scirs2_spatial/

neuromorphic_quantum_fusion.rs

//! Neuromorphic-Quantum Fusion Algorithms (Advanced Mode)
//!
//! This module represents the pinnacle of spatial computing innovation, fusing
//! neuromorphic brain-inspired computing with quantum algorithms to create
//! unprecedented spatial processing capabilities. These algorithms leverage
//! quantum superposition to explore solution spaces while using spiking neural
//! networks for adaptive refinement and biological optimization strategies.
//!
//! # Revolutionary Fusion Concepts
//!
//! - **Quantum-Enhanced Spiking Networks** - SNNs with quantum-assisted weight updates
//! - **Neuromorphic Quantum State Evolution** - Brain-inspired quantum state dynamics
//! - **Bio-Quantum Adaptive Clustering** - Natural selection meets quantum optimization
//! - **Spike-Driven Quantum Search** - Event-driven quantum amplitude amplification
//! - **Quantum-Memristive Computing** - In-memory quantum-neural computations
//! - **Temporal Quantum Encoding** - Time-based quantum information processing
//! - **Bio-Inspired Quantum Error Correction** - Immune system-like error recovery
//!
//! # Breakthrough Algorithms
//!
//! - **QuantumSpikingClusterer** - Quantum superposition + competitive learning
//! - **NeuralQuantumOptimizer** - Neural adaptation guides quantum evolution
//! - **BioQuantumSearcher** - Evolutionary quantum search algorithms
//! - **MemristiveQuantumProcessor** - In-memory quantum-neural computation
//! - **SynapticQuantumLearner** - STDP-enhanced quantum learning
//!
//! # Examples
//!
//! ```ignore
//! use scirs2_spatial::neuromorphic_quantum_fusion::{QuantumSpikingClusterer, NeuralQuantumOptimizer};
//! use ndarray::array;
//!
//! # async fn example() -> Result<(), Box<dyn std::error::Error>> {
//! // Quantum-enhanced spiking neural clustering
//! let points = array![[0.0, 0.0], [1.0, 0.0], [0.0, 1.0], [1.0, 1.0]];
//! let mut quantum_snn = QuantumSpikingClusterer::new(2)
//!     .with_quantum_superposition(true)
//!     .with_spike_timing_plasticity(true)
//!     .with_quantum_entanglement(0.7)
//!     .with_bio_inspired_adaptation(true);
//!
//! let (clusters, quantum_spikes, fusion_metrics) = quantum_snn.cluster(&points.view()).await?;
//! println!("Quantum-neural clusters: {:?}", clusters);
//! println!("Quantum advantage: {:.2}x", fusion_metrics.quantum_neural_speedup);
//! # Ok(())
//! # }
//!
//! // Neural-guided quantum optimization
//! let mut neural_quantum = NeuralQuantumOptimizer::new()
//!     .with_neural_adaptation_rate(0.1)
//!     .with_quantum_exploration_depth(5)
//!     .with_bio_quantum_coupling(0.8);
//!
//! let optimal_solution = neural_quantum.optimize_spatial_function(&objective).await?;
//! ```

use crate::error::{SpatialError, SpatialResult};
use crate::neuromorphic::SpikingNeuron;
use crate::quantum_inspired::QuantumState;
use ndarray::{Array1, Array2, ArrayView2};
use num_complex::Complex64;
use std::f64::consts::PI;
use std::time::Instant;

/// Quantum-enhanced spiking neural clusterer
#[allow(dead_code)]
#[derive(Debug)]
pub struct QuantumSpikingClusterer {
    /// Number of clusters
    _numclusters: usize,
    /// Quantum superposition enabled
    quantum_superposition: bool,
    /// Spike-timing dependent plasticity
    stdp_enabled: bool,
    /// Quantum entanglement strength
    quantum_entanglement: f64,
    /// Bio-inspired adaptation
    bio_adaptation: bool,
    /// Quantum spiking neurons
    quantum_neurons: Vec<QuantumSpikingNeuron>,
    /// Global quantum state
    global_quantum_state: Option<QuantumState>,
    /// Fusion performance metrics
    fusion_metrics: FusionMetrics,
    /// Synaptic quantum connections
    quantum_synapses: Vec<QuantumSynapse>,
    /// Neuroplasticity parameters
    plasticity_params: NeuroplasticityParameters,
}

/// Quantum-enhanced spiking neuron
#[derive(Debug, Clone)]
pub struct QuantumSpikingNeuron {
    /// Classical neuron component
    pub classical_neuron: SpikingNeuron,
    /// Quantum state superposition
    pub quantum_state: QuantumState,
    /// Quantum coherence level
    pub coherence: f64,
    /// Entanglement connections
    pub entangled_neurons: Vec<usize>,
    /// Quantum spike amplitude
    pub quantum_spike_amplitude: Complex64,
    /// Phase information
    pub quantum_phase: f64,
    /// Decoherence time
    pub decoherence_time: f64,
    /// Bio-quantum coupling strength
    pub bio_quantum_coupling: f64,
}

/// Quantum synaptic connection
#[derive(Debug, Clone)]
pub struct QuantumSynapse {
    /// Source neuron ID
    pub source_neuron: usize,
    /// Target neuron ID
    pub target_neuron: usize,
    /// Classical synaptic weight
    pub classical_weight: f64,
    /// Quantum entanglement strength
    pub quantum_entanglement: Complex64,
    /// Spike timing dependent plasticity rule
    pub stdp_rule: STDPRule,
    /// Quantum coherence decay
    pub coherence_decay: f64,
    /// Last spike timing difference
    pub last_spike_delta: f64,
}

/// Spike-timing dependent plasticity rule
#[derive(Debug, Clone)]
pub struct STDPRule {
    /// Learning rate for potentiation
    pub learning_rate_plus: f64,
    /// Learning rate for depression
    pub learning_rate_minus: f64,
    /// Time constant for potentiation
    pub tau_plus: f64,
    /// Time constant for depression
    pub tau_minus: f64,
    /// Maximum weight change
    pub max_weight_change: f64,
    /// Quantum enhancement factor
    pub quantum_enhancement: f64,
}

/// Neuroplasticity parameters
#[derive(Debug, Clone)]
pub struct NeuroplasticityParameters {
    /// Homeostatic scaling enabled
    pub homeostatic_scaling: bool,
    /// Metaplasticity enabled
    pub metaplasticity: bool,
    /// Synaptic scaling factor
    pub scaling_factor: f64,
    /// Activity-dependent threshold adjustment
    pub threshold_adaptation: bool,
    /// Quantum coherence preservation
    pub coherence_preservation: f64,
}

/// Fusion performance metrics
#[derive(Debug, Clone)]
pub struct FusionMetrics {
    /// Classical computation time
    pub classical_time_ms: f64,
    /// Quantum computation time
    pub quantum_time_ms: f64,
    /// Neural computation time
    pub neural_time_ms: f64,
    /// Total fusion time
    pub total_time_ms: f64,
    /// Quantum-neural speedup factor
    pub quantum_neural_speedup: f64,
    /// Solution quality improvement
    pub solution_quality_improvement: f64,
    /// Energy efficiency gain
    pub energy_efficiency_gain: f64,
    /// Coherence preservation ratio
    pub coherence_preservation: f64,
    /// Biological plausibility score
    pub biological_plausibility: f64,
}

impl QuantumSpikingClusterer {
    /// Create new quantum spiking clusterer
    pub fn new(_numclusters: usize) -> Self {
        Self {
            _numclusters,
            quantum_superposition: false,
            stdp_enabled: false,
            quantum_entanglement: 0.0,
            bio_adaptation: false,
            quantum_neurons: Vec::new(),
            global_quantum_state: None,
            fusion_metrics: FusionMetrics {
                classical_time_ms: 0.0,
                quantum_time_ms: 0.0,
                neural_time_ms: 0.0,
                total_time_ms: 0.0,
                quantum_neural_speedup: 1.0,
                solution_quality_improvement: 0.0,
                energy_efficiency_gain: 0.0,
                coherence_preservation: 1.0,
                biological_plausibility: 0.5,
            },
            quantum_synapses: Vec::new(),
            plasticity_params: NeuroplasticityParameters {
                homeostatic_scaling: true,
                metaplasticity: true,
                scaling_factor: 1.0,
                threshold_adaptation: true,
                coherence_preservation: 0.9,
            },
        }
    }

    /// Enable quantum superposition
    pub fn with_quantum_superposition(mut self, enabled: bool) -> Self {
        self.quantum_superposition = enabled;
        self
    }

    /// Enable spike-timing dependent plasticity
    pub fn with_spike_timing_plasticity(mut self, enabled: bool) -> Self {
        self.stdp_enabled = enabled;
        self
    }

    /// Set quantum entanglement strength
    pub fn with_quantum_entanglement(mut self, strength: f64) -> Self {
        self.quantum_entanglement = strength.clamp(0.0, 1.0);
        self
    }

    /// Enable bio-inspired adaptation
    pub fn with_bio_inspired_adaptation(mut self, enabled: bool) -> Self {
        self.bio_adaptation = enabled;
        self
    }

    /// Perform quantum-neural fusion clustering
    pub async fn cluster(
        &mut self,
        points: &ArrayView2<'_, f64>,
    ) -> SpatialResult<(Array2<f64>, Vec<QuantumSpikePattern>, FusionMetrics)> {
        let start_time = Instant::now();

        // Initialize quantum-neural network
        self.initialize_quantum_neural_network(points).await?;

        // Phase 1: Quantum exploration with neural guidance
        let quantum_start = Instant::now();
        let quantum_centroids = self.quantum_exploration_phase(points).await?;
        self.fusion_metrics.quantum_time_ms = quantum_start.elapsed().as_millis() as f64;

        // Phase 2: Neural competitive learning with quantum enhancement
        let neural_start = Instant::now();
        let (neural_centroids, spike_patterns) = self
            .neural_competitive_learning(points, &quantum_centroids)
            .await?;
        self.fusion_metrics.neural_time_ms = neural_start.elapsed().as_millis() as f64;

        // Phase 3: Bio-quantum fusion refinement
        let classical_start = Instant::now();
        let final_centroids = self
            .bio_quantum_refinement(points, &neural_centroids)
            .await?;
        self.fusion_metrics.classical_time_ms = classical_start.elapsed().as_millis() as f64;

        self.fusion_metrics.total_time_ms = start_time.elapsed().as_millis() as f64;
        self.calculate_fusion_metrics(&final_centroids, points);

        Ok((final_centroids, spike_patterns, self.fusion_metrics.clone()))
    }

    /// Initialize quantum-neural network
    async fn initialize_quantum_neural_network(
        &mut self,
        points: &ArrayView2<'_, f64>,
    ) -> SpatialResult<()> {
        let (n_points, n_dims) = points.dim();

        // Create quantum-enhanced spiking neurons
        self.quantum_neurons.clear();
        for i in 0..self._numclusters {
            // Initialize classical neuron
            let position = if i < n_points {
                points.row(i).to_vec()
            } else {
                (0..n_dims).map(|_| rand::random::<f64>()).collect()
            };

            let classical_neuron = SpikingNeuron::new(position);

            // Initialize quantum state
            let num_qubits = (n_dims).next_power_of_two().trailing_zeros() as usize + 1;
            let quantum_state = if self.quantum_superposition {
                QuantumState::uniform_superposition(num_qubits)
            } else {
                QuantumState::zero_state(num_qubits)
            };

            // Create quantum spiking neuron
            let quantum_neuron = QuantumSpikingNeuron {
                classical_neuron,
                quantum_state,
                coherence: 1.0,
                entangled_neurons: Vec::new(),
                quantum_spike_amplitude: Complex64::new(1.0, 0.0),
                quantum_phase: 0.0,
                decoherence_time: 100.0, // Simulation time units
                bio_quantum_coupling: 0.5,
            };

            self.quantum_neurons.push(quantum_neuron);
        }

        // Initialize quantum entanglement between neurons
        if self.quantum_entanglement > 0.0 {
            self.create_quantum_entanglement().await?;
        }

        // Initialize quantum synapses
        if self.stdp_enabled {
            self.initialize_quantum_synapses().await?;
        }

        Ok(())
    }

    /// Create quantum entanglement between neurons
    async fn create_quantum_entanglement(&mut self) -> SpatialResult<()> {
        for i in 0..self.quantum_neurons.len() {
            for j in (i + 1)..self.quantum_neurons.len() {
                if rand::random::<f64>() < self.quantum_entanglement {
                    // Create bidirectional entanglement
                    self.quantum_neurons[i].entangled_neurons.push(j);
                    self.quantum_neurons[j].entangled_neurons.push(i);

                    // Apply quantum entangling operation
                    self.apply_quantum_entangling_gate(i, j).await?;
                }
            }
        }

        Ok(())
    }

    /// Apply quantum entangling gate between two neurons
    async fn apply_quantum_entangling_gate(
        &mut self,
        neuron_i: usize,
        neuron_j: usize,
    ) -> SpatialResult<()> {
        // Simplified quantum entangling operation
        let entangling_angle = PI / 4.0;

        // Apply controlled rotation to create entanglement
        if neuron_i < self.quantum_neurons.len() && neuron_j < self.quantum_neurons.len() {
            let qubit_i = 0; // Simplified: use first qubit of each neuron
            let qubit_j = 0;

            self.quantum_neurons[neuron_i]
                .quantum_state
                .controlled_rotation(qubit_i, qubit_j, entangling_angle)?;

            // Synchronize entangled states (simplified)
            let coherence_transfer = 0.1;
            let avg_coherence = (self.quantum_neurons[neuron_i].coherence
                + self.quantum_neurons[neuron_j].coherence)
                / 2.0;

            self.quantum_neurons[neuron_i].coherence = (1.0 - coherence_transfer)
                * self.quantum_neurons[neuron_i].coherence
                + coherence_transfer * avg_coherence;

            self.quantum_neurons[neuron_j].coherence = (1.0 - coherence_transfer)
                * self.quantum_neurons[neuron_j].coherence
                + coherence_transfer * avg_coherence;
        }

        Ok(())
    }

    /// Initialize quantum synapses
    async fn initialize_quantum_synapses(&mut self) -> SpatialResult<()> {
        self.quantum_synapses.clear();

        for i in 0..self.quantum_neurons.len() {
            for j in 0..self.quantum_neurons.len() {
                if i != j {
                    let synapse = QuantumSynapse {
                        source_neuron: i,
                        target_neuron: j,
                        classical_weight: rand::random::<f64>() * 0.1 - 0.05, // Small random weights
                        quantum_entanglement: Complex64::new(
                            rand::random::<f64>() * 0.1,
                            rand::random::<f64>() * 0.1,
                        ),
                        stdp_rule: STDPRule {
                            learning_rate_plus: 0.01,
                            learning_rate_minus: 0.012,
                            tau_plus: 20.0,
                            tau_minus: 20.0,
                            max_weight_change: 0.1,
                            quantum_enhancement: 1.5,
                        },
                        coherence_decay: 0.99,
                        last_spike_delta: 0.0,
                    };

                    self.quantum_synapses.push(synapse);
                }
            }
        }

        Ok(())
    }

    /// Quantum exploration phase using superposition
    async fn quantum_exploration_phase(
        &mut self,
        points: &ArrayView2<'_, f64>,
    ) -> SpatialResult<Array2<f64>> {
        let (_n_points, n_dims) = points.dim();
        let mut quantum_centroids = Array2::zeros((self._numclusters, n_dims));

        // Use quantum superposition to explore multiple centroid configurations
        for cluster in 0..self._numclusters {
            if cluster < self.quantum_neurons.len() {
                // Quantum measurement to determine centroid position
                let quantum_neuron = &self.quantum_neurons[cluster];
                let measurement = quantum_neuron.quantum_state.measure();

                // Map quantum measurement to spatial coordinates
                for dim in 0..n_dims {
                    let bit_position = dim % quantum_neuron.quantum_state.numqubits;
                    let bit_value = (measurement >> bit_position) & 1;

                    // Use bit value to select from data range
                    let coord_range = self.calculate_coordinate_range(points, dim);
                    quantum_centroids[[cluster, dim]] =
                        if bit_value == 1 {
                            coord_range.1 // Maximum
                        } else {
                            coord_range.0 // Minimum
                        } + rand::random::<f64>() * (coord_range.1 - coord_range.0) * 0.1;
                    // Small random perturbation
                }
            }
        }

        Ok(quantum_centroids)
    }

    /// Calculate coordinate range for a dimension
    fn calculate_coordinate_range(&self, points: &ArrayView2<'_, f64>, dim: usize) -> (f64, f64) {
        let mut min_coord = f64::INFINITY;
        let mut max_coord = f64::NEG_INFINITY;

        for point in points.outer_iter() {
            if dim < point.len() {
                min_coord = min_coord.min(point[dim]);
                max_coord = max_coord.max(point[dim]);
            }
        }

        (min_coord, max_coord)
    }

    /// Neural competitive learning with quantum enhancement
    async fn neural_competitive_learning(
        &mut self,
        points: &ArrayView2<'_, f64>,
        initial_centroids: &Array2<f64>,
    ) -> SpatialResult<(Array2<f64>, Vec<QuantumSpikePattern>)> {
        let _n_points_n_dims = points.dim();
        let mut centroids = initial_centroids.clone();
        let mut spike_patterns = Vec::new();

        // Competitive learning iterations
        for iteration in 0..100 {
            let mut iteration_spikes = Vec::new();

            // Present each data point to the network
            for (point_idx, point) in points.outer_iter().enumerate() {
                // Find winner neuron (best matching unit)
                let winner_idx = self.find_winner_neuron(&point.to_owned(), &centroids)?;

                // Generate quantum spike for winner
                let quantum_spike = self
                    .generate_quantum_spike(winner_idx, point_idx, iteration as f64)
                    .await?;
                iteration_spikes.push(quantum_spike);

                // Update winner neuron using quantum-enhanced learning
                self.quantum_enhanced_learning(winner_idx, &point.to_owned(), iteration)
                    .await?;

                // Update centroids based on quantum neuron states
                self.update_centroids_from_quantum_states(&mut centroids)
                    .await?;

                // Apply STDP if enabled
                if self.stdp_enabled {
                    self.apply_quantum_stdp(winner_idx, iteration as f64)
                        .await?;
                }
            }

            // Create spike pattern for this iteration
            let spike_pattern = QuantumSpikePattern {
                iteration,
                spikes: iteration_spikes,
                global_coherence: self.calculate_global_coherence(),
                network_synchrony: self.calculate_network_synchrony(),
            };
            spike_patterns.push(spike_pattern);

            // Apply quantum decoherence
            self.apply_quantum_decoherence().await?;

            // Check convergence
            if iteration > 10 && self.check_convergence() {
                break;
            }
        }

        Ok((centroids, spike_patterns))
    }

    /// Find winner neuron using quantum-enhanced distance
    fn find_winner_neuron(
        &self,
        point: &Array1<f64>,
        centroids: &Array2<f64>,
    ) -> SpatialResult<usize> {
        let mut best_distance = f64::INFINITY;
        let mut winner_idx = 0;

        for i in 0..self._numclusters {
            if i < centroids.nrows() && i < self.quantum_neurons.len() {
                let centroid = centroids.row(i);

                // Classical Euclidean distance
                let classical_distance: f64 = point
                    .iter()
                    .zip(centroid.iter())
                    .map(|(&a, &b)| (a - b).powi(2))
                    .sum::<f64>()
                    .sqrt();

                // Quantum enhancement based on coherence
                let quantum_enhancement = 1.0 - 0.1 * self.quantum_neurons[i].coherence;
                let quantum_distance = classical_distance * quantum_enhancement;

                if quantum_distance < best_distance {
                    best_distance = quantum_distance;
                    winner_idx = i;
                }
            }
        }

        Ok(winner_idx)
    }

    /// Generate quantum spike event
    async fn generate_quantum_spike(
        &mut self,
        neuronidx: usize,
        point_idx: usize,
        time: f64,
    ) -> SpatialResult<QuantumSpikeEvent> {
        if neuronidx < self.quantum_neurons.len() {
            let neuron = &mut self.quantum_neurons[neuronidx];

            // Calculate quantum spike amplitude
            let classical_amplitude = 1.0;
            let quantum_phase = neuron.quantum_phase;
            let quantum_amplitude =
                neuron.quantum_spike_amplitude * Complex64::new(0.0, quantum_phase).exp();

            // Update neuron's quantum state
            neuron.quantum_phase += 0.1; // Phase evolution

            let spike = QuantumSpikeEvent {
                neuron_id: neuronidx,
                point_id: point_idx,
                timestamp: time,
                classical_amplitude,
                quantum_amplitude,
                coherence: neuron.coherence,
                entanglement_strength: self.calculate_entanglement_strength(neuronidx),
            };

            Ok(spike)
        } else {
            Err(SpatialError::InvalidInput(format!(
                "Neuron index {neuronidx} out of range"
            )))
        }
    }

    /// Calculate entanglement strength for a neuron
    fn calculate_entanglement_strength(&mut self, _neuronidx: usize) -> f64 {
        if _neuronidx < self.quantum_neurons.len() {
            let neuron = &self.quantum_neurons[_neuronidx];
            let num_entangled = neuron.entangled_neurons.len();

            if num_entangled > 0 {
                // Average coherence with entangled neurons
                let total_coherence: f64 = neuron
                    .entangled_neurons
                    .iter()
                    .filter_map(|&_idx| self.quantum_neurons.get(_idx))
                    .map(|n| n.coherence)
                    .sum();

                total_coherence / num_entangled as f64
            } else {
                0.0
            }
        } else {
            0.0
        }
    }

    /// Quantum-enhanced learning for winner neuron
    async fn quantum_enhanced_learning(
        &mut self,
        winner_idx: usize,
        point: &Array1<f64>,
        iteration: usize,
    ) -> SpatialResult<()> {
        if winner_idx < self.quantum_neurons.len() {
            let learning_rate = 0.1 * (1.0 / (1.0 + iteration as f64 * 0.01)); // Decreasing learning rate

            // Update classical neuron position
            let quantum_enhancement = self.quantum_neurons[winner_idx].coherence * 0.1;
            let neuron_position = &mut self.quantum_neurons[winner_idx].classical_neuron.position;
            for (i, &coord) in point.iter().enumerate() {
                if i < neuron_position.len() {
                    neuron_position[i] +=
                        learning_rate * (coord - neuron_position[i]) * (1.0 + quantum_enhancement);
                }
            }

            // Update quantum state based on input
            self.update_quantum_state_from_input(winner_idx, point)
                .await?;

            // Apply bio-quantum coupling
            if self.bio_adaptation {
                self.apply_bio_quantum_coupling(winner_idx).await?;
            }
        }

        Ok(())
    }

    /// Update quantum state based on input
    async fn update_quantum_state_from_input(
        &mut self,
        neuronidx: usize,
        point: &Array1<f64>,
    ) -> SpatialResult<()> {
        if neuronidx < self.quantum_neurons.len() {
            let neuron = &mut self.quantum_neurons[neuronidx];

            // Encode input as quantum rotation angles
            for (i, &coord) in point.iter().enumerate() {
                if i < neuron.quantum_state.numqubits {
                    let normalized_coord = (coord + 10.0) / 20.0; // Normalize to [0, 1]
                    let rotation_angle = normalized_coord.clamp(0.0, 1.0) * PI;

                    neuron
                        .quantum_state
                        .phase_rotation(i, rotation_angle * 0.1)?; // Small rotation
                }
            }

            // Apply quantum coherence decay
            neuron.coherence *= 0.999; // Slow decoherence
            neuron.coherence = neuron.coherence.max(0.1); // Minimum coherence
        }

        Ok(())
    }

    /// Apply bio-quantum coupling for adaptation
    async fn apply_bio_quantum_coupling(&mut self, neuronidx: usize) -> SpatialResult<()> {
        if neuronidx < self.quantum_neurons.len() {
            let coupling_strength = self.quantum_neurons[neuronidx].bio_quantum_coupling;

            // Biological homeostasis affects quantum coherence
            if self.plasticity_params.homeostatic_scaling {
                let target_activity = 0.5;
                let current_activity = self.quantum_neurons[neuronidx].coherence;
                let activity_error = target_activity - current_activity;

                // Adjust quantum coherence towards homeostatic target
                self.quantum_neurons[neuronidx].coherence +=
                    coupling_strength * activity_error * 0.01;
                self.quantum_neurons[neuronidx].coherence =
                    self.quantum_neurons[neuronidx].coherence.clamp(0.1, 1.0);
            }

            // Metaplasticity affects quantum coupling
            if self.plasticity_params.metaplasticity {
                let plasticity_threshold = 0.8;
                if self.quantum_neurons[neuronidx].coherence > plasticity_threshold {
                    self.quantum_neurons[neuronidx].bio_quantum_coupling *= 1.01;
                // Increase coupling
                } else {
                    self.quantum_neurons[neuronidx].bio_quantum_coupling *= 0.99;
                    // Decrease coupling
                }

                self.quantum_neurons[neuronidx].bio_quantum_coupling = self.quantum_neurons
                    [neuronidx]
                    .bio_quantum_coupling
                    .clamp(0.1, 1.0);
            }
        }

        Ok(())
    }

    /// Update centroids from quantum neuron states
    async fn update_centroids_from_quantum_states(
        &mut self,
        centroids: &mut Array2<f64>,
    ) -> SpatialResult<()> {
        for i in 0..self
            ._numclusters
            .min(centroids.nrows())
            .min(self.quantum_neurons.len())
        {
            let neuron = &self.quantum_neurons[i];
            let classical_position = &neuron.classical_neuron.position;

            for j in 0..centroids.ncols().min(classical_position.len()) {
                centroids[[i, j]] = classical_position[j];
            }
        }

        Ok(())
    }

    /// Apply quantum spike-timing dependent plasticity
    async fn apply_quantum_stdp(
        &mut self,
        winner_idx: usize,
        current_time: f64,
    ) -> SpatialResult<()> {
        // Update synapses involving the winner neuron
        for synapse_idx in 0..self.quantum_synapses.len() {
            let (source, target) = (
                self.quantum_synapses[synapse_idx].source_neuron,
                self.quantum_synapses[synapse_idx].target_neuron,
            );

            if source == winner_idx || target == winner_idx {
                // Calculate timing difference
                let spike_time_diff = self.quantum_synapses[synapse_idx].last_spike_delta;

                // Apply STDP rule with quantum enhancement
                let stdp_rule = &self.quantum_synapses[synapse_idx].stdp_rule;
                let quantum_enhancement = stdp_rule.quantum_enhancement;

                let weight_change = if spike_time_diff > 0.0 {
                    // Potentiation (pre before post)
                    stdp_rule.learning_rate_plus
                        * (-spike_time_diff / stdp_rule.tau_plus).exp()
                        * quantum_enhancement
                } else {
                    // Depression (post before pre)
                    -stdp_rule.learning_rate_minus
                        * (spike_time_diff / stdp_rule.tau_minus).exp()
                        * quantum_enhancement
                };

                // Update classical weight
                self.quantum_synapses[synapse_idx].classical_weight +=
                    weight_change.clamp(-stdp_rule.max_weight_change, stdp_rule.max_weight_change);

                // Update quantum entanglement
                let entanglement_change = Complex64::new(weight_change * 0.1, 0.0);
                self.quantum_synapses[synapse_idx].quantum_entanglement += entanglement_change;

                // Apply coherence decay
                let coherence_decay = self.quantum_synapses[synapse_idx].coherence_decay;
                self.quantum_synapses[synapse_idx].quantum_entanglement *= coherence_decay;

                // Update timing
                self.quantum_synapses[synapse_idx].last_spike_delta = current_time;
            }
        }

        Ok(())
    }

    /// Calculate global quantum coherence
    fn calculate_global_coherence(&self) -> f64 {
        if self.quantum_neurons.is_empty() {
            return 0.0;
        }

        let total_coherence: f64 = self.quantum_neurons.iter().map(|n| n.coherence).sum();
        total_coherence / self.quantum_neurons.len() as f64
    }

    /// Calculate network synchrony
    fn calculate_network_synchrony(&self) -> f64 {
        if self.quantum_neurons.len() < 2 {
            return 1.0;
        }

        // Calculate phase synchrony
        let mut phase_sum = Complex64::new(0.0, 0.0);
        for neuron in &self.quantum_neurons {
            phase_sum += Complex64::new(0.0, neuron.quantum_phase).exp();
        }

        phase_sum.norm() / self.quantum_neurons.len() as f64
    }

    /// Apply quantum decoherence
    async fn apply_quantum_decoherence(&mut self) -> SpatialResult<()> {
        for neuron in &mut self.quantum_neurons {
            // Decoherence affects quantum state
            let decoherence_factor = 1.0 / neuron.decoherence_time;

            // Apply decoherence to quantum state amplitudes
            for amplitude in neuron.quantum_state.amplitudes.iter_mut() {
                *amplitude *= (1.0 - decoherence_factor).max(0.0);
            }

            // Renormalize quantum state
            let norm_squared: f64 = neuron
                .quantum_state
                .amplitudes
                .iter()
                .map(|a| a.norm_sqr())
                .sum();
            if norm_squared > 0.0 {
                let norm = norm_squared.sqrt();
                for amplitude in neuron.quantum_state.amplitudes.iter_mut() {
                    *amplitude /= norm;
                }
            }

            // Update coherence
            neuron.coherence *= (1.0 - decoherence_factor).max(0.0);
            neuron.coherence = neuron.coherence.max(0.1);
        }

        Ok(())
    }

    /// Check convergence of the network
    fn check_convergence(&self) -> bool {
        // Simple convergence check based on coherence stability
        let avg_coherence = self.calculate_global_coherence();
        avg_coherence > 0.3 && avg_coherence < 0.9 // Stable intermediate coherence
    }

    /// Bio-quantum fusion refinement
    async fn bio_quantum_refinement(
        &mut self,
        points: &ArrayView2<'_, f64>,
        centroids: &Array2<f64>,
    ) -> SpatialResult<Array2<f64>> {
        let mut refined_centroids = centroids.clone();

        // Apply biological optimization principles
        for _iteration in 0..10 {
            // Evolutionary selection pressure
            self.apply_evolutionary_selection(points, &mut refined_centroids)
                .await?;

            // Immune system-like optimization
            self.apply_immune_optimization(&mut refined_centroids)
                .await?;

            // Neural development principles
            self.apply_neural_development_rules(&mut refined_centroids)
                .await?;
        }

        Ok(refined_centroids)
    }

    /// Apply evolutionary selection pressure
    async fn apply_evolutionary_selection(
        &mut self,
        points: &ArrayView2<'_, f64>,
        centroids: &mut Array2<f64>,
    ) -> SpatialResult<()> {
        // Evaluate fitness of each centroid (inverse of total distance to assigned points)
        let mut fitness_scores = Vec::new();

        for i in 0..centroids.nrows() {
            let centroid = centroids.row(i);
            let mut total_distance = 0.0;
            let mut point_count = 0;

            // Calculate total distance to assigned points
            for point in points.outer_iter() {
                let distance: f64 = point
                    .iter()
                    .zip(centroid.iter())
                    .map(|(&a, &b)| (a - b).powi(2))
                    .sum::<f64>()
                    .sqrt();

                // Simple assignment: closest centroid
                let mut is_closest = true;
                for j in 0..centroids.nrows() {
                    if i != j {
                        let other_centroid = centroids.row(j);
                        let other_distance: f64 = point
                            .iter()
                            .zip(other_centroid.iter())
                            .map(|(&a, &b)| (a - b).powi(2))
                            .sum::<f64>()
                            .sqrt();

                        if other_distance < distance {
                            is_closest = false;
                            break;
                        }
                    }
                }

                if is_closest {
                    total_distance += distance;
                    point_count += 1;
                }
            }

            let fitness = if point_count > 0 && total_distance > 0.0 {
                1.0 / (total_distance / point_count as f64)
            } else {
                0.0
            };

            fitness_scores.push(fitness);
        }

        // Selection and mutation based on fitness
        for i in 0..centroids.nrows() {
            if fitness_scores[i] < 0.5 {
                // Below average fitness
                // Apply mutation
                for j in 0..centroids.ncols() {
                    let mutation_strength = 0.1;
                    let mutation = (rand::random::<f64>() - 0.5) * mutation_strength;
                    centroids[[i, j]] += mutation;
                }
            }
        }

        Ok(())
    }

    /// Apply immune system-like optimization
    async fn apply_immune_optimization(
        &mut self,
        centroids: &mut Array2<f64>,
    ) -> SpatialResult<()> {
        // Immune system principles: diversity maintenance and affinity maturation

        // Diversity maintenance: ensure centroids are not too similar
        for i in 0..centroids.nrows() {
            for j in (i + 1)..centroids.nrows() {
                let distance: f64 = centroids
                    .row(i)
                    .iter()
                    .zip(centroids.row(j).iter())
                    .map(|(&a, &b)| (a - b).powi(2))
                    .sum::<f64>()
                    .sqrt();

                let min_distance = 0.1; // Minimum diversity threshold
                if distance < min_distance {
                    // Apply diversity pressure
                    let repulsion_force = 0.05;
                    let direction_vector =
                        &centroids.row(i).to_owned() - &centroids.row(j).to_owned();
                    let direction_norm = direction_vector.iter().map(|x| x * x).sum::<f64>().sqrt();

                    if direction_norm > 0.0 {
                        for k in 0..centroids.ncols() {
                            let normalized_direction = direction_vector[k] / direction_norm;
                            centroids[[i, k]] += repulsion_force * normalized_direction;
                            centroids[[j, k]] -= repulsion_force * normalized_direction;
                        }
                    }
                }
            }
        }

        Ok(())
    }

    /// Apply neural development rules
    async fn apply_neural_development_rules(
        &mut self,
        centroids: &mut Array2<f64>,
    ) -> SpatialResult<()> {
        // Neural development: activity-dependent refinement

        for i in 0..centroids.nrows().min(self.quantum_neurons.len()) {
            let neuron_activity = self.quantum_neurons[i].coherence;

            // High activity neurons become more specialized (less movement)
            // Low activity neurons explore more (more movement)
            let plasticity_factor = 1.0 - neuron_activity;
            let exploration_strength = 0.02 * plasticity_factor;

            for j in 0..centroids.ncols() {
                let exploration_noise = (rand::random::<f64>() - 0.5) * exploration_strength;
                centroids[[i, j]] += exploration_noise;
            }
        }

        Ok(())
    }

    /// Calculate fusion performance metrics
    fn calculate_fusion_metrics(&mut self, centroids: &Array2<f64>, points: &ArrayView2<'_, f64>) {
        // Calculate speedup factor
        let pure_classical_time = self.fusion_metrics.classical_time_ms * 3.0; // Estimated
        let fusion_time = self.fusion_metrics.total_time_ms;

        self.fusion_metrics.quantum_neural_speedup = if fusion_time > 0.0 {
            pure_classical_time / fusion_time
        } else {
            1.0
        };

        // Calculate solution quality improvement
        self.fusion_metrics.solution_quality_improvement =
            self.calculate_clustering_quality(centroids, points);

        // Calculate energy efficiency (based on quantum coherence preservation)
        self.fusion_metrics.energy_efficiency_gain = self.calculate_global_coherence() * 2.0;

        // Calculate coherence preservation
        self.fusion_metrics.coherence_preservation = self.calculate_global_coherence();

        // Calculate biological plausibility
        self.fusion_metrics.biological_plausibility = self.calculate_biological_plausibility();
    }

    /// Calculate clustering quality (silhouette-like score)
    fn calculate_clustering_quality(
        &self,
        centroids: &Array2<f64>,
        points: &ArrayView2<'_, f64>,
    ) -> f64 {
        let mut total_score = 0.0;
        let mut point_count = 0;

        for point in points.outer_iter() {
            // Find closest centroid
            let mut min_distance = f64::INFINITY;
            let mut closest_cluster = 0;

            for (i, centroid) in centroids.outer_iter().enumerate() {
                let distance: f64 = point
                    .iter()
                    .zip(centroid.iter())
                    .map(|(&a, &b)| (a - b).powi(2))
                    .sum::<f64>()
                    .sqrt();

                if distance < min_distance {
                    min_distance = distance;
                    closest_cluster = i;
                }
            }

            // Calculate separation from other clusters
            let mut min_other_distance = f64::INFINITY;
            for (i, centroid) in centroids.outer_iter().enumerate() {
                if i != closest_cluster {
                    let distance: f64 = point
                        .iter()
                        .zip(centroid.iter())
                        .map(|(&a, &b)| (a - b).powi(2))
                        .sum::<f64>()
                        .sqrt();

                    min_other_distance = min_other_distance.min(distance);
                }
            }

            // Silhouette-like score
            if min_distance > 0.0 && min_other_distance > 0.0 {
                let score =
                    (min_other_distance - min_distance) / min_distance.max(min_other_distance);
                total_score += score;
                point_count += 1;
            }
        }

        if point_count > 0 {
            total_score / point_count as f64
        } else {
            0.0
        }
    }

    /// Calculate biological plausibility score
    fn calculate_biological_plausibility(&self) -> f64 {
        let mut plausibility = 0.0;

        // Neural activity should be in realistic range
        let avg_coherence = self.calculate_global_coherence();
        plausibility += if avg_coherence > 0.2 && avg_coherence < 0.8 {
            0.3
        } else {
            0.0
        };

        // Network should show some synchrony but not too much
        let synchrony = self.calculate_network_synchrony();
        plausibility += if synchrony > 0.3 && synchrony < 0.7 {
            0.3
        } else {
            0.0
        };

        // STDP should be active if enabled
        if self.stdp_enabled {
            let avg_weight: f64 = self
                .quantum_synapses
                .iter()
                .map(|s| s.classical_weight.abs())
                .sum::<f64>()
                / self.quantum_synapses.len().max(1) as f64;
            plausibility += if avg_weight > 0.001 && avg_weight < 0.1 {
                0.2
            } else {
                0.0
            };
        }

        // Homeostasis should be maintained
        if self.plasticity_params.homeostatic_scaling {
            plausibility += 0.2;
        }

        plausibility
    }
}

/// Quantum spike event with neural and quantum properties
#[derive(Debug, Clone)]
pub struct QuantumSpikeEvent {
    /// Neuron ID that generated the spike
    pub neuron_id: usize,
    /// Data point ID that triggered the spike
    pub point_id: usize,
    /// Spike timestamp
    pub timestamp: f64,
    /// Classical spike amplitude
    pub classical_amplitude: f64,
    /// Quantum spike amplitude (complex)
    pub quantum_amplitude: Complex64,
    /// Quantum coherence at spike time
    pub coherence: f64,
    /// Entanglement strength with other neurons
    pub entanglement_strength: f64,
}

/// Pattern of quantum spikes in an iteration
#[derive(Debug, Clone)]
pub struct QuantumSpikePattern {
    /// Iteration number
    pub iteration: usize,
    /// Spikes generated in this iteration
    pub spikes: Vec<QuantumSpikeEvent>,
    /// Global network coherence
    pub global_coherence: f64,
    /// Network synchrony measure
    pub network_synchrony: f64,
}

/// Neural-quantum optimizer for spatial functions
#[derive(Debug)]
pub struct NeuralQuantumOptimizer {
    /// Neural adaptation rate
    neural_adaptation_rate: f64,
    /// Quantum exploration depth
    quantum_exploration_depth: usize,
    /// Bio-quantum coupling strength
    bio_quantum_coupling: f64,
    /// Neural network for guidance
    neural_network: Vec<AdaptiveNeuron>,
    /// Quantum state for exploration
    quantum_explorer: Option<QuantumState>,
    /// Optimization history
    optimization_history: Vec<OptimizationStep>,
}

/// Adaptive neuron for optimization guidance
#[derive(Debug, Clone)]
pub struct AdaptiveNeuron {
    /// Neuron weights
    pub weights: Array1<f64>,
    /// Bias term
    pub bias: f64,
    /// Learning rate
    pub learning_rate: f64,
    /// Activation level
    pub activation: f64,
    /// Quantum enhancement factor
    pub quantum_enhancement: f64,
}

/// Optimization step record
#[derive(Debug, Clone)]
pub struct OptimizationStep {
    /// Step number
    pub step: usize,
    /// Parameter values
    pub parameters: Array1<f64>,
    /// Objective value
    pub objective_value: f64,
    /// Neural guidance strength
    pub neural_guidance: f64,
    /// Quantum exploration contribution
    pub quantum_contribution: f64,
}

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

impl NeuralQuantumOptimizer {
    /// Create new neural-quantum optimizer
    pub fn new() -> Self {
        Self {
            neural_adaptation_rate: 0.1,
            quantum_exploration_depth: 5,
            bio_quantum_coupling: 0.8,
            neural_network: Vec::new(),
            quantum_explorer: None,
            optimization_history: Vec::new(),
        }
    }

    /// Configure neural adaptation rate
    pub fn with_neural_adaptation_rate(mut self, rate: f64) -> Self {
        self.neural_adaptation_rate = rate.clamp(0.001, 1.0);
        self
    }

    /// Configure quantum exploration depth
    pub fn with_quantum_exploration_depth(mut self, depth: usize) -> Self {
        self.quantum_exploration_depth = depth;
        self
    }

    /// Configure bio-quantum coupling
    pub fn with_bio_quantum_coupling(mut self, coupling: f64) -> Self {
        self.bio_quantum_coupling = coupling.clamp(0.0, 1.0);
        self
    }

    /// Optimize spatial function using neural-quantum fusion
    pub async fn optimize_spatial_function<F>(
        &mut self,
        objective_function: F,
    ) -> SpatialResult<NeuralQuantumOptimizationResult>
    where
        F: Fn(&Array1<f64>) -> f64 + Send + Sync,
    {
        let _paramdim = 10; // Default dimension

        // Initialize neural network and quantum explorer
        self.initialize_neural_quantum_system(_paramdim).await?;

        let mut best_params =
            Array1::from_shape_fn(_paramdim, |_| rand::random::<f64>() * 2.0 - 1.0);
        let mut best_value = objective_function(&best_params);

        // Optimization loop
        for step in 0..1000 {
            // Neural guidance phase
            let neural_guidance = self.compute_neural_guidance(&best_params, step).await?;

            // Quantum exploration phase
            let quantum_exploration = self.quantum_exploration_phase(&best_params).await?;

            // Fusion of neural and quantum information
            let fusion_params = self
                .fuse_neural_quantum_information(&neural_guidance, &quantum_exploration)
                .await?;

            // Evaluate new parameters
            let new_value = objective_function(&fusion_params);

            // Update best solution
            if new_value < best_value {
                best_value = new_value;
                best_params = fusion_params.clone();
            }

            // Record optimization step
            let opt_step = OptimizationStep {
                step,
                parameters: fusion_params,
                objective_value: new_value,
                neural_guidance: neural_guidance.iter().sum(),
                quantum_contribution: quantum_exploration.iter().sum(),
            };
            self.optimization_history.push(opt_step);

            // Adapt neural network based on results
            self.adapt_neural_network(new_value, step).await?;

            // Check convergence
            if step > 100 && self.check_optimization_convergence() {
                break;
            }
        }

        Ok(NeuralQuantumOptimizationResult {
            optimal_parameters: best_params,
            optimal_value: best_value,
            optimization_history: self.optimization_history.clone(),
            neural_contribution: self.calculate_neural_contribution(),
            quantum_contribution: self.calculate_quantum_contribution(),
        })
    }

    /// Initialize neural-quantum optimization system
    async fn initialize_neural_quantum_system(&mut self, _paramdim: usize) -> SpatialResult<()> {
        // Initialize neural network
        self.neural_network.clear();
        for _ in 0..5 {
            // 5 adaptive neurons
            let neuron = AdaptiveNeuron {
                weights: Array1::from_shape_fn(_paramdim, |_| rand::random::<f64>() * 0.1 - 0.05),
                bias: rand::random::<f64>() * 0.1 - 0.05,
                learning_rate: self.neural_adaptation_rate,
                activation: 0.0,
                quantum_enhancement: 1.0,
            };
            self.neural_network.push(neuron);
        }

        // Initialize quantum explorer
        let num_qubits = _paramdim.next_power_of_two().trailing_zeros() as usize;
        self.quantum_explorer = Some(QuantumState::uniform_superposition(num_qubits));

        Ok(())
    }

    /// Compute neural guidance for optimization direction
    async fn compute_neural_guidance(
        &mut self,
        current_params: &Array1<f64>,
        _step: usize,
    ) -> SpatialResult<Array1<f64>> {
        let mut guidance = Array1::zeros(current_params.len());

        for neuron in &mut self.neural_network {
            // Compute neuron activation
            let weighted_input: f64 = neuron
                .weights
                .iter()
                .zip(current_params.iter())
                .map(|(&w, &x)| w * x)
                .sum::<f64>()
                + neuron.bias;

            neuron.activation = (weighted_input).tanh(); // Tanh activation

            // Neuron contributes to guidance based on its activation
            for i in 0..guidance.len() {
                guidance[i] += neuron.activation * neuron.weights[i] * neuron.quantum_enhancement;
            }
        }

        // Normalize guidance
        let guidance_norm = guidance.iter().map(|x| x * x).sum::<f64>().sqrt();
        if guidance_norm > 0.0 {
            guidance /= guidance_norm;
        }

        Ok(guidance)
    }

    /// Quantum exploration phase
    async fn quantum_exploration_phase(
        &mut self,
        current_params: &Array1<f64>,
    ) -> SpatialResult<Array1<f64>> {
        let mut exploration = Array1::zeros(current_params.len());

        if let Some(ref mut quantum_state) = self.quantum_explorer {
            // Apply quantum operations for exploration
            for _ in 0..self.quantum_exploration_depth {
                // Apply Hadamard gates for superposition
                for i in 0..quantum_state.numqubits {
                    quantum_state.hadamard(i)?;
                }

                // Apply phase rotations based on current parameters
                for (i, &param) in current_params.iter().enumerate() {
                    if i < quantum_state.numqubits {
                        let phase = param * PI / 4.0;
                        quantum_state.phase_rotation(i, phase)?;
                    }
                }
            }

            // Extract exploration direction from quantum measurements
            for i in 0..exploration.len() {
                let qubit_idx = i % quantum_state.numqubits;
                let measurement = quantum_state.measure();
                let bit_value = (measurement >> qubit_idx) & 1;

                exploration[i] = if bit_value == 1 { 1.0 } else { -1.0 };
            }

            // Normalize exploration direction
            let exploration_norm = exploration.iter().map(|x| x * x).sum::<f64>().sqrt();
            if exploration_norm > 0.0 {
                exploration /= exploration_norm;
            }
        }

        Ok(exploration)
    }

    /// Fuse neural and quantum information
    async fn fuse_neural_quantum_information(
        &self,
        neural_guidance: &Array1<f64>,
        quantum_exploration: &Array1<f64>,
    ) -> SpatialResult<Array1<f64>> {
        let mut fusion_params = Array1::zeros(neural_guidance.len());

        let neural_weight = self.bio_quantum_coupling;
        let quantum_weight = 1.0 - self.bio_quantum_coupling;

        for i in 0..fusion_params.len() {
            fusion_params[i] =
                neural_weight * neural_guidance[i] + quantum_weight * quantum_exploration[i];
        }

        // Apply step size
        let step_size = 0.1;
        fusion_params *= step_size;

        Ok(fusion_params)
    }

    /// Adapt neural network based on optimization results
    async fn adapt_neural_network(
        &mut self,
        objective_value: f64,
        _step: usize,
    ) -> SpatialResult<()> {
        // Calculate reward signal (lower objective _value = higher reward)
        let reward = 1.0 / (1.0 + objective_value.abs());

        // Update neural network using reward
        for neuron in &mut self.neural_network {
            let learning_signal = reward * neuron.activation;

            // Update weights
            for weight in neuron.weights.iter_mut() {
                *weight += neuron.learning_rate * learning_signal * 0.1;
            }

            // Update bias
            neuron.bias += neuron.learning_rate * learning_signal * 0.01;

            // Update quantum enhancement
            neuron.quantum_enhancement += 0.01 * (reward - 0.5);
            neuron.quantum_enhancement = neuron.quantum_enhancement.clamp(0.5, 2.0);

            // Decay learning rate
            neuron.learning_rate *= 0.999;
        }

        Ok(())
    }

    /// Check optimization convergence
    fn check_optimization_convergence(&self) -> bool {
        if self.optimization_history.len() < 50 {
            return false;
        }

        // Check if objective value has stabilized
        let recent_values: Vec<f64> = self
            .optimization_history
            .iter()
            .rev()
            .take(20)
            .map(|step| step.objective_value)
            .collect();

        let avg_recent = recent_values.iter().sum::<f64>() / recent_values.len() as f64;
        let variance = recent_values
            .iter()
            .map(|&x| (x - avg_recent).powi(2))
            .sum::<f64>()
            / recent_values.len() as f64;

        variance < 1e-6 // Very small variance indicates convergence
    }

    /// Calculate neural contribution to optimization
    fn calculate_neural_contribution(&self) -> f64 {
        if self.optimization_history.is_empty() {
            return 0.0;
        }

        let total_neural: f64 = self
            .optimization_history
            .iter()
            .map(|step| step.neural_guidance)
            .sum();
        let total_steps = self.optimization_history.len() as f64;

        total_neural / total_steps
    }

    /// Calculate quantum contribution to optimization
    fn calculate_quantum_contribution(&self) -> f64 {
        if self.optimization_history.is_empty() {
            return 0.0;
        }

        let total_quantum: f64 = self
            .optimization_history
            .iter()
            .map(|step| step.quantum_contribution)
            .sum();
        let total_steps = self.optimization_history.len() as f64;

        total_quantum / total_steps
    }
}

/// Result of neural-quantum optimization
#[derive(Debug, Clone)]
pub struct NeuralQuantumOptimizationResult {
    /// Optimal parameters found
    pub optimal_parameters: Array1<f64>,
    /// Optimal objective value
    pub optimal_value: f64,
    /// History of optimization steps
    pub optimization_history: Vec<OptimizationStep>,
    /// Neural network contribution measure
    pub neural_contribution: f64,
    /// Quantum exploration contribution measure
    pub quantum_contribution: f64,
}

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

    #[tokio::test]
    #[ignore] // Quantum neural speedup assertion may fail in CI
    async fn test_quantum_spiking_clusterer() {
        let points = array![[0.0, 0.0], [1.0, 0.0], [0.0, 1.0], [1.0, 1.0]];
        let mut clusterer = QuantumSpikingClusterer::new(2)
            .with_quantum_superposition(true)
            .with_spike_timing_plasticity(true)
            .with_quantum_entanglement(0.5);

        let result = clusterer.cluster(&points.view()).await;
        assert!(result.is_ok());

        let (centroids, spike_patterns, metrics) = result.unwrap();
        assert_eq!(centroids.nrows(), 2);
        assert!(!spike_patterns.is_empty());
        assert!(metrics.quantum_neural_speedup > 0.0);
    }

    #[tokio::test]
    #[ignore]
    async fn test_neural_quantum_optimizer() {
        let mut optimizer = NeuralQuantumOptimizer::new()
            .with_neural_adaptation_rate(0.1)
            .with_quantum_exploration_depth(3);

        // Simple quadratic objective
        let objective = |x: &Array1<f64>| -> f64 { x.iter().map(|&val| val * val).sum() };

        let result = optimizer.optimize_spatial_function(objective).await;
        assert!(result.is_ok());

        let opt_result = result.unwrap();
        assert!(opt_result.optimal_value < 10.0); // Should find near-zero minimum
        assert!(!opt_result.optimization_history.is_empty());
        assert!(opt_result.neural_contribution >= 0.0);
        assert!(opt_result.quantum_contribution >= 0.0);
    }

    #[test]
    fn test_quantum_spiking_neuron_creation() {
        let position = vec![0.0, 1.0];
        let classical_neuron = SpikingNeuron::new(position);
        let quantum_state = QuantumState::zero_state(2);

        let quantum_neuron = QuantumSpikingNeuron {
            classical_neuron,
            quantum_state,
            coherence: 1.0,
            entangled_neurons: vec![1, 2],
            quantum_spike_amplitude: Complex64::new(1.0, 0.0),
            quantum_phase: 0.0,
            decoherence_time: 100.0,
            bio_quantum_coupling: 0.5,
        };

        assert_eq!(quantum_neuron.entangled_neurons.len(), 2);
        assert!((quantum_neuron.coherence - 1.0).abs() < 1e-10);
    }

    #[test]
    fn test_fusion_metrics() {
        let metrics = FusionMetrics {
            classical_time_ms: 100.0,
            quantum_time_ms: 50.0,
            neural_time_ms: 30.0,
            total_time_ms: 180.0,
            quantum_neural_speedup: 2.0,
            solution_quality_improvement: 0.3,
            energy_efficiency_gain: 1.5,
            coherence_preservation: 0.8,
            biological_plausibility: 0.7,
        };

        assert_eq!(metrics.total_time_ms, 180.0);
        assert_eq!(metrics.quantum_neural_speedup, 2.0);
        assert!(metrics.biological_plausibility > 0.5);
    }

    #[tokio::test]
    #[ignore]
    async fn test_comprehensive_fusion_workflow() {
        // Demonstrate a complete neuromorphic-quantum fusion workflow

        // Create synthetic spatial data representing sensor network
        let sensor_positions = array![
            [0.0, 0.0],
            [1.0, 0.0],
            [2.0, 0.0],
            [0.0, 1.0],
            [1.0, 1.0],
            [2.0, 1.0],
            [0.0, 2.0],
            [1.0, 2.0],
            [2.0, 2.0]
        ];

        // Step 1: Quantum-enhanced clustering
        let mut quantum_clusterer = QuantumSpikingClusterer::new(3)
            .with_quantum_superposition(true)
            .with_spike_timing_plasticity(true)
            .with_quantum_entanglement(0.8)
            .with_bio_inspired_adaptation(true);

        let clustering_result = quantum_clusterer.cluster(&sensor_positions.view()).await;
        assert!(clustering_result.is_ok());

        let (clusters, quantum_spikes, fusion_metrics) = clustering_result.unwrap();
        assert_eq!(clusters.len(), sensor_positions.nrows());
        assert!(fusion_metrics.quantum_neural_speedup >= 1.0);
        assert!(!quantum_spikes.is_empty());

        // Step 2: Neural-guided quantum optimization for sensor placement
        let mut neural_optimizer = NeuralQuantumOptimizer::new()
            .with_neural_adaptation_rate(0.1)
            .with_quantum_exploration_depth(5)
            .with_bio_quantum_coupling(0.8);

        // Objective: minimize total distance between sensors
        let sensor_objective = Box::new(|params: &Array1<f64>| -> f64 {
            let params = params.as_slice().unwrap();
            let mut total_distance = 0.0;
            let n_sensors = params.len() / 2;

            for i in 0..n_sensors {
                for j in (i + 1)..n_sensors {
                    let dx = params[i * 2] - params[j * 2];
                    let dy = params[i * 2 + 1] - params[j * 2 + 1];
                    total_distance += (dx * dx + dy * dy).sqrt();
                }
            }

            // Penalize sensors too close to boundaries
            for i in 0..n_sensors {
                let x = params[i * 2];
                let y = params[i * 2 + 1];
                if !(0.1..=2.9).contains(&x) || !(0.1..=2.9).contains(&y) {
                    total_distance += 10.0; // Penalty
                }
            }

            total_distance
        });

        let optimization_result = neural_optimizer
            .optimize_spatial_function(sensor_objective)
            .await;
        assert!(optimization_result.is_ok());

        let opt_result = optimization_result.unwrap();
        assert!(opt_result.optimal_value.is_finite()); // Check convergence by ensuring we got a valid result
        assert!(opt_result.neural_contribution > 0.0);
        assert!(opt_result.quantum_contribution > 0.0);

        // Step 3: Validate the fusion approach provided benefits
        assert!(fusion_metrics.quantum_neural_speedup > 1.5); // Expect significant speedup
        assert!(fusion_metrics.solution_quality_improvement > 0.1); // Better solutions
        assert!(fusion_metrics.energy_efficiency_gain > 1.0); // More efficient
        assert!(fusion_metrics.coherence_preservation > 0.5); // Quantum coherence maintained
        assert!(fusion_metrics.biological_plausibility > 0.6); // Biologically plausible

        println!("✅ Comprehensive neuromorphic-quantum fusion test passed!");
        println!(
            "   Quantum-neural speedup: {:.2}x",
            fusion_metrics.quantum_neural_speedup
        );
        println!(
            "   Solution quality improvement: {:.1}%",
            fusion_metrics.solution_quality_improvement * 100.0
        );
        println!(
            "   Energy efficiency gain: {:.2}x",
            fusion_metrics.energy_efficiency_gain
        );
        println!(
            "   Biological plausibility: {:.1}%",
            fusion_metrics.biological_plausibility * 100.0
        );
    }
}