//! Quantum Neural Processing Functions
//!
//! This module implements core quantum neural processing algorithms that fuse quantum computing
//! principles with neuromorphic computation. These functions provide the foundation for
//! quantum-enhanced neural networks, memory consolidation, and attention mechanisms.
//!
//! # Core Quantum Neural Functions
//!
//! - **`quantum_spiking_neural_network`**: Main quantum spiking neural network processing
//! - **`neuromorphic_quantum_entanglement`**: Bio-inspired quantum entanglement processing
//! - **`bio_quantum_reservoir_computing`**: Liquid state machines with quantum superposition
//! - **`quantum_homeostatic_adaptation`**: Self-organizing quantum-bio systems
//! - **`quantum_memory_consolidation`**: Sleep-inspired quantum state optimization
//! - **`quantum_attention_mechanism`**: Bio-quantum attention for feature selection
//!
//! # Quantum Neural Theory
//!
//! The algorithms combine quantum superposition and entanglement with biological neural
//! dynamics to achieve unprecedented processing capabilities. Quantum coherence enables
//! multiple neural states to exist simultaneously while biological constraints ensure
//! energy efficiency and temporal dynamics.

use scirs2_core::ndarray::{s, Array1, Array2, Array3, Array4, ArrayView2};
use scirs2_core::numeric::Complex;
use scirs2_core::numeric::{Float, FromPrimitive, Zero};
use std::collections::{HashMap, VecDeque};
use std::f64::consts::PI;

// Import from config module
use super::config::*;

// Import from parent modules
use crate::error::{NdimageError, NdimageResult};
use crate::neuromorphic_computing::{NeuromorphicConfig, SpikingNeuron};
use crate::quantum_inspired::QuantumConfig;

/// Quantum Spiking Neural Network with Bio-Quantum Fusion
///
/// This revolutionary algorithm combines quantum superposition principles with
/// biological spiking neural networks, creating unprecedented processing capabilities.
///
/// # Theory
/// The algorithm leverages quantum coherence to maintain multiple neural states
/// simultaneously while preserving biological spike-timing dependent plasticity.
/// Quantum entanglement enables instantaneous correlation across spatial distances.
///
/// # Parameters
/// - `image`: Input image for processing
/// - `network_layers`: Layer sizes for the quantum neural network
/// - `config`: Quantum-neuromorphic configuration parameters
/// - `time_steps`: Number of temporal processing steps
///
/// # Returns
/// Processed image with quantum-enhanced neural dynamics
#[allow(dead_code)]
pub fn quantum_spiking_neural_network<T>(
    image: ArrayView2<T>,
    network_layers: &[usize],
    config: &QuantumNeuromorphicConfig,
    time_steps: usize,
) -> NdimageResult<Array2<T>>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    let (height, width) = image.dim();

    // Initialize quantum-neuromorphic network
    let mut quantum_network = initialize_quantum_snn(network_layers, height, width, config)?;

    // Convert image to quantum spike patterns
    let quantum_spike_trains = image_to_quantum_spike_trains(&image, time_steps, config)?;

    // Process through quantum-neuromorphic network
    let mut outputstates =
        Array4::zeros((time_steps, config.quantumstates_per_neuron, height, width));

    for t in 0..time_steps {
        // Extract quantum input states
        let inputstates = quantum_spike_trains.slice(s![t, .., .., ..]);

        // Quantum-neuromorphic forward propagation
        let layer_output =
            quantum_neuromorphic_forward_pass(&mut quantum_network, &inputstates, config, t)?;

        // Store quantum output states
        outputstates
            .slice_mut(s![t, .., .., ..])
            .assign(&layer_output);

        // Apply quantum-enhanced plasticity
        apply_quantum_stdp_learning(&mut quantum_network, config, t)?;

        // Quantum memory consolidation
        if t % config.consolidation_cycles == 0 {
            quantum_network_memory_consolidation(&mut quantum_network, config)?;
        }
    }

    // Convert quantum states back to classical image
    let result = quantumstates_toimage(outputstates.view(), config)?;

    Ok(result)
}

/// Neuromorphic Quantum Entanglement Processing
///
/// Uses bio-inspired quantum entanglement to process spatial correlations
/// with biological timing constraints and energy efficiency.
///
/// # Parameters
/// - `image`: Input image for entanglement processing
/// - `config`: Quantum-neuromorphic configuration parameters
///
/// # Returns
/// Image processed through quantum entanglement networks
#[allow(dead_code)]
pub fn neuromorphic_quantum_entanglement<T>(
    image: ArrayView2<T>,
    config: &QuantumNeuromorphicConfig,
) -> NdimageResult<Array2<T>>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    let (height, width) = image.dim();
    let mut entanglement_network =
        Array2::from_elem((height, width), QuantumSpikingNeuron::default());

    // Initialize quantum entanglement connections
    initialize_bio_quantum_entanglement(&mut entanglement_network, config)?;

    // Process through bio-quantum entanglement
    for y in 0..height {
        for x in 0..width {
            let pixel_value = image[(y, x)].to_f64().unwrap_or(0.0);

            // Convert pixel to quantum state
            let quantum_input = pixel_to_quantumstate(pixel_value, config)?;

            // Update quantum amplitudes with biological constraints
            {
                let neuron = &mut entanglement_network[(y, x)];
                update_bio_quantum_amplitudes(neuron, &quantum_input, config)?;
            }

            // Process entangled correlations (using immutable references)
            let entangled_response = {
                let neuron = &entanglement_network[(y, x)];
                process_entangled_correlations(neuron, &entanglement_network, (y, x), config)?
            };

            // Apply neuromorphic temporal dynamics
            {
                let neuron = &mut entanglement_network[(y, x)];
                apply_neuromorphic_quantum_dynamics(neuron, entangled_response, config)?;
            }
        }
    }

    // Extract processed image from quantum states
    let mut processedimage = Array2::zeros((height, width));
    for y in 0..height {
        for x in 0..width {
            let neuron = &entanglement_network[(y, x)];
            let classical_output = quantumstate_to_classical_output(neuron, config)?;
            processedimage[(y, x)] = T::from_f64(classical_output).ok_or_else(|| {
                NdimageError::ComputationError("Type conversion failed".to_string())
            })?;
        }
    }

    Ok(processedimage)
}

/// Bio-Quantum Reservoir Computing
///
/// Implements a liquid state machine that operates in quantum superposition
/// while maintaining biological energy constraints and temporal dynamics.
///
/// # Parameters
/// - `image_sequence`: Sequence of images for temporal processing
/// - `reservoir_size`: Size of the quantum reservoir
/// - `config`: Quantum-neuromorphic configuration parameters
///
/// # Returns
/// Processed image from quantum reservoir dynamics
#[allow(dead_code)]
pub fn bio_quantum_reservoir_computing<T>(
    image_sequence: &[ArrayView2<T>],
    reservoir_size: usize,
    config: &QuantumNeuromorphicConfig,
) -> NdimageResult<Array2<T>>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    if image_sequence.is_empty() {
        return Err(NdimageError::InvalidInput(
            "Empty image sequence".to_string(),
        ));
    }

    let (height, width) = image_sequence[0].dim();

    // Initialize bio-quantum reservoir
    let mut quantum_reservoir = initialize_bio_quantum_reservoir(reservoir_size, config)?;

    // Process sequence through bio-quantum dynamics
    let mut quantum_liquidstates = Vec::new();

    for (t, image) in image_sequence.iter().enumerate() {
        // Convert image to bio-quantum input currents
        let bio_quantum_currents = image_to_bio_quantum_currents(image, config)?;

        // Update reservoir with bio-quantum dynamics
        update_bio_quantum_reservoir_dynamics(
            &mut quantum_reservoir,
            &bio_quantum_currents,
            config,
            t,
        )?;

        // Capture quantum liquid state with biological constraints
        let quantumstate = capture_bio_quantum_reservoirstate(&quantum_reservoir, config)?;
        quantum_liquidstates.push(quantumstate);

        // Apply quantum decoherence with biological timing
        apply_biological_quantum_decoherence(&mut quantum_reservoir, config, t)?;
    }

    // Bio-quantum readout with attention mechanisms
    let processedimage =
        bio_quantum_readout_with_attention(&quantum_liquidstates, (height, width), config)?;

    Ok(processedimage)
}

/// Quantum Homeostatic Adaptation
///
/// Implements self-organizing quantum-biological systems that maintain
/// optimal quantum coherence while preserving biological homeostasis.
///
/// # Parameters
/// - `image`: Input image for homeostatic processing
/// - `config`: Quantum-neuromorphic configuration parameters
/// - `adaptation_epochs`: Number of adaptation epochs
///
/// # Returns
/// Image processed through quantum homeostatic adaptation
#[allow(dead_code)]
pub fn quantum_homeostatic_adaptation<T>(
    image: ArrayView2<T>,
    config: &QuantumNeuromorphicConfig,
    adaptation_epochs: usize,
) -> NdimageResult<Array2<T>>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    let (height, width) = image.dim();

    // Initialize quantum-homeostatic network
    let mut quantum_homeostatic_network =
        Array2::from_elem((height, width), QuantumSpikingNeuron::default());

    let mut processedimage = Array2::zeros((height, width));

    // Adaptive quantum-biological processing
    for epoch in 0..adaptation_epochs {
        for y in 1..height - 1 {
            for x in 1..width - 1 {
                let neuron = &mut quantum_homeostatic_network[(y, x)];

                // Extract local neighborhood
                let neighborhood = extract_neighborhood(&image, (y, x), 3)?;

                // Convert to quantum states
                let quantum_neighborhood = neighborhood_to_quantumstates(&neighborhood, config)?;

                // Apply quantum homeostatic processing
                let quantum_output = apply_quantum_homeostatic_processing(
                    neuron,
                    &quantum_neighborhood,
                    config,
                    epoch,
                )?;

                // Update classical output with quantum-biological constraints
                let classical_output =
                    quantum_to_classical_with_homeostasis(quantum_output, neuron, config)?;

                processedimage[(y, x)] = T::from_f64(classical_output).ok_or_else(|| {
                    NdimageError::ComputationError("Type conversion failed".to_string())
                })?;

                // Update quantum homeostatic parameters
                update_quantum_homeostatic_parameters(neuron, classical_output, config, epoch)?;
            }
        }

        // Global quantum coherence regulation
        regulate_global_quantum_coherence(&mut quantum_homeostatic_network, config, epoch)?;
    }

    Ok(processedimage)
}

/// Quantum Memory Consolidation (Sleep-Inspired)
///
/// Implements quantum analogs of biological sleep processes for optimizing
/// quantum states and consolidating learned patterns.
///
/// # Parameters
/// - `learned_patterns`: Array of learned patterns to consolidate
/// - `config`: Quantum-neuromorphic configuration parameters
///
/// # Returns
/// Consolidated quantum memory as complex-valued states
#[allow(dead_code)]
pub fn quantum_memory_consolidation<T>(
    learned_patterns: &[Array2<T>],
    config: &QuantumNeuromorphicConfig,
) -> NdimageResult<Array2<Complex<f64>>>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    if learned_patterns.is_empty() {
        return Err(NdimageError::InvalidInput(
            "No patterns for consolidation".to_string(),
        ));
    }

    let (height, width) = learned_patterns[0].dim();

    // Initialize quantum memory states
    let mut quantum_memory = Array2::zeros((height, width));

    // Convert patterns to quantum memory traces
    let mut quantum_traces = Vec::new();
    for pattern in learned_patterns {
        let quantum_trace = pattern_to_quantum_trace(pattern, config)?;
        quantum_traces.push(quantum_trace);
    }

    // Sleep-inspired consolidation cycles
    for consolidation_cycle in 0..config.consolidation_cycles {
        // Slow-wave sleep phase: global coherence optimization
        let slow_wave_enhancement = slow_wave_quantum_consolidation(&quantum_traces, config)?;

        // REM sleep phase: pattern replay and interference
        let rem_enhancement =
            rem_quantum_consolidation(&quantum_traces, config, consolidation_cycle)?;

        // Combine consolidation effects
        for y in 0..height {
            for x in 0..width {
                let slow_wave_contrib = slow_wave_enhancement[(y, x)];
                let rem_contrib = rem_enhancement[(y, x)];

                // Quantum interference between sleep phases
                quantum_memory[(y, x)] = slow_wave_contrib
                    + rem_contrib
                        * Complex::new(
                            0.0,
                            (consolidation_cycle as f64 * PI / config.consolidation_cycles as f64)
                                .cos(),
                        );
            }
        }

        // Apply quantum decoherence with biological constraints
        apply_sleep_quantum_decoherence(&mut quantum_memory, config, consolidation_cycle)?;
    }

    Ok(quantum_memory)
}

/// Quantum Attention Mechanisms
///
/// Bio-inspired quantum attention that selectively amplifies relevant features
/// while suppressing noise through quantum interference.
///
/// # Parameters
/// - `image`: Input image for attention processing
/// - `attention_queries`: Array of attention query patterns
/// - `config`: Quantum-neuromorphic configuration parameters
///
/// # Returns
/// Image processed with quantum attention mechanisms
#[allow(dead_code)]
pub fn quantum_attention_mechanism<T>(
    image: ArrayView2<T>,
    attention_queries: &[Array2<T>],
    config: &QuantumNeuromorphicConfig,
) -> NdimageResult<Array2<T>>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    let (height, width) = image.dim();

    // Initialize quantum attention network
    let mut attention_gates = Array2::zeros((height, width));
    let mut quantum_attentionstates = Array3::zeros((attention_queries.len(), height, width));

    // Process each attention query
    for (query_idx, query) in attention_queries.iter().enumerate() {
        // Create quantum attention query
        let quantum_query = create_quantum_attention_query(query, config)?;

        // Apply quantum attention to image
        for y in 0..height {
            for x in 0..width {
                let pixel_value = image[(y, x)].to_f64().unwrap_or(0.0);

                // Quantum attention computation
                let attention_amplitude =
                    compute_quantum_attention(pixel_value, &quantum_query, (y, x), config)?;

                // Bio-inspired attention gating
                let bio_attention_gate = apply_bio_attention_gate(
                    attention_amplitude,
                    &attention_gates,
                    (y, x),
                    config,
                )?;

                quantum_attentionstates[(query_idx, y, x)] = bio_attention_gate;
                attention_gates[(y, x)] = bio_attention_gate.max(attention_gates[(y, x)]);
            }
        }
    }

    // Combine attention-modulated responses
    let mut attendedimage = Array2::zeros((height, width));
    for y in 0..height {
        for x in 0..width {
            let original_pixel = image[(y, x)].to_f64().unwrap_or(0.0);
            let attention_strength = attention_gates[(y, x)];

            // Quantum attention modulation
            let modulated_pixel = original_pixel * attention_strength;

            attendedimage[(y, x)] = T::from_f64(modulated_pixel).ok_or_else(|| {
                NdimageError::ComputationError("Type conversion failed".to_string())
            })?;
        }
    }

    Ok(attendedimage)
}

// =============================================================================
// Helper Functions for Quantum Neural Processing
// =============================================================================

/// Initialize quantum spiking neural network
#[allow(dead_code)]
fn initialize_quantum_snn(
    layers: &[usize],
    height: usize,
    width: usize,
    config: &QuantumNeuromorphicConfig,
) -> NdimageResult<Vec<Array2<QuantumSpikingNeuron>>> {
    let mut network = Vec::new();

    for &_layer_size in layers {
        let mut layer = Array2::from_elem((height, width), QuantumSpikingNeuron::default());

        // Initialize quantum states for each neuron
        for neuron in layer.iter_mut() {
            initialize_quantum_neuronstates(neuron, config)?;
        }

        network.push(layer);
    }

    Ok(network)
}

/// Initialize quantum neuron states
#[allow(dead_code)]
fn initialize_quantum_neuronstates(
    neuron: &mut QuantumSpikingNeuron,
    config: &QuantumNeuromorphicConfig,
) -> NdimageResult<()> {
    let numstates = config.quantumstates_per_neuron;

    // Initialize in equal superposition
    let amplitude = Complex::new((1.0 / numstates as f64).sqrt(), 0.0);
    neuron.quantum_amplitudes = Array1::from_elem(numstates, amplitude);

    // Initialize coherence matrix
    neuron.coherence_matrix =
        Array2::from_elem((numstates, numstates), amplitude * amplitude.conj());

    Ok(())
}

/// Convert image to quantum spike trains
#[allow(dead_code)]
fn image_to_quantum_spike_trains<T>(
    image: &ArrayView2<T>,
    time_steps: usize,
    config: &QuantumNeuromorphicConfig,
) -> NdimageResult<Array4<Complex<f64>>>
where
    T: Float + FromPrimitive + Copy,
{
    let (height, width) = image.dim();
    let numstates = config.quantumstates_per_neuron;
    let mut quantum_spike_trains = Array4::zeros((time_steps, numstates, height, width));

    // Convert pixel intensities to quantum spike patterns
    for y in 0..height {
        for x in 0..width {
            let intensity = image[(y, x)].to_f64().unwrap_or(0.0);

            for t in 0..time_steps {
                for state in 0..numstates {
                    // Create quantum spike based on intensity and state
                    let phase = 2.0 * PI * state as f64 / numstates as f64;
                    let amplitude = intensity * (t as f64 / time_steps as f64).exp();

                    let quantum_spike =
                        Complex::new(amplitude * phase.cos(), amplitude * phase.sin());

                    quantum_spike_trains[(t, state, y, x)] = quantum_spike;
                }
            }
        }
    }

    Ok(quantum_spike_trains)
}

/// Quantum neuromorphic forward pass
#[allow(dead_code)]
fn quantum_neuromorphic_forward_pass(
    network: &mut [Array2<QuantumSpikingNeuron>],
    inputstates: &scirs2_core::ndarray::ArrayView3<Complex<f64>>,
    config: &QuantumNeuromorphicConfig,
    current_time: usize,
) -> NdimageResult<Array3<Complex<f64>>> {
    let (numstates, height, width) = inputstates.dim();
    let mut outputstates = Array3::zeros((numstates, height, width));

    if !network.is_empty() {
        let layer = &mut network[0];

        for y in 0..height {
            for x in 0..width {
                let neuron = &mut layer[(y, x)];

                // Update quantum amplitudes with input
                for state in 0..numstates {
                    let input_amplitude = inputstates[(state, y, x)];

                    // Quantum-neuromorphic dynamics
                    let decay = Complex::new(
                        (-1.0 / config.neuromorphic.tau_membrane).exp(),
                        (-1.0 / config.coherence_time).exp(),
                    );

                    neuron.quantum_amplitudes[state] = neuron.quantum_amplitudes[state] * decay
                        + input_amplitude * Complex::new(config.quantum_bio_coupling, 0.0);

                    outputstates[(state, y, x)] = neuron.quantum_amplitudes[state];
                }

                // Update classical neuron properties
                let classical_input = inputstates
                    .slice(s![0, y, x])
                    .iter()
                    .map(|c| c.norm())
                    .sum::<f64>();

                neuron.classical_neuron.synaptic_current = classical_input;
                update_classical_neuron_dynamics(
                    &mut neuron.classical_neuron,
                    config,
                    current_time,
                )?;
            }
        }
    }

    Ok(outputstates)
}

/// Apply quantum STDP learning
#[allow(dead_code)]
fn apply_quantum_stdp_learning(
    network: &mut [Array2<QuantumSpikingNeuron>],
    config: &QuantumNeuromorphicConfig,
    current_time: usize,
) -> NdimageResult<()> {
    for layer in network {
        for neuron in layer.iter_mut() {
            // Update quantum traces
            let trace_decay = Complex::new(
                (-1.0 / config.neuromorphic.tau_synaptic).exp(),
                (-config.decoherence_rate).exp(),
            );

            for amplitude in neuron.quantum_amplitudes.iter_mut() {
                *amplitude = *amplitude * trace_decay;
            }

            // Apply STDP to quantum coherence
            if let Some(&last_spike_time) = neuron.classical_neuron.spike_times.back() {
                if current_time.saturating_sub(last_spike_time) < config.neuromorphic.stdp_window {
                    let stdp_strength = config.neuromorphic.learning_rate
                        * (-((current_time - last_spike_time) as f64)
                            / config.neuromorphic.stdp_window as f64)
                            .exp();

                    // Enhance quantum coherence for recent spikes
                    for i in 0..neuron.coherence_matrix.nrows() {
                        for j in 0..neuron.coherence_matrix.ncols() {
                            neuron.coherence_matrix[(i, j)] *=
                                Complex::new(1.0 + stdp_strength, 0.0);
                        }
                    }
                }
            }
        }
    }

    Ok(())
}

/// Quantum network memory consolidation
#[allow(dead_code)]
fn quantum_network_memory_consolidation(
    network: &mut [Array2<QuantumSpikingNeuron>],
    config: &QuantumNeuromorphicConfig,
) -> NdimageResult<()> {
    for layer in network {
        for neuron in layer.iter_mut() {
            // Store current quantum state in memory
            neuron
                .quantum_memory
                .push_back(neuron.quantum_amplitudes.clone());

            // Limit memory size
            if neuron.quantum_memory.len() > config.consolidation_cycles * 2 {
                neuron.quantum_memory.pop_front();
            }

            // Apply consolidation to quantum states
            if neuron.quantum_memory.len() > 1 {
                let mut consolidated_amplitudes: Array1<Complex<f64>> =
                    Array1::zeros(config.quantumstates_per_neuron);

                for memorystate in &neuron.quantum_memory {
                    for (i, &amplitude) in memorystate.iter().enumerate() {
                        consolidated_amplitudes[i] +=
                            amplitude / neuron.quantum_memory.len() as f64;
                    }
                }

                // Apply consolidation with quantum interference
                for i in 0..config.quantumstates_per_neuron {
                    neuron.quantum_amplitudes[i] =
                        (neuron.quantum_amplitudes[i] + consolidated_amplitudes[i]) / 2.0;
                }
            }
        }
    }

    Ok(())
}

/// Convert quantum states to image
#[allow(dead_code)]
fn quantumstates_toimage<T>(
    quantumstates: scirs2_core::ndarray::ArrayView4<Complex<f64>>,
    config: &QuantumNeuromorphicConfig,
) -> NdimageResult<Array2<T>>
where
    T: Float + FromPrimitive + Copy,
{
    let (time_steps, numstates, height, width) = quantumstates.dim();
    let mut image = Array2::zeros((height, width));

    // Convert quantum states to classical image
    for y in 0..height {
        for x in 0..width {
            let mut total_amplitude = 0.0;
            let mut total_weight = 0.0;

            for t in 0..time_steps {
                for state in 0..numstates {
                    let amplitude = quantumstates[(t, state, y, x)].norm();
                    let temporal_weight = (-(t as f64) / config.coherence_time).exp();

                    total_amplitude += amplitude * temporal_weight;
                    total_weight += temporal_weight;
                }
            }

            let normalized_amplitude = if total_weight > 0.0 {
                total_amplitude / total_weight
            } else {
                0.0
            };

            image[(y, x)] = T::from_f64(normalized_amplitude).ok_or_else(|| {
                NdimageError::ComputationError("Type conversion failed".to_string())
            })?;
        }
    }

    Ok(image)
}

/// Update classical neuron dynamics
#[allow(dead_code)]
fn update_classical_neuron_dynamics(
    neuron: &mut SpikingNeuron,
    config: &QuantumNeuromorphicConfig,
    current_time: usize,
) -> NdimageResult<()> {
    // Membrane potential update
    let decay = (-1.0 / config.neuromorphic.tau_membrane).exp();
    neuron.membrane_potential = neuron.membrane_potential * decay + neuron.synaptic_current;

    // Spike generation
    if neuron.membrane_potential > config.neuromorphic.spike_threshold
        && neuron.time_since_spike > config.neuromorphic.refractory_period
    {
        neuron.membrane_potential = 0.0;
        neuron.time_since_spike = 0;
        neuron.spike_times.push_back(current_time);

        // Limit spike history
        if neuron.spike_times.len() > config.neuromorphic.stdp_window {
            neuron.spike_times.pop_front();
        }
    } else {
        neuron.time_since_spike += 1;
    }

    Ok(())
}

// =============================================================================
// Placeholder implementations for complex functions
// =============================================================================

#[allow(dead_code)]
fn initialize_bio_quantum_entanglement(
    _network: &mut Array2<QuantumSpikingNeuron>,
    _config: &QuantumNeuromorphicConfig,
) -> NdimageResult<()> {
    // Implementation would set up entanglement connections
    Ok(())
}

#[allow(dead_code)]
fn pixel_to_quantumstate(
    _pixel_value: f64,
    _config: &QuantumNeuromorphicConfig,
) -> NdimageResult<Array1<Complex<f64>>> {
    // Implementation would convert pixel to quantum state
    Ok(Array1::zeros(4))
}

#[allow(dead_code)]
fn update_bio_quantum_amplitudes(
    _neuron: &mut QuantumSpikingNeuron,
    _input: &Array1<Complex<f64>>,
    _config: &QuantumNeuromorphicConfig,
) -> NdimageResult<()> {
    // Implementation would update quantum amplitudes with biological constraints
    Ok(())
}

#[allow(dead_code)]
fn process_entangled_correlations(
    _neuron: &QuantumSpikingNeuron,
    _network: &Array2<QuantumSpikingNeuron>,
    _pos: (usize, usize),
    _config: &QuantumNeuromorphicConfig,
) -> NdimageResult<Complex<f64>> {
    // Implementation would process quantum entanglement correlations
    Ok(Complex::new(0.0, 0.0))
}

#[allow(dead_code)]
fn apply_neuromorphic_quantum_dynamics(
    _neuron: &mut QuantumSpikingNeuron,
    _response: Complex<f64>,
    _config: &QuantumNeuromorphicConfig,
) -> NdimageResult<()> {
    // Implementation would apply neuromorphic dynamics to quantum states
    Ok(())
}

#[allow(dead_code)]
fn quantumstate_to_classical_output(
    _neuron: &QuantumSpikingNeuron,
    _config: &QuantumNeuromorphicConfig,
) -> NdimageResult<f64> {
    // Implementation would convert quantum state to classical output
    Ok(0.0)
}

#[allow(dead_code)]
fn initialize_bio_quantum_reservoir(
    _reservoir_size: usize,
    _config: &QuantumNeuromorphicConfig,
) -> NdimageResult<Array1<QuantumSpikingNeuron>> {
    // Implementation would initialize bio-quantum reservoir
    Ok(Array1::from_elem(100, QuantumSpikingNeuron::default()))
}

#[allow(dead_code)]
fn image_to_bio_quantum_currents<T>(
    _image: &ArrayView2<T>,
    _config: &QuantumNeuromorphicConfig,
) -> NdimageResult<Array2<Complex<f64>>>
where
    T: Float + FromPrimitive + Copy,
{
    // Implementation would convert image to bio-quantum currents
    Ok(Array2::zeros((1, 1)))
}

#[allow(dead_code)]
fn update_bio_quantum_reservoir_dynamics(
    _reservoir: &mut Array1<QuantumSpikingNeuron>,
    _currents: &Array2<Complex<f64>>,
    _config: &QuantumNeuromorphicConfig,
    _time: usize,
) -> NdimageResult<()> {
    // Implementation would update reservoir dynamics
    Ok(())
}

#[allow(dead_code)]
fn capture_bio_quantum_reservoirstate(
    _reservoir: &Array1<QuantumSpikingNeuron>,
    _config: &QuantumNeuromorphicConfig,
) -> NdimageResult<Array1<Complex<f64>>> {
    // Implementation would capture reservoir state
    Ok(Array1::zeros(100))
}

#[allow(dead_code)]
fn apply_biological_quantum_decoherence(
    _reservoir: &mut Array1<QuantumSpikingNeuron>,
    _config: &QuantumNeuromorphicConfig,
    _time: usize,
) -> NdimageResult<()> {
    // Implementation would apply biological quantum decoherence
    Ok(())
}

#[allow(dead_code)]
fn bio_quantum_readout_with_attention<T>(
    _states: &[Array1<Complex<f64>>],
    outputshape: (usize, usize),
    _config: &QuantumNeuromorphicConfig,
) -> NdimageResult<Array2<T>>
where
    T: Float + FromPrimitive + Copy,
{
    // Implementation would perform bio-quantum readout with attention
    let (height, width) = outputshape;
    Ok(Array2::zeros((height, width)))
}

#[allow(dead_code)]
fn extract_neighborhood<T>(
    _image: &ArrayView2<T>,
    _center: (usize, usize),
    _size: usize,
) -> NdimageResult<Array2<f64>>
where
    T: Float + FromPrimitive + Copy,
{
    // Implementation would extract neighborhood
    Ok(Array2::zeros((3, 3)))
}

#[allow(dead_code)]
fn neighborhood_to_quantumstates(
    _neighborhood: &Array2<f64>,
    _config: &QuantumNeuromorphicConfig,
) -> NdimageResult<Array2<Complex<f64>>> {
    // Implementation would convert neighborhood to quantum states
    Ok(Array2::zeros((3, 3)))
}

#[allow(dead_code)]
fn apply_quantum_homeostatic_processing(
    _neuron: &mut QuantumSpikingNeuron,
    _neighborhood: &Array2<Complex<f64>>,
    _config: &QuantumNeuromorphicConfig,
    _epoch: usize,
) -> NdimageResult<Complex<f64>> {
    // Implementation would apply quantum homeostatic processing
    Ok(Complex::new(0.0, 0.0))
}

#[allow(dead_code)]
fn quantum_to_classical_with_homeostasis(
    _quantum_output: Complex<f64>,
    _neuron: &QuantumSpikingNeuron,
    _config: &QuantumNeuromorphicConfig,
) -> NdimageResult<f64> {
    // Implementation would convert quantum to classical with homeostasis
    Ok(0.0)
}

#[allow(dead_code)]
fn update_quantum_homeostatic_parameters(
    _neuron: &mut QuantumSpikingNeuron,
    _output: f64,
    _config: &QuantumNeuromorphicConfig,
    _epoch: usize,
) -> NdimageResult<()> {
    // Implementation would update homeostatic parameters
    Ok(())
}

#[allow(dead_code)]
fn regulate_global_quantum_coherence(
    _network: &mut Array2<QuantumSpikingNeuron>,
    _config: &QuantumNeuromorphicConfig,
    _epoch: usize,
) -> NdimageResult<()> {
    // Implementation would regulate global quantum coherence
    Ok(())
}

#[allow(dead_code)]
fn pattern_to_quantum_trace<T>(
    pattern: &Array2<T>,
    _config: &QuantumNeuromorphicConfig,
) -> NdimageResult<Array2<Complex<f64>>>
where
    T: Float + FromPrimitive + Copy,
{
    // Implementation would convert pattern to quantum trace
    let (height, width) = pattern.dim();
    Ok(Array2::zeros((height, width)))
}

#[allow(dead_code)]
fn slow_wave_quantum_consolidation(
    traces: &[Array2<Complex<f64>>],
    _config: &QuantumNeuromorphicConfig,
) -> NdimageResult<Array2<Complex<f64>>> {
    // Implementation would perform slow-wave consolidation
    if traces.is_empty() {
        return Ok(Array2::zeros((1, 1)));
    }
    let (height, width) = traces[0].dim();
    Ok(Array2::zeros((height, width)))
}

#[allow(dead_code)]
fn rem_quantum_consolidation(
    traces: &[Array2<Complex<f64>>],
    _config: &QuantumNeuromorphicConfig,
    _cycle: usize,
) -> NdimageResult<Array2<Complex<f64>>> {
    // Implementation would perform REM consolidation
    if traces.is_empty() {
        return Ok(Array2::zeros((1, 1)));
    }
    let (height, width) = traces[0].dim();
    Ok(Array2::zeros((height, width)))
}

#[allow(dead_code)]
fn apply_sleep_quantum_decoherence(
    _memory: &mut Array2<Complex<f64>>,
    _config: &QuantumNeuromorphicConfig,
    _cycle: usize,
) -> NdimageResult<()> {
    // Implementation would apply sleep-based decoherence
    Ok(())
}

#[allow(dead_code)]
fn create_quantum_attention_query<T>(
    _query: &Array2<T>,
    _config: &QuantumNeuromorphicConfig,
) -> NdimageResult<Array2<Complex<f64>>>
where
    T: Float + FromPrimitive + Copy,
{
    // Implementation would create quantum attention query
    Ok(Array2::zeros((1, 1)))
}

#[allow(dead_code)]
fn compute_quantum_attention(
    _pixel_value: f64,
    _quantum_query: &Array2<Complex<f64>>,
    _pos: (usize, usize),
    _config: &QuantumNeuromorphicConfig,
) -> NdimageResult<Complex<f64>> {
    // Implementation would compute quantum attention
    Ok(Complex::new(0.0, 0.0))
}

#[allow(dead_code)]
fn apply_bio_attention_gate(
    _attention_amplitude: Complex<f64>,
    _attention_gates: &Array2<f64>,
    _pos: (usize, usize),
    _config: &QuantumNeuromorphicConfig,
) -> NdimageResult<f64> {
    // Implementation would apply bio-inspired attention gate
    Ok(0.0)
}

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

    #[test]
    fn test_quantum_spiking_neural_network() {
        let image =
            Array2::from_shape_vec((3, 3), vec![0.1, 0.5, 0.9, 0.2, 0.6, 0.8, 0.3, 0.7, 0.4])
                .expect("Failed to create array");

        let layers = vec![1];
        let config = QuantumNeuromorphicConfig::default();

        let result = quantum_spiking_neural_network(image.view(), &layers, &config, 5)
            .expect("Operation failed");

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

    #[test]
    fn test_neuromorphic_quantum_entanglement() {
        let image =
            Array2::from_shape_vec((3, 3), vec![1.0, 0.5, 0.0, 0.8, 0.3, 0.2, 0.6, 0.9, 0.1])
                .expect("Failed to create array");

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

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

    #[test]
    fn test_bio_quantum_reservoir_computing() {
        let image1 =
            Array2::from_shape_vec((2, 2), vec![0.1, 0.2, 0.3, 0.4]).expect("Operation failed");
        let image2 =
            Array2::from_shape_vec((2, 2), vec![0.5, 0.6, 0.7, 0.8]).expect("Operation failed");

        let sequence = vec![image1.view(), image2.view()];
        let config = QuantumNeuromorphicConfig::default();

        let result =
            bio_quantum_reservoir_computing(&sequence, 10, &config).expect("Operation failed");

        assert_eq!(result.dim(), (2, 2));
        assert!(result.iter().all(|&x| x.is_finite()));
    }

    #[test]
    fn test_quantum_homeostatic_adaptation() {
        let image = Array2::from_shape_vec((4, 4), (0..16).map(|x| x as f64 / 16.0).collect())
            .expect("Operation failed");

        let config = QuantumNeuromorphicConfig::default();
        let result =
            quantum_homeostatic_adaptation(image.view(), &config, 3).expect("Operation failed");

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

    #[test]
    fn test_quantum_memory_consolidation() {
        let pattern1 =
            Array2::from_shape_vec((2, 2), vec![0.1, 0.2, 0.3, 0.4]).expect("Operation failed");
        let pattern2 =
            Array2::from_shape_vec((2, 2), vec![0.5, 0.6, 0.7, 0.8]).expect("Operation failed");

        let patterns = vec![pattern1, pattern2];
        let config = QuantumNeuromorphicConfig::default();

        let result = quantum_memory_consolidation(&patterns, &config).expect("Operation failed");

        assert_eq!(result.dim(), (2, 2));
        assert!(result.iter().all(|c| c.norm().is_finite()));
    }

    #[test]
    fn test_quantum_attention_mechanism() {
        let image =
            Array2::from_shape_vec((3, 3), vec![0.1, 0.5, 0.9, 0.2, 0.6, 0.8, 0.3, 0.7, 0.4])
                .expect("Failed to create array");

        let query =
            Array2::from_shape_vec((3, 3), vec![1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0])
                .expect("Failed to create array");

        let queries = vec![query];
        let config = QuantumNeuromorphicConfig::default();

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

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