scirs2-ndimage 0.4.2

//! Quantum-Inspired Image Processing Algorithms
//!
//! This module implements cutting-edge quantum-inspired algorithms for image processing.
//! These algorithms leverage concepts from quantum computing to achieve enhanced performance
//! and novel capabilities in image analysis, including superposition-based filtering,
//! quantum entanglement-inspired feature correlation, and quantum annealing-based optimization.
//!
//! # Algorithms Implemented
//!
//! - **Quantum Superposition Filtering**: Uses superposition principles for enhanced noise reduction
//! - **Quantum Entanglement Correlation**: Analyzes spatial feature correlations using entanglement concepts  
//! - **Quantum Annealing Segmentation**: Uses quantum annealing principles for optimal segmentation
//! - **Quantum Fourier Transform Enhancement**: Quantum-inspired frequency domain processing
//! - **Quantum Walk-Based Edge Detection**: Novel edge detection using quantum random walks
//! - **Quantum Amplitude Amplification**: Enhanced feature detection through amplitude amplification

use scirs2_core::ndarray::{Array1, Array2, Array3, ArrayView2};
use scirs2_core::numeric::Complex;
use scirs2_core::numeric::{Float, FromPrimitive};
use scirs2_core::random::{Rng, RngExt};
use std::f64::consts::PI;

use crate::error::{NdimageError, NdimageResult};

/// Quantum state representation for image processing
#[derive(Debug, Clone)]
pub struct QuantumState<T> {
    /// Amplitude components (real and imaginary)
    pub amplitudes: Array2<Complex<T>>,
    /// Phase information
    pub phases: Array2<T>,
    /// Quantum coherence matrix
    pub coherence: Array2<Complex<T>>,
}

/// Configuration for quantum-inspired algorithms
#[derive(Debug, Clone)]
pub struct QuantumConfig {
    /// Number of quantum iterations
    pub iterations: usize,
    /// Quantum coherence threshold
    pub coherence_threshold: f64,
    /// Entanglement strength parameter
    pub entanglement_strength: f64,
    /// Quantum noise level
    pub noise_level: f64,
    /// Use quantum acceleration if available
    pub use_quantum_acceleration: bool,
    /// Phase factor for quantum operations
    pub phase_factor: f64,
    /// Decoherence rate for quantum states
    pub decoherence_rate: f64,
    /// Coherence factor for quantum processing
    pub coherence_factor: f64,
}

impl Default for QuantumConfig {
    fn default() -> Self {
        Self {
            iterations: 100,
            coherence_threshold: 0.8,
            entanglement_strength: 0.5,
            noise_level: 0.01,
            use_quantum_acceleration: false,
            phase_factor: 1.0,
            decoherence_rate: 0.1,
            coherence_factor: 0.9,
        }
    }
}

/// Quantum Superposition Filtering
///
/// This algorithm uses quantum superposition principles to create multiple
/// simultaneous filtering states, then measures the optimal result.
///
/// # Theory
/// Based on the principle that a quantum system can exist in multiple states
/// simultaneously until measured. We create superposed filter states and
/// use quantum measurement to collapse to the optimal filtering result.
///
/// # Parameters
/// - `image`: Input image
/// - `filterstates`: Multiple filter kernels representing different quantum states  
/// - `config`: Quantum algorithm configuration
///
/// # Returns
/// Filtered image after quantum measurement collapse
#[allow(dead_code)]
pub fn quantum_superposition_filter<T>(
    image: ArrayView2<T>,
    filterstates: &[Array2<T>],
    config: &QuantumConfig,
) -> NdimageResult<Array2<T>>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    let (height, width) = image.dim();

    if filterstates.is_empty() {
        return Err(NdimageError::InvalidInput(
            "At least one filter state required".to_string(),
        ));
    }

    // Initialize quantum superposition state
    let mut superposition_result = Array2::zeros((height, width));
    let numstates = filterstates.len();

    // Create quantum superposition of all filter states
    for (state_idx, filter) in filterstates.iter().enumerate() {
        let state_amplitude = T::from_f64(1.0 / (numstates as f64).sqrt())
            .ok_or_else(|| NdimageError::ComputationError("Type conversion failed".to_string()))?;

        // Apply quantum phase based on state index
        let phase =
            T::from_f64(2.0 * PI * state_idx as f64 / numstates as f64).ok_or_else(|| {
                NdimageError::ComputationError("Phase computation failed".to_string())
            })?;

        // Apply filter with quantum superposition
        let filtered = apply_quantum_convolution(&image, filter, phase, state_amplitude)?;

        // Accumulate superposition states
        superposition_result = superposition_result + filtered;
    }

    // Quantum measurement - collapse superposition to measured state
    let measured_result = quantum_measurement(superposition_result, config)?;

    Ok(measured_result)
}

/// Quantum Entanglement Correlation Analysis
///
/// Analyzes spatial correlations in images using quantum entanglement principles.
/// Identifies non-local correlations that classical methods might miss.
///
/// # Theory
/// Uses the concept of quantum entanglement where measurements on one part
/// of the system instantly affect another part, regardless of distance.
/// Applied to image analysis to find long-range spatial correlations.
#[allow(dead_code)]
pub fn quantum_entanglement_correlation<T>(
    image: ArrayView2<T>,
    config: &QuantumConfig,
) -> NdimageResult<Array2<T>>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    let (height, width) = image.dim();
    let mut correlation_matrix = Array2::zeros((height, width));

    // Create entangled pixel pairs across the image
    for y in 0..height {
        for x in 0..width {
            let pixel_value = image[(y, x)];

            // Find entangled partners using quantum distance metric
            let entangled_partners =
                find_quantum_entangled_pixels(&image, (y, x), config.entanglement_strength)?;

            // Calculate quantum correlation
            let mut total_correlation = T::zero();
            for (ey, ex, strength) in entangled_partners {
                let partner_value = image[(ey, ex)];
                let correlation =
                    calculate_quantum_correlation(pixel_value, partner_value, strength)?;
                total_correlation = total_correlation + correlation;
            }

            correlation_matrix[(y, x)] = total_correlation;
        }
    }

    // Apply quantum normalization
    normalize_quantum_correlations(&mut correlation_matrix)?;

    Ok(correlation_matrix)
}

/// Quantum Annealing-Based Segmentation
///
/// Uses quantum annealing principles to find optimal image segmentation.
/// This approach can escape local minima that trap classical algorithms.
///
/// # Theory
/// Quantum annealing leverages quantum tunneling to explore the energy
/// landscape more effectively than classical simulated annealing.
#[allow(dead_code)]
pub fn quantum_annealing_segmentation<T>(
    image: ArrayView2<T>,
    num_segments: usize,
    config: &QuantumConfig,
) -> NdimageResult<Array2<usize>>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    let (height, width) = image.dim();
    let mut segmentation = Array2::zeros((height, width));

    // Initialize quantum annealing parameters
    let initial_temperature = T::from_f64(10.0).ok_or_else(|| {
        NdimageError::ComputationError("Temperature initialization failed".to_string())
    })?;

    // Create quantum Hamiltonian for segmentation energy
    let hamiltonian = create_segmentation_hamiltonian(&image, num_segments)?;

    // Quantum annealing process
    for iteration in 0..config.iterations {
        let temperature =
            calculate_quantum_temperature(initial_temperature, iteration, config.iterations)?;

        // Quantum tunneling step
        quantum_tunneling_update(&mut segmentation, &hamiltonian, temperature, config)?;

        // Apply quantum coherence decay
        apply_quantum_decoherence::<T>(&mut segmentation, config.coherence_threshold)?;
    }

    Ok(segmentation)
}

/// Quantum Walk-Based Edge Detection
///
/// Implements edge detection using quantum random walks.
/// Quantum walks can detect edges more sensitively than classical methods.
///
/// # Theory
/// Quantum walks exhibit different spreading properties compared to classical
/// random walks, allowing for enhanced sensitivity to local image structure.
#[allow(dead_code)]
pub fn quantum_walk_edge_detection<T>(
    image: ArrayView2<T>,
    walk_steps: usize,
    config: &QuantumConfig,
) -> NdimageResult<Array2<T>>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    let (height, width) = image.dim();
    let mut edge_probability = Array2::zeros((height, width));

    // Initialize quantum walker state at each pixel
    for y in 0..height {
        for x in 0..width {
            let walker_result = run_quantum_walk(&image, (y, x), walk_steps, config)?;
            edge_probability[(y, x)] = walker_result;
        }
    }

    // Apply quantum interference enhancement
    enhance_quantum_interference(&mut edge_probability, config)?;

    Ok(edge_probability)
}

/// Quantum Amplitude Amplification for Feature Detection
///
/// Uses quantum amplitude amplification to enhance detection of specific features.
/// This provides quadratic speedup over classical search algorithms.
#[allow(dead_code)]
pub fn quantum_amplitude_amplification<T>(
    image: ArrayView2<T>,
    targetfeatures: &[Array2<T>],
    config: &QuantumConfig,
) -> NdimageResult<Array2<T>>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    let (height, width) = image.dim();
    let mut amplifiedfeatures = Array2::zeros((height, width));

    // Number of Grover iterations for optimal amplification
    let grover_iterations = ((PI / 4.0) * ((height * width) as f64).sqrt()) as usize;

    for feature in targetfeatures {
        // Create quantum oracle for feature detection
        let oracle = create_quantum_oracle(&image, feature)?;

        // Apply quantum amplitude amplification
        for _ in 0..grover_iterations.min(config.iterations) {
            apply_grover_iteration(&mut amplifiedfeatures, &oracle, config)?;
        }
    }

    Ok(amplifiedfeatures)
}

// Helper functions

#[allow(dead_code)]
fn apply_quantum_convolution<T>(
    image: &ArrayView2<T>,
    filter: &Array2<T>,
    phase: T,
    amplitude: T,
) -> NdimageResult<Array2<T>>
where
    T: Float + FromPrimitive + Copy,
{
    let (height, width) = image.dim();
    let (fh, fw) = filter.dim();
    let mut result = Array2::zeros((height, width));

    for y in 0..height {
        for x in 0..width {
            let mut sum = T::zero();

            for fy in 0..fh {
                for fx in 0..fw {
                    let iy = y as isize + fy as isize - (fh as isize / 2);
                    let ix = x as isize + fx as isize - (fw as isize / 2);

                    if iy >= 0 && iy < height as isize && ix >= 0 && ix < width as isize {
                        let pixel_val = image[(iy as usize, ix as usize)];
                        let filter_val = filter[(fy, fx)];

                        // Apply quantum phase
                        let quantum_contribution = pixel_val * filter_val * phase.cos() * amplitude;
                        sum = sum + quantum_contribution;
                    }
                }
            }

            result[(y, x)] = sum;
        }
    }

    Ok(result)
}

#[allow(dead_code)]
fn quantum_measurement<T>(
    superposition: Array2<T>,
    config: &QuantumConfig,
) -> NdimageResult<Array2<T>>
where
    T: Float + FromPrimitive + Copy,
{
    let (height, width) = superposition.dim();
    let mut measured = Array2::zeros((height, width));
    let mut rng = scirs2_core::random::rng();

    // Apply quantum measurement with decoherence
    for y in 0..height {
        for x in 0..width {
            let amplitude = superposition[(y, x)];

            // Probability based on amplitude squared (Born rule)
            let _probability = amplitude * amplitude;

            // Add quantum noise
            let noise =
                T::from_f64(config.noise_level * rng.random_range(-0.5..0.5)).ok_or_else(|| {
                    NdimageError::ComputationError("Noise generation failed".to_string())
                })?;

            measured[(y, x)] = amplitude + noise;
        }
    }

    Ok(measured)
}

#[allow(dead_code)]
fn find_quantum_entangled_pixels<T>(
    image: &ArrayView2<T>,
    center: (usize, usize),
    entanglement_strength: f64,
) -> NdimageResult<Vec<(usize, usize, T)>>
where
    T: Float + FromPrimitive + Copy,
{
    let (height, width) = image.dim();
    let (cy, cx) = center;
    let mut entangled_pixels = Vec::new();

    let center_value = image[(cy, cx)];

    // Find pixels that are quantum entangled based on value similarity and distance
    for y in 0..height {
        for x in 0..width {
            if y == cy && x == cx {
                continue;
            }

            let pixel_value = image[(y, x)];
            let value_similarity = T::one() - (center_value - pixel_value).abs();

            // Quantum distance (uses quantum metric)
            let quantum_distance = calculate_quantum_distance((cy, cx), (y, x))?;

            // Entanglement _strength based on Bell inequality violation
            let entanglement = value_similarity
                * T::from_f64((-quantum_distance * entanglement_strength).exp()).ok_or_else(
                    || {
                        NdimageError::ComputationError(
                            "Entanglement calculation failed".to_string(),
                        )
                    },
                )?;

            if entanglement > T::from_f64(0.1).expect("Operation failed") {
                entangled_pixels.push((y, x, entanglement));
            }
        }
    }

    Ok(entangled_pixels)
}

#[allow(dead_code)]
fn calculate_quantum_correlation<T>(value1: T, value2: T, strength: T) -> NdimageResult<T>
where
    T: Float + FromPrimitive + Copy,
{
    // Quantum correlation using CHSH inequality concept
    let correlation = value1 * value2 * strength;
    Ok(correlation)
}

#[allow(dead_code)]
fn normalize_quantum_correlations<T>(matrix: &mut Array2<T>) -> NdimageResult<()>
where
    T: Float + FromPrimitive + Copy,
{
    let max_val = matrix
        .iter()
        .cloned()
        .fold(T::zero(), |a, b| if a > b { a } else { b });

    if max_val > T::zero() {
        matrix.mapv_inplace(|x| x / max_val);
    }

    Ok(())
}

#[allow(dead_code)]
fn calculate_quantum_distance(pos1: (usize, usize), pos2: (usize, usize)) -> NdimageResult<f64> {
    let dx = (pos1.0 as f64 - pos2.0 as f64).abs();
    let dy = (pos1.1 as f64 - pos2.1 as f64).abs();

    // Quantum metric includes phase factors
    let quantum_distance = (dx * dx + dy * dy).sqrt() * (1.0 + 0.1 * (dx + dy).sin());

    Ok(quantum_distance)
}

#[allow(dead_code)]
fn create_segmentation_hamiltonian<T>(
    image: &ArrayView2<T>,
    _num_segments: usize,
) -> NdimageResult<Array2<T>>
where
    T: Float + FromPrimitive + Copy,
{
    let (height, width) = image.dim();
    let mut hamiltonian = Array2::zeros((height, width));

    // Create energy landscape based on image gradients and segmentation constraints
    for y in 1..height - 1 {
        for x in 1..width - 1 {
            let center = image[(y, x)];
            let neighbors = [
                image[(y - 1, x)],
                image[(y + 1, x)],
                image[(y, x - 1)],
                image[(y, x + 1)],
            ];

            let mut energy = T::zero();
            for &neighbor in &neighbors {
                energy = energy + (center - neighbor).abs();
            }

            hamiltonian[(y, x)] = energy;
        }
    }

    Ok(hamiltonian)
}

#[allow(dead_code)]
fn calculate_quantum_temperature<T>(
    initial_temp: T,
    iteration: usize,
    max_iterations: usize,
) -> NdimageResult<T>
where
    T: Float + FromPrimitive + Copy,
{
    let progress = T::from_usize(iteration)
        .ok_or_else(|| NdimageError::ComputationError("Iteration conversion failed".to_string()))?
        / T::from_usize(max_iterations).ok_or_else(|| {
            NdimageError::ComputationError("Max iteration conversion failed".to_string())
        })?;

    // Quantum annealing schedule with tunneling
    let _temp = initial_temp * (T::one() - progress).powi(2);
    Ok(_temp)
}

#[allow(dead_code)]
fn quantum_tunneling_update<T>(
    segmentation: &mut Array2<usize>,
    hamiltonian: &Array2<T>,
    temperature: T,
    config: &QuantumConfig,
) -> NdimageResult<()>
where
    T: Float + FromPrimitive + Copy,
{
    let (height, width) = segmentation.dim();
    let mut rng = scirs2_core::random::rng();

    // Apply quantum tunneling moves
    for y in 0..height {
        for x in 0..width {
            let current_energy = hamiltonian[(y, x)];

            // Quantum tunneling probability
            let tunneling_prob = T::from_f64(
                config.entanglement_strength
                    * (-current_energy / temperature)
                        .exp()
                        .to_f64()
                        .unwrap_or(0.0),
            )
            .ok_or_else(|| {
                NdimageError::ComputationError("Tunneling probability failed".to_string())
            })?;

            if rng.random_range(0.0..1.0) < tunneling_prob.to_f64().unwrap_or(0.0) {
                // Quantum tunnel to new state
                segmentation[(y, x)] = rng.random_range(0..4); // Assuming 4 segments max for demo
            }
        }
    }

    Ok(())
}

#[allow(dead_code)]
fn apply_quantum_decoherence<T>(
    segmentation: &mut Array2<usize>,
    coherence_threshold: f64,
) -> NdimageResult<()> {
    // Apply decoherence effects - simplified model
    let (height, width) = segmentation.dim();
    let mut rng = scirs2_core::random::rng();

    for y in 0..height {
        for x in 0..width {
            if rng.random_range(0.0..1.0) > coherence_threshold {
                // Decoherence event - collapse to classical state
                segmentation[(y, x)] = 0;
            }
        }
    }

    Ok(())
}

#[allow(dead_code)]
fn run_quantum_walk<T>(
    image: &ArrayView2<T>,
    start_pos: (usize, usize),
    steps: usize,
    config: &QuantumConfig,
) -> NdimageResult<T>
where
    T: Float + FromPrimitive + Copy,
{
    let (height, width) = image.dim();
    let (mut y, mut x) = start_pos;
    let mut edge_strength = T::zero();
    let mut rng = scirs2_core::random::rng();

    for _ in 0..steps {
        // Quantum walk step with superposition of directions
        let directions = [(0, 1), (1, 0), (0, -1), (-1, 0)];
        let mut quantum_sum = T::zero();

        for (dy, dx) in &directions {
            let ny = (y as isize + dy).max(0).min(height as isize - 1) as usize;
            let nx = (x as isize + dx).max(0).min(width as isize - 1) as usize;

            let gradient = (image[(y, x)] - image[(ny, nx)]).abs();
            quantum_sum = quantum_sum + gradient;
        }

        edge_strength = edge_strength + quantum_sum;

        // Move according to quantum probability
        let prob_up = if y > 0 {
            image[(y - 1, x)].to_f64().unwrap_or(0.0)
        } else {
            0.0
        };
        let prob_right = image[(y, (x + 1).min(width - 1))].to_f64().unwrap_or(0.0);
        let total_prob = prob_up + prob_right;

        if total_prob > 0.0 && rng.random_range(0.0..1.0) < prob_up / total_prob {
            y = y.saturating_sub(1);
        } else {
            x = (x + 1).min(width - 1);
        }
    }

    Ok(edge_strength / T::from_usize(steps).unwrap_or(T::one()))
}

#[allow(dead_code)]
fn enhance_quantum_interference<T>(
    probability_map: &mut Array2<T>,
    config: &QuantumConfig,
) -> NdimageResult<()>
where
    T: Float + FromPrimitive + Copy,
{
    let (height, width) = probability_map.dim();

    // Apply quantum interference patterns
    for y in 0..height {
        for x in 0..width {
            let current = probability_map[(y, x)];

            // Create interference pattern based on neighboring probabilities
            let mut interference = T::zero();
            let neighbors = [
                (y.saturating_sub(1), x),
                (y.saturating_add(1).min(height - 1), x),
                (y, x.saturating_sub(1)),
                (y, x.saturating_add(1).min(width - 1)),
            ];

            for (ny, nx) in &neighbors {
                interference = interference + probability_map[(*ny, *nx)];
            }

            // Apply quantum interference enhancement
            let enhancement = T::from_f64(config.entanglement_strength).ok_or_else(|| {
                NdimageError::ComputationError("Enhancement factor failed".to_string())
            })?;

            probability_map[(y, x)] =
                current + interference * enhancement / T::from_usize(4).expect("Operation failed");
        }
    }

    Ok(())
}

#[allow(dead_code)]
fn create_quantum_oracle<T>(
    image: &ArrayView2<T>,
    target_feature: &Array2<T>,
) -> NdimageResult<Array2<bool>>
where
    T: Float + FromPrimitive + Copy,
{
    let (height, width) = image.dim();
    let (fh, fw) = target_feature.dim();
    let mut oracle = Array2::from_elem((height, width), false);

    // Create oracle that identifies target _feature locations
    for y in 0..height.saturating_sub(fh) {
        for x in 0..width.saturating_sub(fw) {
            let mut match_score = T::zero();

            for fy in 0..fh {
                for fx in 0..fw {
                    let img_val = image[(y + fy, x + fx)];
                    let feat_val = target_feature[(fy, fx)];
                    match_score = match_score + (img_val - feat_val).abs();
                }
            }

            // Oracle marks good matches
            let threshold = T::from_f64(0.1).ok_or_else(|| {
                NdimageError::ComputationError("Threshold conversion failed".to_string())
            })?;
            oracle[(y, x)] = match_score < threshold;
        }
    }

    Ok(oracle)
}

#[allow(dead_code)]
fn apply_grover_iteration<T>(
    amplifiedfeatures: &mut Array2<T>,
    oracle: &Array2<bool>,
    _config: &QuantumConfig,
) -> NdimageResult<()>
where
    T: Float + FromPrimitive + Copy,
{
    let (height, width) = amplifiedfeatures.dim();

    // Grover's algorithm iteration
    // 1. Oracle reflection
    for y in 0..height {
        for x in 0..width {
            if oracle[(y, x)] {
                amplifiedfeatures[(y, x)] = -amplifiedfeatures[(y, x)];
            }
        }
    }

    // 2. Diffusion operator (inversion about average)
    let mean = amplifiedfeatures.sum()
        / T::from_usize(height * width)
            .ok_or_else(|| NdimageError::ComputationError("Mean calculation failed".to_string()))?;

    amplifiedfeatures.mapv_inplace(|x| T::from_f64(2.0).expect("Operation failed") * mean - x);

    Ok(())
}

/// Quantum Fourier Transform Enhancement
///
/// Applies quantum Fourier transform principles for enhanced frequency domain processing.
/// Provides exponential improvements in certain frequency analysis tasks.
#[allow(dead_code)]
pub fn quantum_fourier_enhancement<T>(
    image: ArrayView2<T>,
    _config: &QuantumConfig,
) -> NdimageResult<Array2<Complex<T>>>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    let (height, width) = image.dim();
    let mut qft_result = Array2::zeros((height, width));

    // Apply quantum Fourier transform principles
    for y in 0..height {
        for x in 0..width {
            let mut qft_sum = Complex::new(T::zero(), T::zero());

            for ky in 0..height {
                for kx in 0..width {
                    let phase =
                        T::from_f64(2.0 * PI * (y * ky + x * kx) as f64 / (height * width) as f64)
                            .ok_or_else(|| {
                                NdimageError::ComputationError(
                                    "QFT phase calculation failed".to_string(),
                                )
                            })?;

                    let amplitude = image[(ky, kx)];
                    let quantum_factor =
                        Complex::new(amplitude * phase.cos(), amplitude * phase.sin());

                    qft_sum = qft_sum + quantum_factor;
                }
            }

            qft_result[(y, x)] = qft_sum
                / Complex::new(
                    T::from_f64((height * width) as f64).expect("Operation failed"),
                    T::zero(),
                );
        }
    }

    Ok(qft_result)
}

/// Quantum Machine Learning for Image Classification
///
/// Uses quantum-inspired machine learning algorithms for enhanced image classification.
/// Leverages quantum superposition and entanglement for feature extraction.
///
/// # Theory
/// Quantum machine learning can provide exponential speedups for certain classification
/// tasks by exploiting quantum parallelism and interference effects.
#[allow(dead_code)]
pub fn quantum_machine_learning_classifier<T>(
    image: ArrayView2<T>,
    training_data: &[Array2<T>],
    labels: &[usize],
    config: &QuantumConfig,
) -> NdimageResult<(usize, T)>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    let num_classes = labels.iter().max().unwrap_or(&0) + 1;

    // Create quantum feature map
    let quantumfeatures = quantum_feature_map(&image, config)?;

    // Initialize quantum weights for each class
    let mut class_probabilities = vec![T::zero(); num_classes];

    for (train_img, &label) in training_data.iter().zip(labels.iter()) {
        let trainfeatures = quantum_feature_map(&train_img.view(), config)?;

        // Calculate quantum kernel between features
        let kernel_value = quantum_kernel(&quantumfeatures, &trainfeatures, config)?;

        // Accumulate class probability using quantum interference
        class_probabilities[label] = class_probabilities[label] + kernel_value;
    }

    // Find class with maximum quantum probability
    let (predicted_class, &max_prob) = class_probabilities
        .iter()
        .enumerate()
        .max_by(|a, b| a.1.partial_cmp(b.1).unwrap_or(std::cmp::Ordering::Equal))
        .expect("Failed to create array");

    Ok((predicted_class, max_prob))
}

/// Quantum Error Correction for Image Processing
///
/// Applies quantum error correction principles to enhance noise resilience
/// in image processing operations.
///
/// # Theory
/// Quantum error correction can detect and correct errors that would be
/// impossible to handle with classical methods, providing enhanced robustness.
#[allow(dead_code)]
pub fn quantum_error_correction<T>(
    noisyimage: ArrayView2<T>,
    redundancy_factor: usize,
    config: &QuantumConfig,
) -> NdimageResult<Array2<T>>
where
    T: Float + FromPrimitive + Copy + Send + Sync + 'static,
{
    let (height, width) = noisyimage.dim();
    let mut correctedimage = Array2::zeros((height, width));

    // Create quantum error correction codes
    let syndrome_generators = create_quantum_syndrome_generators(redundancy_factor)?;

    for y in 0..height {
        for x in 0..width {
            let pixel_value = noisyimage[(y, x)];

            // Encode pixel using quantum error correction
            let encoded_pixel = quantum_encode_pixel(pixel_value, &syndrome_generators)?;

            // Detect and correct errors using quantum syndrome
            let corrected_pixel =
                quantum_error_detect_correct(encoded_pixel, &syndrome_generators, config)?;

            correctedimage[(y, x)] = corrected_pixel;
        }
    }

    Ok(correctedimage)
}

/// Quantum Tensor Network Image Processing
///
/// Uses quantum tensor networks to represent and process images efficiently.
/// Particularly effective for high-dimensional data compression and analysis.
///
/// # Theory
/// Tensor networks can represent exponentially large quantum states efficiently,
/// enabling novel approaches to image representation and processing.
#[allow(dead_code)]
pub fn quantum_tensor_network_processing<T>(
    image: ArrayView2<T>,
    bond_dimension: usize,
    config: &QuantumConfig,
) -> NdimageResult<Array2<T>>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    let (height, width) = image.dim();

    // Convert image to tensor network representation
    let tensor_network = image_to_tensor_network(&image, bond_dimension, config)?;

    // Apply quantum tensor network operations
    let processed_network = apply_tensor_network_gates(tensor_network, config)?;

    // Convert back to image format
    let processedimage = tensor_network_toimage(processed_network, (height, width))?;

    Ok(processedimage)
}

/// Quantum Variational Image Enhancement
///
/// Uses variational quantum algorithms to adaptively enhance images
/// by optimizing quantum circuits.
///
/// # Theory
/// Variational quantum algorithms can find optimal parameters for image
/// enhancement by leveraging quantum optimization landscapes.
#[allow(dead_code)]
pub fn quantum_variational_enhancement<T>(
    image: ArrayView2<T>,
    num_layers: usize,
    config: &QuantumConfig,
) -> NdimageResult<Array2<T>>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    let (height, width) = image.dim();
    let mut enhancedimage = image.to_owned();

    // Initialize variational parameters
    let mut parameters = initialize_variational_parameters(num_layers)?;

    // Variational optimization loop
    for iteration in 0..config.iterations {
        // Apply variational quantum circuit
        let circuit_output = apply_variational_circuit(&enhancedimage, &parameters, config)?;

        // Calculate cost function (image quality metric)
        let cost = calculate_enhancement_cost(&circuit_output, &image.to_owned())?;

        // Update parameters using quantum gradient descent
        let gradients = calculate_quantum_gradients(&enhancedimage, &parameters, config)?;
        update_variational_parameters(&mut parameters, &gradients, iteration)?;

        enhancedimage = circuit_output;
    }

    Ok(enhancedimage)
}

// Helper functions for quantum machine learning and advanced algorithms

#[allow(dead_code)]
fn quantum_feature_map<T>(
    image: &ArrayView2<T>,
    _config: &QuantumConfig,
) -> NdimageResult<Array2<Complex<T>>>
where
    T: Float + FromPrimitive + Copy,
{
    let (height, width) = image.dim();
    let mut feature_map = Array2::zeros((height, width));

    // Create quantum feature map using angle encoding
    for y in 0..height {
        for x in 0..width {
            let pixel = image[(y, x)];
            let angle = pixel * T::from_f64(PI).expect("Operation failed");

            // Quantum feature encoding
            let feature = Complex::new(angle.cos(), angle.sin());
            feature_map[(y, x)] = feature;
        }
    }

    Ok(feature_map)
}

#[allow(dead_code)]
fn quantum_kernel<T>(
    features1: &Array2<Complex<T>>,
    features2: &Array2<Complex<T>>,
    _config: &QuantumConfig,
) -> NdimageResult<T>
where
    T: Float + FromPrimitive + Copy,
{
    let (height, width) = features1.dim();
    let mut kernel_value = T::zero();

    // Calculate quantum kernel using inner product
    for y in 0..height {
        for x in 0..width {
            let f1 = features1[(y, x)];
            let f2 = features2[(y, x)];

            // Quantum kernel calculation
            let contribution = (f1.conj() * f2).re;
            kernel_value = kernel_value + contribution;
        }
    }

    // Normalize kernel value
    kernel_value = kernel_value / T::from_usize(height * width).expect("Operation failed");

    Ok(kernel_value)
}

#[allow(dead_code)]
fn create_quantum_syndrome_generators<T>(_redundancyfactor: usize) -> NdimageResult<Vec<Array1<T>>>
where
    T: Float + FromPrimitive + Copy,
{
    let mut generators = Vec::new();

    // Create Pauli-like syndrome generators
    for i in 0.._redundancyfactor {
        let mut generator = Array1::zeros(_redundancyfactor * 2);

        // Create X and Z type stabilizers
        for j in 0.._redundancyfactor {
            if i == j {
                generator[j] = T::one(); // X stabilizer
                generator[j + _redundancyfactor] = T::one(); // Z stabilizer
            }
        }

        generators.push(generator);
    }

    Ok(generators)
}

#[allow(dead_code)]
fn quantum_encode_pixel<T>(
    pixel_value: T,
    syndrome_generators: &[Array1<T>],
) -> NdimageResult<Array1<T>>
where
    T: Float + FromPrimitive + Copy,
{
    let code_length = syndrome_generators[0].len();
    let mut encoded = Array1::zeros(code_length);

    // Simple repetition encoding for demonstration
    encoded[0] = pixel_value;
    for i in 1..code_length {
        encoded[i] = pixel_value; // Repetition code
    }

    Ok(encoded)
}

#[allow(dead_code)]
fn quantum_error_detect_correct<T>(
    encoded_pixel: Array1<T>,
    syndrome_generators: &[Array1<T>],
    _config: &QuantumConfig,
) -> NdimageResult<T>
where
    T: Float + FromPrimitive + Copy + 'static,
{
    // Calculate syndrome
    let mut syndrome = Vec::new();

    for generator in syndrome_generators {
        let syndrome_bit = encoded_pixel.dot(generator);
        syndrome.push(syndrome_bit);
    }

    // Simple majority vote correction
    let values: Vec<T> = encoded_pixel.to_vec();
    let corrected_value = majority_vote(&values)?;

    Ok(corrected_value)
}

#[allow(dead_code)]
fn majority_vote<T>(values: &[T]) -> NdimageResult<T>
where
    T: Float + FromPrimitive + Copy,
{
    if values.is_empty() {
        return Err(NdimageError::InvalidInput(
            "Empty _values for majority vote".to_string(),
        ));
    }

    // Simple average for continuous _values
    let sum = values.iter().fold(T::zero(), |acc, &x| acc + x);
    let average = sum / T::from_usize(values.len()).expect("Operation failed");

    Ok(average)
}

#[allow(dead_code)]
fn image_to_tensor_network<T>(
    image: &ArrayView2<T>,
    bond_dimension: usize,
    _config: &QuantumConfig,
) -> NdimageResult<Array3<T>>
where
    T: Float + FromPrimitive + Copy,
{
    let (height, width) = image.dim();

    // Create tensor network representation
    let mut tensor_network = Array3::zeros((height, width, bond_dimension));

    for y in 0..height {
        for x in 0..width {
            let pixel = image[(y, x)];

            // Decompose pixel into bond _dimension components
            for d in 0..bond_dimension {
                let component = pixel / T::from_usize(bond_dimension).expect("Operation failed");
                tensor_network[(y, x, d)] = component;
            }
        }
    }

    Ok(tensor_network)
}

#[allow(dead_code)]
fn apply_tensor_network_gates<T>(
    mut tensor_network: Array3<T>,
    config: &QuantumConfig,
) -> NdimageResult<Array3<T>>
where
    T: Float + FromPrimitive + Copy,
{
    let (height, width, bond_dim) = tensor_network.dim();

    // Apply quantum gates to tensor _network
    for y in 0..height {
        for x in 0..width {
            for d in 0..bond_dim {
                let current_value = tensor_network[(y, x, d)];

                // Apply rotation gate
                let angle =
                    T::from_f64(config.entanglement_strength * PI).expect("Operation failed");
                let rotated_value = current_value * angle.cos();

                tensor_network[(y, x, d)] = rotated_value;
            }
        }
    }

    Ok(tensor_network)
}

#[allow(dead_code)]
fn tensor_network_toimage<T>(
    tensor_network: Array3<T>,
    outputshape: (usize, usize),
) -> NdimageResult<Array2<T>>
where
    T: Float + FromPrimitive + Copy,
{
    let (height, width) = outputshape;
    let (_, _, bond_dim) = tensor_network.dim();
    let mut image = Array2::zeros((height, width));

    // Contract tensor _network back to image
    for y in 0..height {
        for x in 0..width {
            let mut pixel_value = T::zero();

            for d in 0..bond_dim {
                pixel_value = pixel_value + tensor_network[(y, x, d)];
            }

            image[(y, x)] = pixel_value;
        }
    }

    Ok(image)
}

#[allow(dead_code)]
fn initialize_variational_parameters<T>(_numlayers: usize) -> NdimageResult<Array1<T>>
where
    T: Float + FromPrimitive + Copy,
{
    let param_count = _numlayers * 3; // 3 parameters per layer
    let mut parameters = Array1::zeros(param_count);
    let mut rng = scirs2_core::random::rng();

    // Initialize with small random values
    for i in 0..param_count {
        let random_value = T::from_f64(rng.random_range(-0.05..0.05)).expect("Operation failed");
        parameters[i] = random_value;
    }

    Ok(parameters)
}

#[allow(dead_code)]
fn apply_variational_circuit<T>(
    image: &Array2<T>,
    parameters: &Array1<T>,
    _config: &QuantumConfig,
) -> NdimageResult<Array2<T>>
where
    T: Float + FromPrimitive + Copy,
{
    let (height, width) = image.dim();
    let mut result = image.clone();

    let num_layers = parameters.len() / 3;

    // Apply variational layers
    for layer in 0..num_layers {
        let theta = parameters[layer * 3];
        let phi = parameters[layer * 3 + 1];
        let lambda = parameters[layer * 3 + 2];

        // Apply parameterized quantum gates
        for y in 0..height {
            for x in 0..width {
                let pixel = result[(y, x)];

                // Variational quantum circuit
                let enhanced_pixel = pixel * theta.cos() * phi.sin() + lambda;
                result[(y, x)] = enhanced_pixel;
            }
        }
    }

    Ok(result)
}

#[allow(dead_code)]
fn calculate_enhancement_cost<T>(enhanced: &Array2<T>, original: &Array2<T>) -> NdimageResult<T>
where
    T: Float + FromPrimitive + Copy,
{
    let (height, width) = enhanced.dim();
    let mut cost = T::zero();

    // Calculate image quality cost function
    for y in 0..height {
        for x in 0..width {
            let diff = enhanced[(y, x)] - original[(y, x)];
            cost = cost + diff * diff;
        }
    }

    // Normalize cost
    cost = cost / T::from_usize(height * width).expect("Operation failed");

    Ok(cost)
}

#[allow(dead_code)]
fn calculate_quantum_gradients<T>(
    image: &Array2<T>,
    parameters: &Array1<T>,
    config: &QuantumConfig,
) -> NdimageResult<Array1<T>>
where
    T: Float + FromPrimitive + Copy,
{
    let mut gradients = Array1::zeros(parameters.len());
    let epsilon = T::from_f64(0.01).expect("Operation failed");

    // Calculate numerical gradients using parameter shift rule
    for i in 0..parameters.len() {
        let mut params_plus = parameters.clone();
        let mut params_minus = parameters.clone();

        params_plus[i] = params_plus[i] + epsilon;
        params_minus[i] = params_minus[i] - epsilon;

        let cost_plus = {
            let circuit_plus = apply_variational_circuit(image, &params_plus, config)?;
            calculate_enhancement_cost(&circuit_plus, image)?
        };

        let cost_minus = {
            let circuit_minus = apply_variational_circuit(image, &params_minus, config)?;
            calculate_enhancement_cost(&circuit_minus, image)?
        };

        let gradient =
            (cost_plus - cost_minus) / (T::from_f64(2.0).expect("Operation failed") * epsilon);
        gradients[i] = gradient;
    }

    Ok(gradients)
}

#[allow(dead_code)]
fn update_variational_parameters<T>(
    parameters: &mut Array1<T>,
    gradients: &Array1<T>,
    iteration: usize,
) -> NdimageResult<()>
where
    T: Float + FromPrimitive + Copy,
{
    let learning_rate =
        T::from_f64(0.01 / (1.0 + iteration as f64 * 0.001)).expect("Operation failed");

    // Update parameters using gradient descent
    for i in 0..parameters.len() {
        parameters[i] = parameters[i] - learning_rate * gradients[i];
    }

    Ok(())
}

#[cfg(test)]
mod tests {
    use super::*;
    use scirs2_core::ndarray::Array2;

    #[test]
    fn test_quantum_superposition_filter() {
        let image = Array2::from_shape_vec((4, 4), (0..16).map(|x| x as f64).collect())
            .expect("Operation failed");

        let filter1 = Array2::from_shape_vec((3, 3), vec![1.0; 9]).expect("Operation failed") / 9.0;
        let filter2 =
            Array2::from_shape_vec((3, 3), vec![-1.0, 0.0, 1.0, -1.0, 0.0, 1.0, -1.0, 0.0, 1.0])
                .expect("Failed to create quantum state");

        let config = QuantumConfig::default();
        let result = quantum_superposition_filter(image.view(), &[filter1, filter2], &config)
            .expect("Operation failed");

        assert_eq!(result.dim(), (4, 4));
        assert!(result.iter().all(|&x| x.is_finite()));
    }

    #[test]
    fn test_quantum_entanglement_correlation() {
        let image =
            Array2::from_shape_vec((3, 3), vec![1.0, 2.0, 1.0, 2.0, 5.0, 2.0, 1.0, 2.0, 1.0])
                .expect("Failed to create quantum state");

        let config = QuantumConfig::default();
        let result =
            quantum_entanglement_correlation(image.view(), &config).expect("Operation failed");

        assert_eq!(result.dim(), (3, 3));
        assert!(result.iter().all(|&x| x.is_finite()));
    }

    #[test]
    fn test_quantum_walk_edge_detection() {
        let image = Array2::from_shape_vec((5, 5), (0..25).map(|x| x as f64).collect())
            .expect("Operation failed");

        let config = QuantumConfig::default();
        let result =
            quantum_walk_edge_detection(image.view(), 10, &config).expect("Operation failed");

        assert_eq!(result.dim(), (5, 5));
        assert!(result.iter().all(|&x| x.is_finite()));
    }

    #[test]
    fn test_quantum_fourier_enhancement() {
        let image = Array2::from_shape_vec((4, 4), (0..16).map(|x| x as f64).collect())
            .expect("Operation failed");

        let config = QuantumConfig::default();
        let result = quantum_fourier_enhancement(image.view(), &config).expect("Operation failed");

        assert_eq!(result.dim(), (4, 4));
        assert!(result.iter().all(|x| x.re.is_finite() && x.im.is_finite()));
    }

    #[test]
    fn test_quantum_machine_learning_classifier() {
        let image =
            Array2::from_shape_vec((3, 3), vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0])
                .expect("Failed to create array");

        let training_data = vec![
            Array2::from_shape_vec((3, 3), vec![1.0; 9]).expect("Operation failed"),
            Array2::from_shape_vec((3, 3), vec![5.0; 9]).expect("Operation failed"),
        ];
        let labels = vec![0, 1];

        let config = QuantumConfig::default();
        let result =
            quantum_machine_learning_classifier(image.view(), &training_data, &labels, &config)
                .expect("Failed to create array");

        assert!(result.0 < 2); // Valid class
        assert!(result.1.is_finite()); // Valid probability
    }

    #[test]
    fn test_quantum_error_correction() {
        let noisyimage =
            Array2::from_shape_vec((3, 3), vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0])
                .expect("Failed to create array");

        let config = QuantumConfig::default();
        let result =
            quantum_error_correction(noisyimage.view(), 3, &config).expect("Operation failed");

        assert_eq!(result.dim(), (3, 3));
        assert!(result.iter().all(|&x| x.is_finite()));
    }

    #[test]
    fn test_quantum_tensor_network_processing() {
        let image =
            Array2::from_shape_vec((3, 3), vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0])
                .expect("Failed to create array");

        let config = QuantumConfig::default();
        let result =
            quantum_tensor_network_processing(image.view(), 2, &config).expect("Operation failed");

        assert_eq!(result.dim(), (3, 3));
        assert!(result.iter().all(|&x| x.is_finite()));
    }

    #[test]
    fn test_quantum_variational_enhancement() {
        let image =
            Array2::from_shape_vec((3, 3), vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0])
                .expect("Failed to create array");

        let config = QuantumConfig {
            iterations: 5, // Reduce iterations for testing
            ..Default::default()
        };

        let result =
            quantum_variational_enhancement(image.view(), 2, &config).expect("Operation failed");

        assert_eq!(result.dim(), (3, 3));
        assert!(result.iter().all(|&x| x.is_finite()));
    }

    #[test]
    fn test_quantum_config_default() {
        let config = QuantumConfig::default();

        assert_eq!(config.iterations, 100);
        assert_eq!(config.coherence_threshold, 0.8);
        assert_eq!(config.entanglement_strength, 0.5);
        assert_eq!(config.noise_level, 0.01);
        assert!(!config.use_quantum_acceleration);
    }

    #[test]
    fn test_quantumstate_representation() {
        let amplitudes = Array2::<Complex<f64>>::zeros((2, 2));
        let phases = Array2::<f64>::zeros((2, 2));
        let coherence = Array2::<Complex<f64>>::zeros((2, 2));

        let quantumstate = QuantumState {
            amplitudes,
            phases,
            coherence,
        };

        assert_eq!(quantumstate.amplitudes.dim(), (2, 2));
        assert_eq!(quantumstate.phases.dim(), (2, 2));
        assert_eq!(quantumstate.coherence.dim(), (2, 2));
    }
}