scirs2-vision 0.4.4

//! Quantum-inspired streaming processing for next-generation computer vision
//!
//! This module implements quantum-inspired algorithms for advanced-high performance
//! streaming processing, leveraging quantum computing principles like superposition,
//! entanglement, and interference for optimized computer vision pipelines.
//!
//! # Features
//!
//! - Quantum-inspired optimization algorithms for pipeline scheduling
//! - Superposition-based parallel processing architectures

#![allow(dead_code)]
//! - Quantum interference algorithms for noise reduction
//! - Entanglement-inspired feature correlation analysis
//! - Quantum annealing for adaptive parameter optimization

use crate::error::Result;
#[cfg(test)]
use crate::streaming::FrameMetadata;
use crate::streaming::{Frame, ProcessingStage};
use scirs2_core::ndarray::ArrayStatCompat;
use scirs2_core::ndarray::{Array1, Array2};
use scirs2_core::random::prelude::*;
use scirs2_core::random::rand_prelude::IndexedMutRandom;
use scirs2_core::random::{Rng, RngExt};
use statrs::statistics::Statistics;
use std::collections::HashMap;
use std::sync::{Arc, Mutex};
use std::time::Instant;

/// Quantum-inspired amplitude for representing processing states
#[derive(Debug, Clone)]
pub struct QuantumAmplitude {
    /// Real component of the amplitude
    pub real: f64,
    /// Imaginary component of the amplitude
    pub imaginary: f64,
}

impl QuantumAmplitude {
    /// Create a new quantum amplitude
    pub fn new(real: f64, imaginary: f64) -> Self {
        Self { real, imaginary }
    }

    /// Calculate the probability (amplitude squared)
    pub fn probability(&self) -> f64 {
        self.real * self.real + self.imaginary * self.imaginary
    }

    /// Normalize the amplitude
    pub fn normalize(&mut self) {
        let magnitude = (self.real * self.real + self.imaginary * self.imaginary).sqrt();
        if magnitude > 0.0 {
            self.real /= magnitude;
            self.imaginary /= magnitude;
        }
    }
}

/// Quantum-inspired state for processing optimization
#[derive(Debug, Clone)]
pub struct QuantumProcessingState {
    /// Processing stage amplitudes
    pub stage_amplitudes: HashMap<String, QuantumAmplitude>,
    /// Global phase for quantum interference
    pub global_phase: f64,
    /// Entanglement matrix for stage correlations
    pub entanglement_matrix: Array2<f64>,
    /// Quantum energy (cost function)
    pub energy: f64,
}

impl QuantumProcessingState {
    /// Create a new quantum processing state
    pub fn new(_stagenames: &[String]) -> Self {
        let mut stage_amplitudes = HashMap::new();

        // Initialize amplitudes in superposition
        for stagename in _stagenames {
            let amplitude = QuantumAmplitude::new(1.0 / (_stagenames.len() as f64).sqrt(), 0.0);
            stage_amplitudes.insert(stagename.clone(), amplitude);
        }

        let n_stages = _stagenames.len();
        let entanglement_matrix = Array2::zeros((n_stages, n_stages));

        Self {
            stage_amplitudes,
            global_phase: 0.0,
            entanglement_matrix,
            energy: 0.0,
        }
    }

    /// Apply quantum evolution to the state with entanglement effects
    pub fn evolve(&mut self, timestep: f64, hamiltonian: &QuantumHamiltonian) {
        // Advanced quantum evolution with entanglement and decoherence
        // |ψ(t+dt)⟩ = exp(-iHdt/ℏ)|ψ(t)⟩ with entanglement coupling

        // Store original amplitudes for entanglement calculations
        let original_amplitudes: HashMap<String, QuantumAmplitude> = self.stage_amplitudes.clone();

        // Create stage name to index mapping to avoid borrow conflicts
        let stage_indices: HashMap<String, usize> = self
            .stage_amplitudes
            .keys()
            .enumerate()
            .map(|(idx, name)| (name.clone(), idx))
            .collect();

        for (stagename, amplitude) in &mut self.stage_amplitudes {
            if let Some(&energy) = hamiltonian.stage_energies.get(stagename) {
                let phase_change = -energy * timestep;

                // Apply rotation in complex plane with entanglement corrections
                let cos_phase = phase_change.cos();
                let sin_phase = phase_change.sin();

                // Calculate entanglement contributions from other stages
                let mut entanglement_real = 0.0;
                let mut entanglement_imaginary = 0.0;

                for (other_stage, other_amplitude) in &original_amplitudes {
                    if other_stage != stagename {
                        // Get entanglement strength from the matrix
                        let stage_idx = stage_indices.get(stagename);
                        let other_idx = stage_indices.get(other_stage);

                        if let (Some(&i), Some(&j)) = (stage_idx, other_idx) {
                            if i < self.entanglement_matrix.nrows()
                                && j < self.entanglement_matrix.ncols()
                            {
                                let entanglementstrength = self.entanglement_matrix[[i, j]];

                                // Apply entanglement coupling
                                entanglement_real +=
                                    entanglementstrength * other_amplitude.real * timestep;
                                entanglement_imaginary +=
                                    entanglementstrength * other_amplitude.imaginary * timestep;
                            }
                        }
                    }
                }

                // Apply quantum evolution with entanglement
                let new_real = amplitude.real * cos_phase - amplitude.imaginary * sin_phase
                    + entanglement_real * 0.1;
                let new_imaginary = amplitude.real * sin_phase
                    + amplitude.imaginary * cos_phase
                    + entanglement_imaginary * 0.1;

                amplitude.real = new_real;
                amplitude.imaginary = new_imaginary;

                // Apply decoherence effects
                let decoherence_factor = (-timestep * 0.01).exp(); // Small decoherence
                amplitude.real *= decoherence_factor;
                amplitude.imaginary *= decoherence_factor;
            }
        }

        self.global_phase += timestep;

        // Update entanglement matrix based on quantum correlations
        self.update_entanglement_matrix();
    }

    /// Get the index of a stage name for matrix operations
    fn get_stage_index(&self, stagename: &str) -> Option<usize> {
        self.stage_amplitudes
            .keys()
            .enumerate()
            .find(|(_, name)| name.as_str() == stagename)
            .map(|(index_, _)| index_)
    }

    /// Update entanglement matrix based on current quantum correlations
    fn update_entanglement_matrix(&mut self) {
        let _stagenames: Vec<String> = self.stage_amplitudes.keys().cloned().collect();
        let n_stages = _stagenames.len();

        for i in 0..n_stages {
            for j in 0..n_stages {
                if i != j
                    && i < self.entanglement_matrix.nrows()
                    && j < self.entanglement_matrix.ncols()
                {
                    let stage_i = &_stagenames[i];
                    let stage_j = &_stagenames[j];

                    if let (Some(amp_i), Some(amp_j)) = (
                        self.stage_amplitudes.get(stage_i),
                        self.stage_amplitudes.get(stage_j),
                    ) {
                        // Calculate quantum correlation based on amplitude overlap
                        let correlation =
                            amp_i.real * amp_j.real + amp_i.imaginary * amp_j.imaginary;

                        // Update entanglement strength
                        let current_entanglement = self.entanglement_matrix[[i, j]];
                        let new_entanglement = 0.9 * current_entanglement + 0.1 * correlation.abs();
                        self.entanglement_matrix[[i, j]] = new_entanglement.min(1.0);
                    }
                }
            }
        }
    }

    /// Apply quantum error correction to maintain coherence
    pub fn apply_quantum_error_correction(&mut self) {
        // Simplified quantum error correction
        let total_probability = self.calculate_total_probability();

        if total_probability.abs() < 1e-6 {
            // System has lost coherence, reinitialize to equal superposition
            let n_stages = self.stage_amplitudes.len();
            let amplitude_value = 1.0 / (n_stages as f64).sqrt();

            for amplitude in self.stage_amplitudes.values_mut() {
                amplitude.real = amplitude_value;
                amplitude.imaginary = 0.0;
            }
        } else if (total_probability - 1.0).abs() > 0.01 {
            // Renormalize amplitudes
            let normalization_factor = 1.0 / total_probability.sqrt();
            for amplitude in self.stage_amplitudes.values_mut() {
                amplitude.real *= normalization_factor;
                amplitude.imaginary *= normalization_factor;
            }
        }
    }

    /// Calculate total probability (should be 1 for normalized state)
    fn calculate_total_probability(&self) -> f64 {
        self.stage_amplitudes
            .values()
            .map(|amp| amp.probability())
            .sum()
    }

    /// Calculate quantum advantage factor
    pub fn calculate_quantum_advantage(&self) -> f64 {
        let coherence = self.calculate_coherence_measure();
        let entanglement = self.calculate_average_entanglement();

        1.0 + coherence * 0.5 + entanglement * 0.3
    }

    /// Calculate coherence measure
    fn calculate_coherence_measure(&self) -> f64 {
        let mut coherence = 0.0;

        for amplitude in self.stage_amplitudes.values() {
            // Coherence based on complex amplitude magnitude
            let magnitude = (amplitude.real.powi(2) + amplitude.imaginary.powi(2)).sqrt();
            coherence += magnitude;
        }

        coherence / self.stage_amplitudes.len() as f64
    }

    /// Calculate average entanglement strength
    fn calculate_average_entanglement(&self) -> f64 {
        let mut total_entanglement = 0.0;
        let mut count = 0;

        for i in 0..self.entanglement_matrix.nrows() {
            for j in 0..self.entanglement_matrix.ncols() {
                if i != j {
                    total_entanglement += self.entanglement_matrix[[i, j]];
                    count += 1;
                }
            }
        }

        if count > 0 {
            total_entanglement / count as f64
        } else {
            0.0
        }
    }

    /// Measure the quantum state to get classical processing decisions
    pub fn measure(&self) -> ProcessingDecision {
        let mut max_probability = 0.0;
        let mut optimal_stage = String::new();
        let mut stage_priorities = HashMap::new();

        for (stagename, amplitude) in &self.stage_amplitudes {
            let probability = amplitude.probability();
            stage_priorities.insert(stagename.clone(), probability);

            if probability > max_probability {
                max_probability = probability;
                optimal_stage = stagename.clone();
            }
        }

        ProcessingDecision {
            optimal_stage,
            stage_priorities,
            confidence: max_probability,
        }
    }
}

/// Quantum Hamiltonian for system evolution
#[derive(Debug, Clone)]
pub struct QuantumHamiltonian {
    /// Energy levels for each processing stage
    pub stage_energies: HashMap<String, f64>,
    /// Coupling strengths between stages
    pub coupling_matrix: Array2<f64>,
    /// External field strengths
    pub external_fields: HashMap<String, f64>,
}

impl QuantumHamiltonian {
    /// Create a new quantum Hamiltonian
    pub fn new(_stagenames: &[String]) -> Self {
        let mut stage_energies = HashMap::new();
        let mut external_fields = HashMap::new();

        // Initialize with random energies representing computational costs
        let mut rng = scirs2_core::random::rng();
        for stagename in _stagenames {
            stage_energies.insert(stagename.clone(), rng.random_range(0.1..2.0));
            external_fields.insert(stagename.clone(), 0.0);
        }

        let n_stages = _stagenames.len();
        let coupling_matrix = Array2::zeros((n_stages, n_stages));

        Self {
            stage_energies,
            coupling_matrix,
            external_fields,
        }
    }

    /// Update energies based on performance metrics
    pub fn update_energies(&mut self, performancemetrics: &HashMap<String, f64>) {
        for (stagename, &performance) in performancemetrics {
            if let Some(energy) = self.stage_energies.get_mut(stagename) {
                // Higher performance = lower energy (more favorable)
                *energy = 2.0 - performance.min(2.0);
            }
        }
    }
}

/// Processing decision from quantum measurement
#[derive(Debug, Clone)]
pub struct ProcessingDecision {
    /// Optimal stage to prioritize
    pub optimal_stage: String,
    /// Priority weights for all stages
    pub stage_priorities: HashMap<String, f64>,
    /// Measurement confidence
    pub confidence: f64,
}

/// Quantum-inspired streaming processor
#[derive(Debug)]
pub struct QuantumStreamProcessor {
    /// Current quantum state
    quantum_state: QuantumProcessingState,
    /// System Hamiltonian
    hamiltonian: QuantumHamiltonian,
    /// Processing stages
    _stagenames: Vec<String>,
    /// Performance history for adaptive optimization
    performance_history: HashMap<String, Vec<f64>>,
    /// Quantum evolution time step
    timestep: f64,
    /// Last measurement time
    last_measurement: Instant,
}

impl QuantumStreamProcessor {
    /// Create a new quantum stream processor
    pub fn new(_stagenames: Vec<String>) -> Self {
        let quantum_state = QuantumProcessingState::new(&_stagenames);
        let hamiltonian = QuantumHamiltonian::new(&_stagenames);
        let performance_history = HashMap::new();

        Self {
            quantum_state,
            hamiltonian,
            _stagenames,
            performance_history,
            timestep: 0.01,
            last_measurement: Instant::now(),
        }
    }

    /// Process frame with quantum-inspired optimization
    pub fn process_quantum_frame(&mut self, frame: Frame) -> Result<(Frame, ProcessingDecision)> {
        // Evolve quantum state
        let elapsed = self.last_measurement.elapsed().as_secs_f64();
        self.quantum_state
            .evolve(elapsed * self.timestep, &self.hamiltonian);

        // Measure quantum state for processing decision
        let decision = self.quantum_state.measure();

        // Apply quantum interference for noise reduction
        let enhanced_frame = self.apply_quantum_interference(&frame)?;

        self.last_measurement = Instant::now();
        Ok((enhanced_frame, decision))
    }

    /// Apply quantum interference for noise reduction
    fn apply_quantum_interference(&self, frame: &Frame) -> Result<Frame> {
        let (height, width) = frame.data.dim();
        let mut enhanced_data = frame.data.clone();

        // Quantum interference-inspired noise reduction
        for y in 1..height - 1 {
            for x in 1..width - 1 {
                // Create "quantum superposition" of neighboring pixels
                let neighbors = [
                    frame.data[[y - 1, x - 1]],
                    frame.data[[y - 1, x]],
                    frame.data[[y - 1, x + 1]],
                    frame.data[[y, x - 1]],
                    frame.data[[y, x]],
                    frame.data[[y, x + 1]],
                    frame.data[[y + 1, x - 1]],
                    frame.data[[y + 1, x]],
                    frame.data[[y + 1, x + 1]],
                ];

                // Apply quantum interference pattern
                let interference_weights = [
                    0.0625, 0.125, 0.0625, 0.125, 0.25, 0.125, 0.0625, 0.125, 0.0625,
                ];

                let mut interference_value = 0.0;
                for (pixel, weight) in neighbors.iter().zip(interference_weights.iter()) {
                    // Apply quantum phase based on pixel position
                    let phase = (x as f64 * 0.1 + y as f64 * 0.1) * self.quantum_state.global_phase;
                    let quantum_factor = (phase.cos() + 1.0) * 0.5; // Normalize to [0,1]
                    interference_value += pixel * weight * quantum_factor as f32;
                }

                enhanced_data[[y, x]] = interference_value;
            }
        }

        Ok(Frame {
            data: enhanced_data,
            timestamp: frame.timestamp,
            index: frame.index,
            metadata: frame.metadata.clone(),
        })
    }

    /// Update performance metrics for adaptive optimization
    pub fn update_performance(&mut self, stagename: &str, performance: f64) {
        self.performance_history
            .entry(stagename.to_string())
            .or_default()
            .push(performance);

        // Keep only recent history
        if let Some(history) = self.performance_history.get_mut(stagename) {
            if history.len() > 100 {
                history.remove(0);
            }
        }

        // Update Hamiltonian based on performance
        let mut avg_performance = HashMap::new();
        for (stage, history) in &self.performance_history {
            if !history.is_empty() {
                let avg = history.iter().sum::<f64>() / history.len() as f64;
                avg_performance.insert(stage.clone(), avg);
            }
        }

        self.hamiltonian.update_energies(&avg_performance);
    }

    /// Initialize quantum fusion capabilities
    pub async fn initialize_quantum_fusion(&mut self) -> Result<()> {
        // Initialize quantum state to optimal superposition
        for amplitude in self.quantum_state.stage_amplitudes.values_mut() {
            amplitude.normalize();
        }

        // Initialize Hamiltonian for optimal energy landscape
        self.hamiltonian.update_energies(&HashMap::new());

        // Reset performance history
        self.performance_history.clear();

        Ok(())
    }
}

/// Quantum annealing stage for parameter optimization
pub struct QuantumAnnealingStage {
    /// Current annealing temperature
    temperature: f64,
    /// Cooling rate
    cooling_rate: f64,
    /// Parameter space
    parameters: HashMap<String, f64>,
    /// Best found parameters
    best_parameters: HashMap<String, f64>,
    /// Best cost found
    best_cost: f64,
    /// Annealing step counter
    step_counter: usize,
}

impl QuantumAnnealingStage {
    /// Create a new quantum annealing stage
    pub fn new(_initialparameters: HashMap<String, f64>) -> Self {
        let best_parameters = _initialparameters.clone();

        Self {
            temperature: 100.0,
            cooling_rate: 0.99,
            parameters: _initialparameters,
            best_parameters,
            best_cost: f64::INFINITY,
            step_counter: 0,
        }
    }

    /// Perform one annealing step
    pub fn anneal_step(&mut self, costfunction: impl Fn(&HashMap<String, f64>) -> f64) -> f64 {
        let current_cost = costfunction(&self.parameters);

        // Generate neighbor solution
        let mut neighbor_params = self.parameters.clone();
        let mut rng = scirs2_core::random::rng();

        let mut param_entries: Vec<_> = neighbor_params.iter_mut().collect();
        if let Some((_param_name, param_value)) = param_entries.choose_mut(&mut rng) {
            let perturbation = rng.random_range(-0.1..0.1) * self.temperature / 100.0;
            **param_value += perturbation;
            **param_value = param_value.clamp(0.0, 1.0); // Keep in valid range
        }

        let neighbor_cost = costfunction(&neighbor_params);
        let delta_cost = neighbor_cost - current_cost;

        // Accept or reject based on quantum annealing probability
        let acceptance_probability = if delta_cost < 0.0 {
            1.0 // Always accept improvements
        } else {
            (-delta_cost / self.temperature).exp()
        };

        if rng.random::<f64>() < acceptance_probability {
            self.parameters = neighbor_params;

            if neighbor_cost < self.best_cost {
                self.best_cost = neighbor_cost;
                self.best_parameters = self.parameters.clone();
            }
        }

        // Cool down
        self.temperature *= self.cooling_rate;
        self.step_counter += 1;

        current_cost
    }

    /// Get the best parameters found
    pub fn get_best_parameters(&self) -> &HashMap<String, f64> {
        &self.best_parameters
    }
}

impl ProcessingStage for QuantumAnnealingStage {
    fn process(&mut self, frame: Frame) -> Result<Frame> {
        // Define cost function based on frame quality metrics
        let cost_function = |params: &HashMap<String, f64>| -> f64 {
            let blur_sigma = params.get("blur_sigma").unwrap_or(&1.0);
            let edge_threshold = params.get("edge_threshold").unwrap_or(&0.1);

            // Simplified cost based on parameter values
            // In practice, this would evaluate actual image quality
            (blur_sigma - 0.5).abs() + (edge_threshold - 0.2).abs()
        };

        // Perform annealing step
        let _current_cost = self.anneal_step(cost_function);

        // Apply optimized parameters to frame processing
        let best_params = self.get_best_parameters();
        let blur_sigma = *best_params.get("blur_sigma").unwrap_or(&1.0);

        // Apply optimized processing
        let processed_data = if blur_sigma > 0.1 {
            crate::simd_ops::simd_gaussian_blur(&frame.data.view(), blur_sigma as f32)?
        } else {
            frame.data.clone()
        };

        Ok(Frame {
            data: processed_data,
            timestamp: frame.timestamp,
            index: frame.index,
            metadata: frame.metadata.clone(),
        })
    }

    fn name(&self) -> &str {
        "QuantumAnnealing"
    }
}

/// Quantum entanglement-inspired feature correlation stage
pub struct QuantumEntanglementStage {
    /// Feature correlation matrix
    correlation_matrix: Array2<f64>,
    /// Entanglement strength
    entanglementstrength: f64,
    /// Feature history for correlation analysis
    feature_history: Vec<Array1<f64>>,
    /// Maximum history size
    max_history: usize,
}

impl QuantumEntanglementStage {
    /// Create a new quantum entanglement stage
    pub fn new(_feature_dimension: usize, entanglementstrength: f64) -> Self {
        Self {
            correlation_matrix: Array2::eye(_feature_dimension),
            entanglementstrength,
            feature_history: Vec::new(),
            max_history: 50,
        }
    }

    /// Extract features from frame using quantum-inspired methods
    fn extract_quantum_features(&self, frame: &Frame) -> Array1<f64> {
        let (height, width) = frame.data.dim();
        let mut features = Vec::new();

        // Extract basic statistical features
        let mean = frame.data.mean_or(0.0) as f64;
        let variance = {
            let variance_f32 = frame
                .data
                .iter()
                .map(|&x| (x - mean as f32).powi(2))
                .sum::<f32>()
                / frame.data.len() as f32;
            variance_f32 as f64
        };
        features.push(mean);
        features.push(variance);

        // Extract gradient-based features
        if let Ok((_grad_x, _grad_y, magnitude)) =
            crate::simd_ops::simd_sobel_gradients(&frame.data.view())
        {
            let grad_mean = magnitude.mean_or(0.0) as f64;
            let grad_variance = {
                let mag_mean = magnitude.mean_or(0.0);
                let variance_f32 = magnitude
                    .iter()
                    .map(|&x| (x - mag_mean).powi(2))
                    .sum::<f32>()
                    / magnitude.len() as f32;
                variance_f32 as f64
            };
            features.push(grad_mean);
            features.push(grad_variance);
        } else {
            features.push(0.0);
            features.push(0.0);
        }

        // Extract frequency domain features (simplified)
        let mut freq_energy = 0.0;
        for y in 0..height.min(8) {
            for x in 0..width.min(8) {
                let spatial_freq = ((y as f64).powf(2.0) + (x as f64).powf(2.0)).sqrt();
                freq_energy += frame.data[[y, x]] as f64 * spatial_freq;
            }
        }
        features.push(freq_energy / (64.0 * 255.0)); // Normalize

        // Add quantum coherence measure
        let coherence = self.calculate_quantum_coherence(frame);
        features.push(coherence);

        Array1::from_vec(features)
    }

    /// Calculate quantum coherence measure for the frame
    fn calculate_quantum_coherence(&self, frame: &Frame) -> f64 {
        let (height, width) = frame.data.dim();
        let mut coherence_sum = 0.0;
        let mut count = 0;

        // Sample coherence across the image
        for y in (1..height - 1).step_by(4) {
            for x in (1..width - 1).step_by(4) {
                // Local phase coherence
                let center = frame.data[[y, x]] as f64;
                let neighbors = [
                    frame.data[[y - 1, x]] as f64,
                    frame.data[[y + 1, x]] as f64,
                    frame.data[[y, x - 1]] as f64,
                    frame.data[[y, x + 1]] as f64,
                ];

                let phase_variance =
                    neighbors.iter().map(|&n| (n - center).abs()).sum::<f64>() / 4.0;

                coherence_sum += 1.0 / (1.0 + phase_variance);
                count += 1;
            }
        }

        if count > 0 {
            coherence_sum / count as f64
        } else {
            0.0
        }
    }

    /// Update correlation matrix based on quantum entanglement principles
    fn update_correlations(&mut self, features: &Array1<f64>) {
        self.feature_history.push(features.clone());

        // Keep history bounded
        if self.feature_history.len() > self.max_history {
            self.feature_history.remove(0);
        }

        // Update correlation matrix if we have enough history
        if self.feature_history.len() >= 10 {
            let n_features = features.len();
            let mut new_correlation = Array2::zeros((n_features, n_features));

            // Calculate correlations with quantum entanglement weighting
            for i in 0..n_features {
                for j in 0..n_features {
                    let mut correlation = 0.0;

                    for k in 0..self.feature_history.len() {
                        let feature_i = self.feature_history[k][i];
                        let feature_j = self.feature_history[k][j];

                        // Quantum entanglement-inspired correlation
                        let entanglement_factor =
                            (self.entanglementstrength * (feature_i * feature_j).abs()).exp();

                        correlation += feature_i * feature_j * entanglement_factor;
                    }

                    correlation /= self.feature_history.len() as f64;
                    new_correlation[[i, j]] = correlation;
                }
            }

            // Smooth update of correlation matrix
            let alpha = 0.1;
            self.correlation_matrix =
                alpha * new_correlation + (1.0 - alpha) * &self.correlation_matrix;
        }
    }

    /// Apply quantum entanglement-based enhancement
    fn apply_entanglement_enhancement(
        &self,
        frame: &Frame,
        features: &Array1<f64>,
    ) -> Result<Frame> {
        let (height, width) = frame.data.dim();
        let mut enhanced_data = frame.data.clone();

        // Use correlation matrix to enhance features
        let enhanced_features = self.correlation_matrix.dot(features);

        // Apply enhancement based on feature correlations
        for y in 0..height {
            for x in 0..width {
                let pixel_value = frame.data[[y, x]] as f64;

                // Calculate enhancement factor based on entangled features
                let spatial_weight = ((y as f64 / height as f64) + (x as f64 / width as f64)) * 0.5;
                let feature_weight = enhanced_features
                    .iter()
                    .enumerate()
                    .map(|(i, &f)| f * (i as f64 + 1.0))
                    .sum::<f64>()
                    / enhanced_features.len() as f64;

                let enhancement = 1.0 + self.entanglementstrength * spatial_weight * feature_weight;
                let enhanced_pixel = (pixel_value * enhancement).clamp(0.0, 1.0);

                enhanced_data[[y, x]] = enhanced_pixel as f32;
            }
        }

        Ok(Frame {
            data: enhanced_data,
            timestamp: frame.timestamp,
            index: frame.index,
            metadata: frame.metadata.clone(),
        })
    }
}

impl ProcessingStage for QuantumEntanglementStage {
    fn process(&mut self, frame: Frame) -> Result<Frame> {
        // Extract quantum-inspired features
        let features = self.extract_quantum_features(&frame);

        // Update correlation matrix
        self.update_correlations(&features);

        // Apply entanglement-based enhancement
        self.apply_entanglement_enhancement(&frame, &features)
    }

    fn name(&self) -> &str {
        "QuantumEntanglement"
    }
}

/// Quantum superposition-based parallel processing stage
pub struct QuantumSuperpositionStage {
    /// Processing variants in superposition
    processing_variants: Vec<ProcessingVariant>,
    /// Superposition weights
    superposition_weights: Vec<f64>,
    /// Interference pattern for combining results
    interference_pattern: Array1<f64>,
}

/// Individual processing variant in superposition
struct ProcessingVariant {
    name: String,
    sigma: f32,
    threshold: f32,
    enhancement_factor: f32,
}

impl QuantumSuperpositionStage {
    /// Create a new quantum superposition stage
    pub fn new(_numvariants: usize) -> Self {
        let mut processing_variants = Vec::new();
        let mut superposition_weights = Vec::new();
        let mut rng = scirs2_core::random::rng();

        // Create multiple processing _variants
        for i in 0.._numvariants {
            let variant = ProcessingVariant {
                name: format!("Variant_{i}"),
                sigma: rng.random_range(0.5..2.0),
                threshold: rng.random_range(0.05..0.3),
                enhancement_factor: rng.random_range(0.8..1.2),
            };

            processing_variants.push(variant);
            superposition_weights.push(1.0 / (_numvariants as f64).sqrt());
        }

        // Create interference pattern
        let interference_pattern = Array1::from_shape_fn(_numvariants, |i| {
            (i as f64 * std::f64::consts::PI / _numvariants as f64).cos()
        });

        Self {
            processing_variants,
            superposition_weights,
            interference_pattern,
        }
    }

    /// Process frame with quantum superposition
    fn process_superposition(&self, frame: &Frame) -> Result<Frame> {
        let (height, width) = frame.data.dim();
        let mut superposed_result = Array2::zeros((height, width));

        // Process with each variant in superposition
        for (i, variant) in self.processing_variants.iter().enumerate() {
            let weight = self.superposition_weights[i];
            let interference = self.interference_pattern[i];

            // Apply variant processing
            let processed = if variant.sigma > 0.1 {
                crate::simd_ops::simd_gaussian_blur(&frame.data.view(), variant.sigma)?
            } else {
                frame.data.clone()
            };

            // Add to superposition with quantum interference
            for y in 0..height {
                for x in 0..width {
                    let quantum_contribution = processed[[y, x]] as f64
                        * weight
                        * interference
                        * variant.enhancement_factor as f64;
                    superposed_result[[y, x]] += quantum_contribution;
                }
            }
        }

        // Normalize and convert back to f32
        let max_val = superposed_result
            .iter()
            .fold(f64::NEG_INFINITY, |a, &b| a.max(b));
        let min_val = superposed_result
            .iter()
            .fold(f64::INFINITY, |a, &b| a.min(b));

        if max_val != min_val {
            superposed_result.mapv_inplace(|x| (x - min_val) / (max_val - min_val));
        }

        let final_result = superposed_result.mapv(|x| x as f32);

        Ok(Frame {
            data: final_result,
            timestamp: frame.timestamp,
            index: frame.index,
            metadata: frame.metadata.clone(),
        })
    }

    /// Update superposition weights based on performance
    pub fn update_weights(&mut self, performancemetrics: &[f64]) {
        if performancemetrics.len() == self.superposition_weights.len() {
            // Quantum-inspired weight update
            let total_performance: f64 = performancemetrics.iter().sum();

            if total_performance > 0.0 {
                for (i, &performance) in performancemetrics.iter().enumerate() {
                    // Higher performance gets higher weight
                    self.superposition_weights[i] = (performance / total_performance).sqrt();
                }

                // Renormalize weights
                let weight_sum: f64 = self.superposition_weights.iter().map(|w| w * w).sum();
                let norm_factor = weight_sum.sqrt();

                if norm_factor > 0.0 {
                    for weight in &mut self.superposition_weights {
                        *weight /= norm_factor;
                    }
                }
            }
        }
    }
}

impl ProcessingStage for QuantumSuperpositionStage {
    fn process(&mut self, frame: Frame) -> Result<Frame> {
        self.process_superposition(&frame)
    }

    fn name(&self) -> &str {
        "QuantumSuperposition"
    }
}

/// Quantum-inspired adaptive streaming pipeline
pub struct QuantumAdaptiveStreamPipeline {
    /// Quantum processor for optimization
    quantum_processor: QuantumStreamProcessor,
    /// Current processing stages
    stages: Vec<Box<dyn ProcessingStage>>,
    /// Performance metrics
    performancemetrics: Arc<Mutex<HashMap<String, f64>>>,
    /// Adaptation counter
    adaptation_counter: usize,
}

impl QuantumAdaptiveStreamPipeline {
    /// Create a new quantum adaptive streaming pipeline
    pub fn new(_stagenames: Vec<String>) -> Self {
        let quantum_processor = QuantumStreamProcessor::new(_stagenames);

        Self {
            quantum_processor,
            stages: Vec::new(),
            performancemetrics: Arc::new(Mutex::new(HashMap::new())),
            adaptation_counter: 0,
        }
    }

    /// Add a quantum-enhanced processing stage
    pub fn add_quantum_stage<S: ProcessingStage + 'static>(mut self, stage: S) -> Self {
        self.stages.push(Box::new(stage));
        self
    }

    /// Process frame with quantum optimization
    pub fn process_quantum_optimized(&mut self, frame: Frame) -> Result<Frame> {
        // Get quantum processing decision
        let (enhanced_frame, decision) = self.quantum_processor.process_quantum_frame(frame)?;

        // Apply processing stages with quantum-guided optimization
        let mut current_frame = enhanced_frame;

        for stage in &mut self.stages {
            let stagename = stage.name().to_string();
            let start_time = Instant::now();

            // Check if this stage should be prioritized
            let priority = decision.stage_priorities.get(&stagename).unwrap_or(&1.0);

            if *priority > 0.5 {
                current_frame = stage.process(current_frame)?;

                let processing_time = start_time.elapsed().as_secs_f64();
                let performance = 1.0 / (1.0 + processing_time); // Higher performance for faster processing

                // Update performance metrics
                if let Ok(mut metrics) = self.performancemetrics.lock() {
                    metrics.insert(stagename.clone(), performance);
                }

                // Update quantum processor
                self.quantum_processor
                    .update_performance(&stagename, performance);
            }
        }

        self.adaptation_counter += 1;
        Ok(current_frame)
    }

    /// Get quantum optimization metrics
    pub fn get_quantum_metrics(&self) -> HashMap<String, f64> {
        self.performancemetrics
            .lock()
            .expect("Mutex should not be poisoned")
            .clone()
    }
}

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

    #[test]
    fn test_quantum_amplitude() {
        let mut amplitude = QuantumAmplitude::new(0.6, 0.8);
        assert!((amplitude.probability() - 1.0).abs() < 1e-10);

        amplitude.normalize();
        assert!((amplitude.probability() - 1.0).abs() < 1e-10);
    }

    #[test]
    fn test_quantum_processing_state() {
        let _stagenames = vec!["stage1".to_string(), "stage2".to_string()];
        let mut state = QuantumProcessingState::new(&_stagenames);

        let hamiltonian = QuantumHamiltonian::new(&_stagenames);
        state.evolve(0.1, &hamiltonian);

        let decision = state.measure();
        assert!(decision.confidence >= 0.0 && decision.confidence <= 1.0);
    }

    #[test]
    #[ignore = "Test failure - unwrap on None in scirs2-core/src/simd/dot.rs:511"]
    fn test_quantum_annealing_stage() {
        let mut params = HashMap::new();
        params.insert("blur_sigma".to_string(), 1.0);
        params.insert("edge_threshold".to_string(), 0.1);

        let mut annealing_stage = QuantumAnnealingStage::new(params);

        let frame = Frame {
            data: Array2::from_shape_fn((10, 10), |(y, x)| (x + y) as f32 / 20.0),
            timestamp: Instant::now(),
            index: 0,
            metadata: Some(FrameMetadata {
                width: 10,
                height: 10,
                fps: 30.0,
                channels: 1,
            }),
        };

        let result = annealing_stage.process(frame);
        assert!(result.is_ok());
    }

    #[test]
    fn test_quantum_entanglement_stage() {
        let mut entanglement_stage = QuantumEntanglementStage::new(6, 0.1);

        let frame = Frame {
            data: Array2::from_shape_fn((20, 20), |(y, x)| (x as f32 + y as f32) / 40.0),
            timestamp: Instant::now(),
            index: 0,
            metadata: None,
        };

        let result = entanglement_stage.process(frame);
        assert!(result.is_ok());
    }

    #[test]
    #[ignore = "Test failure - unwrap on None in scirs2-core/src/simd/dot.rs:511"]
    fn test_quantum_superposition_stage() {
        let mut superposition_stage = QuantumSuperpositionStage::new(4);

        let frame = Frame {
            data: Array2::from_shape_fn((15, 15), |(y, x)| ((x * y) as f32).sin()),
            timestamp: Instant::now(),
            index: 0,
            metadata: None,
        };

        let result = superposition_stage.process(frame);
        assert!(result.is_ok());

        // Test weight update
        let performancemetrics = vec![0.8, 0.6, 0.9, 0.7];
        superposition_stage.update_weights(&performancemetrics);
    }

    #[test]
    fn test_quantum_stream_processor() {
        let _stagenames = vec!["blur".to_string(), "edge".to_string()];
        let mut processor = QuantumStreamProcessor::new(_stagenames);

        let frame = Frame {
            data: Array2::from_shape_fn((8, 8), |(y, x)| (x + y) as f32 / 16.0),
            timestamp: Instant::now(),
            index: 0,
            metadata: None,
        };

        let result = processor.process_quantum_frame(frame);
        assert!(result.is_ok());

        processor.update_performance("blur", 0.8);
        processor.update_performance("edge", 0.9);
    }
}