oxirs-chat 0.2.4

//! Quantum-Inspired RAG Enhancement Module
//!
//! Implements quantum superposition principles for enhanced retrieval
//! optimization and interference-based result ranking.
//!
//! This module provides advanced quantum-inspired algorithms for:
//! - Quantum superposition search across multiple retrieval paths
//! - Interference optimization for result ranking
//! - Entanglement-based relevance scoring
//! - Quantum error correction for robustness

use super::*;
use anyhow::Result;
use fastrand;
use std::f64::consts::PI;
use tracing::debug;

/// Quantum-inspired state for retrieval optimization
#[derive(Debug, Clone)]
pub struct QuantumRetrievalState {
    pub amplitude: f64,
    pub phase: f64,
    pub entanglement_factor: f64,
    pub coherence_time: Duration,
}

impl QuantumRetrievalState {
    pub fn new(query_complexity: f64) -> Self {
        let normalized_complexity = query_complexity.clamp(0.0, 1.0);
        Self {
            amplitude: (normalized_complexity * PI / 4.0).sin(),
            phase: normalized_complexity * PI / 2.0,
            entanglement_factor: 1.0 / (1.0 + (-normalized_complexity).exp()),
            coherence_time: Duration::from_secs((normalized_complexity * 30.0 + 5.0) as u64),
        }
    }

    /// Enhanced quantum superposition for multiple retrieval paths with error handling
    pub fn superposition_search(
        &self,
        candidates: &[RagDocument],
    ) -> Result<Vec<QuantumSearchResult>> {
        if candidates.is_empty() {
            debug!("No candidates provided for quantum superposition search");
            return Ok(Vec::new());
        }

        let results: Vec<QuantumSearchResult> = candidates
            .iter()
            .filter_map(|doc| {
                // Enhanced probability calculation with content analysis
                let content_factor = self.analyze_content_quantum_properties(&doc.content);
                let base_probability =
                    self.amplitude.powi(2) * (self.phase + content_factor).cos().abs();

                // Apply quantum error correction
                let corrected_probability = self.quantum_error_correction(base_probability);

                if corrected_probability > 0.01 {
                    // Quantum threshold
                    Some(QuantumSearchResult {
                        document: doc.clone(),
                        quantum_probability: corrected_probability,
                        entanglement_score: self.entanglement_factor * corrected_probability,
                        coherence_remaining: self.coherence_time,
                    })
                } else {
                    None
                }
            })
            .collect();

        debug!(
            "Quantum superposition search generated {} results",
            results.len()
        );
        Ok(results)
    }

    /// Advanced quantum interference for result optimization
    pub fn interference_optimization(&self, results: &mut [QuantumSearchResult]) -> Result<()> {
        if results.is_empty() {
            return Ok(());
        }

        // Apply quantum interference patterns
        let results_len = results.len();
        for (i, result) in results.iter_mut().enumerate() {
            // Multi-path interference calculation
            let path_interference = self.calculate_path_interference(i, results_len);
            let phase_interference = (self.phase - result.quantum_probability * PI).sin();

            // Combined interference effect
            let total_interference = (path_interference + phase_interference) / 2.0;
            result.quantum_probability *= (1.0 + total_interference * 0.15).max(0.05);

            // Update entanglement score based on interference
            result.entanglement_score = self.entanglement_factor * result.quantum_probability;
        }

        // Sort by quantum probability with stability
        results.sort_by(|a, b| {
            b.quantum_probability
                .partial_cmp(&a.quantum_probability)
                .unwrap_or(std::cmp::Ordering::Equal)
        });

        debug!(
            "Applied quantum interference optimization to {} results",
            results.len()
        );
        Ok(())
    }

    /// Analyze quantum properties of content
    fn analyze_content_quantum_properties(&self, content: &str) -> f64 {
        let length_factor = (content.len() as f64 * 0.001).min(1.0);
        let complexity_factor = self.calculate_content_complexity(content);
        let entropy_factor = self.calculate_shannon_entropy(content);

        (length_factor + complexity_factor + entropy_factor) / 3.0
    }

    /// Calculate content complexity using various metrics
    fn calculate_content_complexity(&self, content: &str) -> f64 {
        let word_count = content.split_whitespace().count() as f64;
        let unique_chars = content
            .chars()
            .collect::<std::collections::HashSet<_>>()
            .len() as f64;
        let avg_word_length = if word_count > 0.0 {
            content.len() as f64 / word_count
        } else {
            1.0
        };

        ((unique_chars / 26.0).min(1.0) + (avg_word_length / 10.0).min(1.0)) / 2.0
    }

    /// Calculate Shannon entropy for information content
    fn calculate_shannon_entropy(&self, content: &str) -> f64 {
        if content.is_empty() {
            return 0.0;
        }

        let mut char_counts = std::collections::HashMap::new();
        for c in content.chars() {
            *char_counts.entry(c).or_insert(0) += 1;
        }

        let length = content.len() as f64;
        let entropy = char_counts
            .values()
            .map(|&count| {
                let p = count as f64 / length;
                -p * p.log2()
            })
            .sum::<f64>();

        (entropy / 8.0).min(1.0) // Normalize to [0, 1]
    }

    /// Calculate interference between different quantum paths
    fn calculate_path_interference(&self, path_index: usize, total_paths: usize) -> f64 {
        if total_paths <= 1 {
            return 0.0;
        }

        let normalized_position = path_index as f64 / (total_paths - 1) as f64;
        let wave_function = (normalized_position * PI * 2.0).sin();
        let phase_shift = (self.phase + normalized_position * PI).cos();

        (wave_function * phase_shift) * 0.2 // Limit interference strength
    }

    /// Quantum error correction for probability values
    fn quantum_error_correction(&self, probability: f64) -> f64 {
        // Apply basic error correction using redundancy
        let error_threshold = 0.05;
        let corrected = if probability < error_threshold {
            probability * 0.5 // Reduce low-confidence results
        } else if probability > 0.95 {
            0.95 // Cap maximum probability
        } else {
            probability
        };

        // Apply coherence-based correction
        let coherence_factor = (self.coherence_time.as_secs_f64() / 60.0).min(1.0);
        corrected * coherence_factor
    }

    /// Get current quantum state metrics
    pub fn get_state_metrics(&self) -> QuantumStateMetrics {
        QuantumStateMetrics {
            amplitude: self.amplitude,
            phase: self.phase,
            entanglement_factor: self.entanglement_factor,
            coherence_seconds: self.coherence_time.as_secs_f64(),
            quantum_advantage: self.calculate_quantum_advantage(),
        }
    }

    /// Calculate quantum advantage over classical methods
    fn calculate_quantum_advantage(&self) -> f64 {
        let amplitude_benefit = self.amplitude.powi(2);
        let entanglement_benefit = self.entanglement_factor;
        let coherence_benefit = (self.coherence_time.as_secs_f64() / 30.0).min(1.0);

        (amplitude_benefit + entanglement_benefit + coherence_benefit) / 3.0
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QuantumSearchResult {
    pub document: RagDocument,
    pub quantum_probability: f64,
    pub entanglement_score: f64,
    pub coherence_remaining: Duration,
}

impl QuantumSearchResult {
    /// Calculate overall quantum relevance score
    pub fn relevance_score(&self) -> f64 {
        let probability_weight = 0.5;
        let entanglement_weight = 0.3;
        let coherence_weight = 0.2;

        let coherence_factor = (self.coherence_remaining.as_secs_f64() / 60.0).min(1.0);

        self.quantum_probability * probability_weight
            + self.entanglement_score * entanglement_weight
            + coherence_factor * coherence_weight
    }

    /// Check if quantum state is still coherent
    pub fn is_coherent(&self) -> bool {
        self.coherence_remaining > Duration::from_secs(1) && self.quantum_probability > 0.01
    }
}

/// Quantum state metrics for monitoring and optimization
#[derive(Debug, Clone)]
pub struct QuantumStateMetrics {
    pub amplitude: f64,
    pub phase: f64,
    pub entanglement_factor: f64,
    pub coherence_seconds: f64,
    pub quantum_advantage: f64,
}

impl QuantumStateMetrics {
    /// Get overall quantum system health score
    pub fn health_score(&self) -> f64 {
        let amplitude_health = self.amplitude.abs().min(1.0);
        let entanglement_health = self.entanglement_factor;
        let coherence_health = (self.coherence_seconds / 60.0).min(1.0);
        let advantage_health = self.quantum_advantage;

        (amplitude_health + entanglement_health + coherence_health + advantage_health) / 4.0
    }

    /// Check if quantum system is performing optimally
    pub fn is_optimal(&self) -> bool {
        self.health_score() > 0.7 && self.quantum_advantage > 0.5
    }
}

/// RAG document structure for quantum processing
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RagDocument {
    pub id: String,
    pub content: String,
    pub triple: Option<Triple>,
    pub metadata: HashMap<String, String>,
    pub embedding: Option<Vec<f32>>,
}

impl RagDocument {
    /// Create a new RAG document
    pub fn new(id: String, content: String) -> Self {
        Self {
            id,
            content,
            triple: None,
            metadata: HashMap::new(),
            embedding: None,
        }
    }

    /// Create from triple
    pub fn from_triple(triple: Triple) -> Self {
        let id = uuid::Uuid::new_v4().to_string();
        let content = format!(
            "{} {} {}",
            triple.subject(),
            triple.predicate(),
            triple.object()
        );

        Self {
            id,
            content,
            triple: Some(triple),
            metadata: HashMap::new(),
            embedding: None,
        }
    }

    /// Add metadata entry
    pub fn with_metadata(mut self, key: String, value: String) -> Self {
        self.metadata.insert(key, value);
        self
    }

    /// Add embedding vector
    pub fn with_embedding(mut self, embedding: Vec<f32>) -> Self {
        self.embedding = Some(embedding);
        self
    }

    /// Get content length for quantum analysis
    pub fn content_length(&self) -> usize {
        self.content.len()
    }

    /// Check if document has embedding
    pub fn has_embedding(&self) -> bool {
        self.embedding.is_some()
    }
}

/// Quantum entanglement manager for correlated document processing
#[derive(Debug)]
pub struct QuantumEntanglementManager {
    entangled_pairs: Vec<(String, String)>,
    correlation_strength: HashMap<String, f64>,
}

impl QuantumEntanglementManager {
    pub fn new() -> Self {
        Self {
            entangled_pairs: Vec::new(),
            correlation_strength: HashMap::new(),
        }
    }

    /// Create quantum entanglement between two documents
    pub fn entangle_documents(
        &mut self,
        doc1_id: &str,
        doc2_id: &str,
        strength: f64,
    ) -> Result<()> {
        if !(0.0..=1.0).contains(&strength) {
            return Err(anyhow!("Entanglement strength must be between 0.0 and 1.0"));
        }

        let pair = (doc1_id.to_string(), doc2_id.to_string());
        self.entangled_pairs.push(pair.clone());

        // Store bidirectional correlation
        self.correlation_strength
            .insert(format!("{doc1_id}:{doc2_id}"), strength);
        self.correlation_strength
            .insert(format!("{doc2_id}:{doc1_id}"), strength);

        debug!(
            "Entangled documents {} and {} with strength {}",
            doc1_id, doc2_id, strength
        );
        Ok(())
    }

    /// Get entanglement strength between two documents
    pub fn get_entanglement_strength(&self, doc1_id: &str, doc2_id: &str) -> f64 {
        self.correlation_strength
            .get(&format!("{doc1_id}:{doc2_id}"))
            .copied()
            .unwrap_or(0.0)
    }

    /// Apply quantum correlation to search results
    pub fn apply_quantum_correlations(&self, results: &mut [QuantumSearchResult]) -> Result<()> {
        // Collect document data first to avoid borrowing conflicts
        let document_data: Vec<_> = results
            .iter()
            .map(|r| (r.document.id.clone(), r.quantum_probability))
            .collect();

        for result in results.iter_mut() {
            let mut correlation_boost = 0.0;
            let mut correlation_count = 0;

            // Check for entangled documents in the result set
            for (other_id, other_probability) in &document_data {
                if result.document.id != *other_id {
                    let strength = self.get_entanglement_strength(&result.document.id, other_id);
                    if strength > 0.0 {
                        correlation_boost += strength * other_probability;
                        correlation_count += 1;
                    }
                }
            }

            // Apply correlation boost
            if correlation_count > 0 {
                let avg_correlation = correlation_boost / correlation_count as f64;
                result.quantum_probability *= 1.0 + (avg_correlation * 0.2); // 20% max boost
                result.entanglement_score += avg_correlation;
            }
        }

        debug!("Applied quantum correlations to {} results", results.len());
        Ok(())
    }
}

impl Default for QuantumEntanglementManager {
    fn default() -> Self {
        Self::new()
    }
}

/// Advanced Quantum Decoherence Simulation
/// Simulates realistic quantum decoherence effects for enhanced quantum RAG
#[derive(Debug, Clone)]
pub struct QuantumDecoherenceSimulator {
    environment_temperature: f64,
    decoherence_rate: f64,
    noise_level: f64,
    time_step: Duration,
}

impl QuantumDecoherenceSimulator {
    pub fn new(temperature: f64, noise_level: f64) -> Self {
        Self {
            environment_temperature: temperature,
            decoherence_rate: temperature * noise_level * 0.01,
            noise_level,
            time_step: Duration::from_millis(100),
        }
    }

    /// Simulate quantum decoherence over time
    pub fn simulate_decoherence(
        &self,
        state: &mut QuantumRetrievalState,
        elapsed: Duration,
    ) -> Result<()> {
        let time_factor = elapsed.as_secs_f64() / self.time_step.as_secs_f64();

        // Apply decoherence to amplitude
        let decoherence_factor = (-self.decoherence_rate * time_factor).exp();
        state.amplitude *= decoherence_factor;

        // Add environmental noise to phase
        // Using fastrand for simple random number generation
        let phase_noise = (fastrand::f64() * 2.0 - 1.0) * self.noise_level * time_factor;
        state.phase += phase_noise;

        // Reduce entanglement factor due to environmental interaction
        state.entanglement_factor *= decoherence_factor.sqrt();

        // Update coherence time
        let remaining_coherence = state.coherence_time.as_secs_f64() - elapsed.as_secs_f64();
        state.coherence_time = Duration::from_secs_f64(remaining_coherence.max(0.0));

        debug!(
            "Applied decoherence: amplitude={:.3}, phase={:.3}, entanglement={:.3}",
            state.amplitude, state.phase, state.entanglement_factor
        );

        Ok(())
    }

    /// Calculate quantum fidelity after decoherence
    pub fn calculate_quantum_fidelity(
        &self,
        original: &QuantumRetrievalState,
        current: &QuantumRetrievalState,
    ) -> f64 {
        let amplitude_fidelity = (original.amplitude * current.amplitude).abs();
        let phase_fidelity = ((original.phase - current.phase).cos() + 1.0) / 2.0;
        let entanglement_fidelity =
            (original.entanglement_factor * current.entanglement_factor).sqrt();

        (amplitude_fidelity * phase_fidelity * entanglement_fidelity).cbrt()
    }
}

/// Quantum Annealing Optimizer for RAG Configuration
/// Uses quantum annealing principles to find optimal retrieval configurations
#[derive(Debug, Clone)]
pub struct QuantumAnnealingOptimizer {
    initial_temperature: f64,
    final_temperature: f64,
    cooling_schedule: CoolingSchedule,
    annealing_steps: usize,
}

#[derive(Debug, Clone)]
pub enum CoolingSchedule {
    Linear,
    Exponential { factor: f64 },
    Logarithmic,
}

impl QuantumAnnealingOptimizer {
    pub fn new(initial_temp: f64, final_temp: f64, steps: usize) -> Self {
        Self {
            initial_temperature: initial_temp,
            final_temperature: final_temp,
            cooling_schedule: CoolingSchedule::Exponential { factor: 0.95 },
            annealing_steps: steps,
        }
    }

    /// Optimize quantum retrieval parameters using annealing
    pub fn optimize_retrieval_parameters(
        &self,
        query_complexity: f64,
        context_size: usize,
    ) -> Result<OptimizedQuantumParams> {
        let mut current_params = QuantumRetrievalParams::default();
        let mut best_params = current_params.clone();
        let mut best_energy =
            self.calculate_energy(&current_params, query_complexity, context_size);

        let mut temperature = self.initial_temperature;
        let temp_step =
            (self.initial_temperature - self.final_temperature) / self.annealing_steps as f64;

        for step in 0..self.annealing_steps {
            // Generate neighboring state
            let neighbor_params = self.generate_neighbor(&current_params)?;
            let neighbor_energy =
                self.calculate_energy(&neighbor_params, query_complexity, context_size);

            // Accept or reject based on Boltzmann probability
            let energy_diff = neighbor_energy - best_energy;
            let acceptance_probability = if energy_diff < 0.0 {
                1.0
            } else {
                (-energy_diff / temperature).exp()
            };

            // Using fastrand for simple random number generation
            if fastrand::f64() < acceptance_probability {
                current_params = neighbor_params;
                if neighbor_energy < best_energy {
                    best_params = current_params.clone();
                    best_energy = neighbor_energy;
                }
            }

            // Update temperature according to cooling schedule
            temperature = self.update_temperature(temperature, step, temp_step);
        }

        debug!(
            "Quantum annealing optimization completed: energy={:.3}",
            best_energy
        );

        Ok(OptimizedQuantumParams {
            params: best_params,
            final_energy: best_energy,
            convergence_steps: self.annealing_steps,
        })
    }

    /// Calculate energy function for quantum parameters
    fn calculate_energy(
        &self,
        params: &QuantumRetrievalParams,
        query_complexity: f64,
        context_size: usize,
    ) -> f64 {
        // Multi-objective energy function combining efficiency and accuracy
        let efficiency_term = params.amplitude_factor * params.phase_factor;
        let accuracy_term = params.entanglement_strength * query_complexity;
        let context_term = (context_size as f64).ln() * params.coherence_factor;

        // Minimize negative efficiency while maximizing accuracy
        -(efficiency_term * accuracy_term * context_term).ln()
    }

    /// Generate neighboring parameter configuration
    fn generate_neighbor(
        &self,
        current: &QuantumRetrievalParams,
    ) -> Result<QuantumRetrievalParams> {
        // Using fastrand for simple random number generation
        let perturbation = 0.1;

        Ok(QuantumRetrievalParams {
            amplitude_factor: (current.amplitude_factor
                + (fastrand::f64() * 2.0 - 1.0) * perturbation)
                .clamp(0.1, 2.0),
            phase_factor: (current.phase_factor + (fastrand::f64() * 2.0 - 1.0) * perturbation)
                .clamp(0.1, 2.0),
            entanglement_strength: (current.entanglement_strength
                + (fastrand::f64() * 2.0 - 1.0) * perturbation)
                .clamp(0.0, 1.0),
            coherence_factor: (current.coherence_factor
                + (fastrand::f64() * 2.0 - 1.0) * perturbation)
                .clamp(0.1, 2.0),
        })
    }

    /// Update temperature according to cooling schedule
    fn update_temperature(&self, current_temp: f64, step: usize, temp_step: f64) -> f64 {
        match self.cooling_schedule {
            CoolingSchedule::Linear => current_temp - temp_step,
            CoolingSchedule::Exponential { factor } => current_temp * factor,
            CoolingSchedule::Logarithmic => self.initial_temperature / (1.0 + (step as f64).ln()),
        }
    }
}

/// Multi-Dimensional Quantum State Manager
/// Manages quantum states across multiple dimensions for complex retrieval
#[derive(Debug, Clone)]
pub struct MultiDimensionalQuantumState {
    dimensions: Vec<QuantumDimension>,
    correlation_matrix: Vec<Vec<f64>>,
    total_entropy: f64,
}

#[derive(Debug, Clone)]
pub struct QuantumDimension {
    name: String,
    state_vector: Vec<f64>,
    measurement_basis: MeasurementBasis,
    uncertainty: f64,
}

#[derive(Debug, Clone)]
pub enum MeasurementBasis {
    Computational,
    Diagonal,
    Circular,
    Custom(Vec<f64>),
}

impl MultiDimensionalQuantumState {
    pub fn new(dimension_names: Vec<String>) -> Self {
        let num_dims = dimension_names.len();
        let dimensions = dimension_names
            .into_iter()
            .map(|name| QuantumDimension {
                name,
                state_vector: vec![1.0 / (num_dims as f64).sqrt(); num_dims],
                measurement_basis: MeasurementBasis::Computational,
                uncertainty: 0.1,
            })
            .collect();

        // Initialize correlation matrix
        let correlation_matrix = vec![vec![0.0; num_dims]; num_dims];

        Self {
            dimensions,
            correlation_matrix,
            total_entropy: 0.0,
        }
    }

    /// Apply quantum Fourier transform for pattern analysis
    pub fn quantum_fourier_transform(&mut self, dimension_idx: usize) -> Result<()> {
        if dimension_idx >= self.dimensions.len() {
            return Err(anyhow::anyhow!("Dimension index out of bounds"));
        }

        let dimension = &mut self.dimensions[dimension_idx];
        let n = dimension.state_vector.len();
        let mut transformed = vec![0.0; n];

        for (k, transformed_k) in transformed.iter_mut().enumerate().take(n) {
            let mut sum = 0.0;
            for j in 0..n {
                let angle = -2.0 * PI * (k * j) as f64 / n as f64;
                sum += dimension.state_vector[j] * angle.cos();
            }
            *transformed_k = sum / (n as f64).sqrt();
        }

        dimension.state_vector = transformed;
        debug!("Applied QFT to dimension: {}", dimension.name);

        Ok(())
    }

    /// Calculate quantum coherence across all dimensions
    pub fn calculate_total_coherence(&self) -> f64 {
        self.dimensions
            .iter()
            .map(|dim| {
                let norm_squared: f64 = dim.state_vector.iter().map(|x| x * x).sum();
                let max_element = dim
                    .state_vector
                    .iter()
                    .fold(0.0f64, |acc, &x| acc.max(x.abs()));
                (norm_squared - max_element * max_element)
                    / (1.0 - 1.0 / dim.state_vector.len() as f64)
            })
            .sum::<f64>()
            / self.dimensions.len() as f64
    }

    /// Measure quantum state in specified basis
    pub fn measure_state(&self, dimension_idx: usize, basis: &MeasurementBasis) -> Result<f64> {
        if dimension_idx >= self.dimensions.len() {
            return Err(anyhow::anyhow!("Dimension index out of bounds"));
        }

        let dimension = &self.dimensions[dimension_idx];
        let measurement_result = match basis {
            MeasurementBasis::Computational => dimension
                .state_vector
                .iter()
                .enumerate()
                .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
                .map(|(i, _)| i as f64)
                .unwrap_or(0.0),
            MeasurementBasis::Diagonal => {
                // Transform to diagonal basis and measure
                let diagonal_projection: f64 = dimension
                    .state_vector
                    .iter()
                    .enumerate()
                    .map(|(i, &val)| val * (i as f64 + 1.0).ln())
                    .sum();
                diagonal_projection / dimension.state_vector.len() as f64
            }
            MeasurementBasis::Circular => {
                // Circular basis measurement
                let phase: f64 = dimension
                    .state_vector
                    .iter()
                    .enumerate()
                    .map(|(i, &val)| {
                        val * (2.0 * PI * i as f64 / dimension.state_vector.len() as f64).cos()
                    })
                    .sum();
                phase.atan2(dimension.state_vector.iter().sum::<f64>())
            }
            MeasurementBasis::Custom(basis_vector) => {
                // Custom basis measurement
                dimension
                    .state_vector
                    .iter()
                    .zip(basis_vector.iter())
                    .map(|(state, basis)| state * basis)
                    .sum()
            }
        };

        Ok(measurement_result)
    }
}

/// Supporting data structures for quantum enhancements
#[derive(Debug, Clone)]
pub struct QuantumRetrievalParams {
    pub amplitude_factor: f64,
    pub phase_factor: f64,
    pub entanglement_strength: f64,
    pub coherence_factor: f64,
}

impl Default for QuantumRetrievalParams {
    fn default() -> Self {
        Self {
            amplitude_factor: 1.0,
            phase_factor: 1.0,
            entanglement_strength: 0.5,
            coherence_factor: 1.0,
        }
    }
}

#[derive(Debug, Clone)]
pub struct OptimizedQuantumParams {
    pub params: QuantumRetrievalParams,
    pub final_energy: f64,
    pub convergence_steps: usize,
}

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

    #[test]
    fn test_quantum_retrieval_state_creation() {
        let state = QuantumRetrievalState::new(0.5);
        assert!(state.amplitude > 0.0);
        assert!(state.entanglement_factor > 0.0);
        assert!(state.coherence_time.as_secs() > 0);
    }

    #[test]
    fn test_quantum_state_metrics() {
        let state = QuantumRetrievalState::new(0.8);
        let metrics = state.get_state_metrics();
        assert!(metrics.health_score() > 0.0);
        assert!(metrics.quantum_advantage > 0.0);
    }

    #[test]
    fn test_rag_document_creation() {
        let doc = RagDocument::new("test-id".to_string(), "test content".to_string());
        assert_eq!(doc.id, "test-id");
        assert_eq!(doc.content, "test content");
        assert!(!doc.has_embedding());
    }

    #[test]
    fn test_quantum_entanglement_manager() {
        let mut manager = QuantumEntanglementManager::new();
        assert!(manager.entangle_documents("doc1", "doc2", 0.8).is_ok());
        assert_eq!(manager.get_entanglement_strength("doc1", "doc2"), 0.8);
        assert_eq!(manager.get_entanglement_strength("doc2", "doc1"), 0.8);
    }

    #[test]
    fn test_quantum_search_result_relevance() {
        let doc = RagDocument::new("test".to_string(), "content".to_string());
        let result = QuantumSearchResult {
            document: doc,
            quantum_probability: 0.8,
            entanglement_score: 0.6,
            coherence_remaining: Duration::from_secs(30),
        };

        assert!(result.relevance_score() > 0.0);
        assert!(result.is_coherent());
    }
}