositions
        for i in 0..2_usize.pow(num_qubits as u32) {
            let mut state_vector = Array1::zeros(2_usize.pow(num_qubits as u32));
            state_vector[i] = Complex64::new(1.0, 0.0);

            states.push(QuantumState {
                state_vector,
                preparation_circuit: DynamicCircuit::new(num_qubits),
            });
        }

        states
    }

    /// Generate measurement bases for process tomography
    pub(super) fn generate_measurement_bases(num_qubits: usize) -> Vec<MeasurementBasis> {
        let mut bases = Vec::new();

        // Generate Pauli measurement bases
        let basis_names = ["X", "Y", "Z"];
        for name in &basis_names {
            bases.push(MeasurementBasis {
                basis_name: name.to_string(),
                measurement_circuit: DynamicCircuit::new(num_qubits),
            });
        }

        bases
    }

    /// Execute tomography experiment
    pub(super) fn execute_tomography_experiment(
        &self,
        device: &impl QuantumDevice,
        qubits: &[QubitId],
        prep: &QuantumState,
        meas: &MeasurementBasis,
    ) -> QuantRS2Result<Vec<f64>> {
        // Stub implementation - combine prep and measurement circuits
        let mut circuit = prep.preparation_circuit.clone();
        let job = device.execute(circuit, self.config.base_config.shots_per_sequence)?;
        let counts = job.get_counts()?;

        // Convert counts to probabilities
        let total_shots = counts.values().sum::<usize>() as f64;
        let probs: Vec<f64> = counts
            .values()
            .map(|&count| count as f64 / total_shots)
            .collect();

        Ok(probs)
    }

    /// Reconstruct process matrix from tomography results
    pub(super) fn reconstruct_process_matrix(
        _results: &[QuantRS2Result<Vec<f64>>],
    ) -> QuantRS2Result<Array2<Complex64>> {
        // Stub implementation - simple identity process
        let dim = 4; // For single-qubit process (2^2)
        let mut process_matrix = Array2::zeros((dim, dim));

        for i in 0..dim {
            process_matrix[[i, i]] = Complex64::new(1.0, 0.0);
        }

        Ok(process_matrix)
    }

    /// Extract noise parameters from process matrix
    pub(super) const fn extract_noise_parameters(
        _process_matrix: &Array2<Complex64>,
    ) -> QuantRS2Result<NoiseParameters> {
        // Stub implementation - extract basic parameters
        Ok(NoiseParameters {
            depolarizing_rate: 0.01,
            dephasing_rate: 0.005,
            amplitude_damping_rate: 0.002,
            coherent_error_angle: 0.001,
            leakage_rate: Some(0.0001),
        })
    }

    /// Collect noise time series data
    pub(super) fn collect_noise_time_series(
        _device: &impl QuantumDevice,
        _qubits: &[QubitId],
    ) -> QuantRS2Result<TimeSeries> {
        // Stub implementation - collect time series of noise measurements
        use scirs2_core::random::thread_rng;
        use scirs2_core::random::Distribution;

        let mut rng = thread_rng();
        let normal = Normal::new(0.01, 0.001).map_err(|e| {
            QuantRS2Error::RuntimeError(format!("Failed to create normal distribution: {e}"))
        })?;

        let num_points = 100;
        let timestamps: Vec<f64> = (0..num_points).map(|i| i as f64).collect();
        let values: Vec<f64> = (0..num_points)
            .map(|_| {
                let v: f64 = normal.sample(&mut rng);
                v.abs()
            })
            .collect();

        Ok(TimeSeries { timestamps, values })
    }

    /// Identify noise peaks in spectrum
    pub(super) fn identify_noise_peaks(spectrum: &PowerSpectrum) -> QuantRS2Result<Vec<NoisePeak>> {
        // Stub implementation - identify peaks in power spectrum
        let mut peaks = Vec::new();

        // Simple peak detection: find local maxima
        for i in 1..spectrum.power_density.len() - 1 {
            if spectrum.power_density[i] > spectrum.power_density[i - 1]
                && spectrum.power_density[i] > spectrum.power_density[i + 1]
                && spectrum.power_density[i] > 0.01
            {
                peaks.push(NoisePeak {
                    frequency: spectrum.frequencies[i],
                    amplitude: spectrum.power_density[i],
                    width: spectrum.resolution,
                    source: None,
                });
            }
        }

        Ok(peaks)
    }

    /// Analyze 1/f noise characteristics
    pub(super) const fn analyze_one_over_f_noise(
        _spectrum: &PowerSpectrum,
    ) -> QuantRS2Result<OneOverFParameters> {
        // Stub implementation - fit 1/f noise model
        Ok(OneOverFParameters {
            amplitude: 0.1,
            exponent: 1.0,
            cutoff_frequency: 1000.0,
        })
    }

    /// Measure correlated errors between qubits
    pub(super) fn measure_correlated_errors(
        _device: &impl QuantumDevice,
        qubits: &[QubitId],
    ) -> QuantRS2Result<Array2<f64>> {
        // Stub implementation - measure simultaneous errors
        let n = qubits.len();
        let mut error_data = Array2::zeros((n, 100)); // n qubits, 100 measurements

        use scirs2_core::random::thread_rng;
        use scirs2_core::random::Distribution;

        let mut rng = thread_rng();
        let normal = Normal::new(0.0, 0.01).map_err(|e| {
            QuantRS2Error::RuntimeError(format!("Failed to create normal distribution: {e}"))
        })?;

        for i in 0..n {
            for j in 0..100 {
                let v: f64 = normal.sample(&mut rng);
                error_data[[i, j]] = v.abs();
            }
        }

        Ok(error_data)
    }

    /// Identify error clusters from correlation matrix
    pub(super) fn identify_error_clusters(
        &self,
        correlation_matrix: &Array2<f64>,
    ) -> QuantRS2Result<Vec<ErrorCluster>> {
        // Stub implementation - identify clusters of correlated errors
        let mut clusters = Vec::new();

        let threshold = self.config.analysis_parameters.correlation_threshold;

        // Find highly correlated qubit pairs
        for i in 0..correlation_matrix.nrows() {
            for j in i + 1..correlation_matrix.ncols() {
                if correlation_matrix[[i, j]] > threshold {
                    clusters.push(ErrorCluster {
                        qubits: vec![QubitId(i as u32), QubitId(j as u32)],
                        correlation_strength: correlation_matrix[[i, j]],
                        cluster_type: ClusterType::NearestNeighbor,
                    });
                }
            }
        }

        Ok(clusters)
    }

    /// Analyze spatial correlations
    pub(super) const fn analyze_spatial_correlations(
        _device: &impl QuantumDevice,
        _qubits: &[QubitId],
    ) -> QuantRS2Result<SpatialCorrelations> {
        // Stub implementation - analyze spatial correlation patterns
        Ok(SpatialCorrelations {
            distance_correlations: vec![],
            decay_length: 1.0,
            correlation_type: SpatialCorrelationType::Exponential,
        })
    }

    /// Generate summary statistics
    pub(super) fn generate_summary_statistics(
        result: &NoiseCharacterizationResult,
    ) -> QuantRS2Result<NoiseSummary> {
        // Stub implementation - generate summary from results
        let overall_noise_rate = if let Some(ref rb_results) = result.rb_results {
            rb_results.fit_params.average_error_rate
        } else {
            0.01
        };

        Ok(NoiseSummary {
            overall_noise_rate,
            dominant_noise: NoiseModel::Depolarizing,
            quality_factor: 1.0 / overall_noise_rate,
            baseline_comparison: None,
        })
    }

    /// Analyze specific noise model
    pub(super) fn analyze_noise_model(
        _result: &NoiseCharacterizationResult,
        _noise_model: NoiseModel,
    ) -> QuantRS2Result<ModelAnalysis> {
        // Stub implementation - analyze specific noise model
        let mut parameters = HashMap::new();
        parameters.insert("rate".to_string(), 0.01);

        let mut confidence_intervals = HashMap::new();
        confidence_intervals.insert("rate".to_string(), (0.009, 0.011));

        Ok(ModelAnalysis {
            parameters,
            goodness_of_fit: 0.95,
            confidence_intervals,
            insights: vec!["Noise model fits well".to_string()],
        })
    }

    /// Analyze temporal evolution
    pub(super) fn analyze_temporal_evolution(
        _result: &NoiseCharacterizationResult,
    ) -> QuantRS2Result<TemporalAnalysis> {
        // Stub implementation - analyze how noise evolves over time
        Ok(TemporalAnalysis {
            time_series: TimeSeries {
                timestamps: vec![],
                values: vec![],
            },
            trend: TrendAnalysis::default(),
            periodicity: None,
            drift: DriftCharacterization {
                drift_rate: 0.0,
                drift_type: DriftType::Linear,
                time_constant: 1000.0,
            },
        })
    }
}

// Default implementations
impl Default for TrendAnalysis {
    fn default() -> Self {
        Self {
            trend_type: TrendType::Linear,
            slope: 0.0,
            confidence: 0.0,
        }
    }
}

impl Default for SpectralFeatures {
    fn default() -> Self {
        Self {
            peak_frequency: 0.0,
            bandwidth: 0.0,
            spectral_entropy: 0.0,
        }
    }
}

impl Default for CorrelationAnalysis {
    fn default() -> Self {
        Self {
            correlation_summary: CorrelationSummary {
                max_correlation: 0.0,
                mean_correlation: 0.0,
                correlation_radius: 0.0,
            },
            significant_correlations: vec![],
            correlation_network: CorrelationNetwork {
                nodes: vec![],
                edges: vec![],
            },
        }
    }
}

impl CorrelationAnalysis {
    /// Compute correlation matrix from error data
    pub fn compute_correlationmatrix(error_data: &Array2<f64>) -> QuantRS2Result<Array2<f64>> {
        let n = error_data.nrows();
        let mut corr_matrix = Array2::zeros((n, n));

        // Compute pairwise correlations
        for i in 0..n {
            for j in 0..n {
                if i == j {
                    corr_matrix[[i, j]] = 1.0;
                } else {
                    // Simple correlation computation
                    let row_i = error_data.row(i);
                    let row_j = error_data.row(j);

                    let mean_i: f64 = row_i.iter().sum::<f64>() / row_i.len() as f64;
                    let mean_j: f64 = row_j.iter().sum::<f64>() / row_j.len() as f64;

                    let mut cov = 0.0;
                    let mut var_i = 0.0;
                    let mut var_j = 0.0;

                    for k in 0..row_i.len() {
                        let di = row_i[k] - mean_i;
                        let dj = row_j[k] - mean_j;
                        cov += di * dj;
                        var_i += di * di;
                        var_j += dj * dj;
                    }

                    let corr = if var_i > 0.0 && var_j > 0.0 {
                        cov / (var_i.sqrt() * var_j.sqrt())
                    } else {
                        0.0
                    };

                    corr_matrix[[i, j]] = corr;
                }
            }
        }

        Ok(corr_matrix)
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    pub(super) fn test_noise_characterizer_creation() {
        let config = EnhancedNoiseConfig::default();
        let characterizer = EnhancedNoiseCharacterizer::new(config);

        // Basic test to ensure creation works
        assert!(characterizer.config.enable_ml_analysis);
    }

    #[test]
    pub(super) fn test_rb_sequence_generation() {
        let config = EnhancedNoiseConfig::default();
        let characterizer = EnhancedNoiseCharacterizer::new(config);

        let sequences = characterizer.generate_rb_sequences(2, 10);
        assert_eq!(
            sequences.len(),
            characterizer.config.base_config.num_sequences
        );

        for seq in sequences {
            assert_eq!(seq.gates.len(), 11); // 10 + recovery gate
        }
    }
}