scirs2_datasets/

quantum_neuromorphic_fusion.rs

//! Quantum-Neuromorphic Fusion Engine
//!
//! This module represents the cutting-edge fusion of quantum computing and neuromorphic
//! processing, creating hybrid bio-quantum systems for advanced dataset processing.
//! It combines quantum entanglement, superposition, and interference with spiking neural
//! networks, synaptic plasticity, and biological learning mechanisms.

use crate::error::{DatasetsError, Result};
use crate::neuromorphic_data_processor::NeuromorphicProcessor;
use crate::quantum_enhanced_generators::QuantumDatasetGenerator;
use crate::utils::Dataset;
use scirs2_core::ndarray::{s, Array1, Array2, Array3};
use scirs2_core::random::prelude::*;
use scirs2_core::random::rand_distributions::Uniform;
use statrs::statistics::Statistics;
use std::f64::consts::PI;
use std::time::{Duration, Instant};

/// Quantum-Neuromorphic Fusion Processor
/// The ultimate synthesis of quantum computing and biological neural networks
#[derive(Debug, Clone)]
#[allow(dead_code)]
pub struct QuantumNeuromorphicFusion {
    /// Quantum subsystem for quantum computational advantages
    quantum_engine: QuantumDatasetGenerator,
    /// Neuromorphic subsystem for bio-inspired processing
    neuromorphic_engine: NeuromorphicProcessor,
    /// Quantum-biological coupling strength (0.0 to 1.0)
    quantum_bio_coupling: f64,
    /// Coherence-plasticity entanglement factor
    coherence_plasticity_factor: f64,
    /// Quantum decoherence time affecting synaptic dynamics
    quantum_decoherence_time: Duration,
    /// Bio-quantum learning rate adaptation
    adaptive_learning_rate: f64,
    /// Enable quantum advantage in neural processing
    quantum_neural_advantage: bool,
}

/// Quantum-enhanced synaptic state
#[derive(Debug, Clone)]
#[allow(dead_code)]
struct QuantumSynapse {
    /// Classical synaptic weight
    classical_weight: f64,
    /// Quantum superposition amplitudes (real, imaginary)
    quantum_amplitudes: (f64, f64),
    /// Quantum phase for interference effects
    quantum_phase: f64,
    /// Entanglement partner synapse index
    entangled_partner: Option<usize>,
    /// Quantum coherence time remaining
    coherence_time: Duration,
    /// Bio-quantum coupling strength
    coupling_strength: f64,
}

/// Quantum-biological neuron with quantum-enhanced dynamics
#[derive(Debug, Clone)]
struct QuantumNeuron {
    /// Classical membrane potential
    membrane_potential: f64,
    /// Quantum state amplitudes |0⟩ and |1⟩
    quantum_state: (f64, f64),
    /// Quantum phase evolution
    phase_evolution: f64,
    /// Biological spike threshold (adaptive)
    spike_threshold: f64,
    /// Last spike time for STDP
    last_spike_time: Option<Instant>,
    /// Quantum decoherence rate
    decoherence_rate: f64,
    /// Entanglement connections to other neurons
    entanglement_map: Vec<usize>,
}

/// Fusion processing results combining quantum and biological intelligence
#[derive(Debug, Clone)]
pub struct QuantumBioFusionResult {
    /// Classical dataset output
    pub classical_dataset: Dataset,
    /// Quantum state evolution over time
    pub quantum_evolution: Array3<f64>, // (time, qubits, samples)
    /// Biological spike patterns
    pub spike_patterns: Array3<f64>, // (time, neurons, samples)
    /// Quantum-biological entanglement matrix
    pub entanglement_matrix: Array2<f64>,
    /// Fusion learning dynamics
    pub fusion_learning_curve: Vec<f64>,
    /// Emergent quantum-bio features
    pub emergent_features: Array2<f64>,
    /// Quantum coherence preservation over time
    pub coherence_preservation: Vec<f64>,
}

/// Quantum interference patterns in biological networks
#[derive(Debug, Clone)]
pub struct QuantumInterference {
    /// Constructive interference strength
    pub constructive_strength: f64,
    /// Destructive interference strength  
    pub destructive_strength: f64,
    /// Interference phase shift
    pub phase_shift: f64,
    /// Spatial interference pattern
    pub spatial_pattern: Array2<f64>,
}

impl Default for QuantumNeuromorphicFusion {
    fn default() -> Self {
        Self {
            quantum_engine: QuantumDatasetGenerator::default(),
            neuromorphic_engine: NeuromorphicProcessor::default(),
            quantum_bio_coupling: 0.7,
            coherence_plasticity_factor: 0.5,
            quantum_decoherence_time: Duration::from_millis(1000),
            adaptive_learning_rate: 0.001,
            quantum_neural_advantage: true,
        }
    }
}

impl QuantumNeuromorphicFusion {
    /// Create a new quantum-neuromorphic fusion processor
    pub fn new(_quantum_coupling: f64, coherence_time: Duration, adaptivelearning: bool) -> Self {
        Self {
            quantum_engine: QuantumDatasetGenerator::default(),
            neuromorphic_engine: NeuromorphicProcessor::default(),
            quantum_bio_coupling: _quantum_coupling.clamp(0.0, 1.0),
            coherence_plasticity_factor: 0.5,
            quantum_decoherence_time: coherence_time,
            adaptive_learning_rate: if adaptivelearning { 0.001 } else { 0.0 },
            quantum_neural_advantage: true,
        }
    }

    /// Configure the fusion with custom quantum and neuromorphic engines
    pub fn with_engines(
        mut self,
        quantum_engine: QuantumDatasetGenerator,
        neuromorphic_engine: NeuromorphicProcessor,
    ) -> Self {
        self.quantum_engine = quantum_engine;
        self.neuromorphic_engine = neuromorphic_engine;
        self
    }

    /// Enable or disable quantum neural advantage
    pub fn with_quantum_advantage(mut self, enabled: bool) -> Self {
        self.quantum_neural_advantage = enabled;
        self
    }

    /// Generate dataset using quantum-neuromorphic fusion
    pub fn generate_fusion_dataset(
        &self,
        n_samples: usize,
        n_features: usize,
        fusion_complexity: f64,
        random_seed: Option<u64>,
    ) -> Result<QuantumBioFusionResult> {
        if n_samples == 0 || n_features == 0 {
            return Err(DatasetsError::InvalidFormat(
                "Samples and _features must be > 0".to_string(),
            ));
        }

        let mut rng = match random_seed {
            Some(_seed) => StdRng::seed_from_u64(_seed),
            None => StdRng::from_rng(&mut thread_rng()),
        };

        // Initialize quantum-biological hybrid network
        let mut quantum_neurons = self.initialize_quantum_neurons(n_features, &mut rng)?;
        let mut quantum_synapses = self.initialize_quantum_synapses(&quantum_neurons, &mut rng)?;

        // Process through quantum-biological fusion
        let simulation_steps = 100;
        let mut classical_data = Array2::zeros((n_samples, n_features));
        let mut classical_targets = Array1::zeros(n_samples);
        let mut quantum_evolution = Array3::zeros((simulation_steps, n_features, n_samples));
        let mut spike_patterns = Array3::zeros((simulation_steps, n_features, n_samples));
        let mut fusion_learning_curve = Vec::with_capacity(n_samples);
        let mut coherence_preservation = Vec::with_capacity(simulation_steps);

        // Generate each sample through quantum-biological co-evolution
        for sample_idx in 0..n_samples {
            let mut sample_learning = 0.0;

            // Quantum-biological simulation over time
            for time_step in 0..simulation_steps {
                // Quantum state evolution with decoherence
                self.evolve_quantum_states(
                    &mut quantum_neurons,
                    &quantum_synapses,
                    time_step as f64 * 0.01, // Convert step to time
                    time_step,
                    &mut rng,
                )?;

                // Biological spike dynamics with quantum influence
                let spike_response = self.quantum_influenced_spiking(
                    &mut quantum_neurons,
                    fusion_complexity,
                    &mut rng,
                )?;

                // Quantum-biological learning and adaptation
                let learning_delta =
                    self.quantum_bio_learning(&mut quantum_synapses, &spike_response, time_step)?;

                sample_learning += learning_delta;

                // Record quantum and biological states
                for neuron_idx in 0..n_features {
                    if neuron_idx < quantum_neurons.len() {
                        // Record quantum state probability
                        let quantum_prob = quantum_neurons[neuron_idx].quantum_state.0.powi(2)
                            + quantum_neurons[neuron_idx].quantum_state.1.powi(2);
                        quantum_evolution[[time_step, neuron_idx, sample_idx]] = quantum_prob;

                        // Record spike response
                        spike_patterns[[time_step, neuron_idx, sample_idx]] =
                            spike_response[neuron_idx];
                    }
                }

                // Measure coherence preservation
                let coherence = self.measure_quantum_coherence(&quantum_neurons)?;
                if sample_idx == 0 {
                    coherence_preservation.push(coherence);
                }
            }

            // Extract final classical _features from quantum-biological evolution
            for feature_idx in 0..n_features {
                if feature_idx < quantum_neurons.len() {
                    // Fusion of quantum and biological information
                    let quantum_component = quantum_neurons[feature_idx].quantum_state.0.tanh();
                    let biological_component =
                        quantum_neurons[feature_idx].membrane_potential.tanh();

                    classical_data[[sample_idx, feature_idx]] = self.quantum_bio_coupling
                        * quantum_component
                        + (1.0 - self.quantum_bio_coupling) * biological_component;
                }
            }

            // Quantum-biological target assignment
            classical_targets[sample_idx] =
                self.fusion_target_assignment(&quantum_neurons, fusion_complexity, &mut rng)?;

            fusion_learning_curve.push(sample_learning / simulation_steps as f64);
        }

        // Create fusion dataset
        let classical_dataset = Dataset::new(classical_data, Some(classical_targets));

        // Extract entanglement matrix
        let entanglement_matrix = self.extract_entanglement_matrix(&quantum_synapses)?;

        // Generate emergent _features from fusion dynamics
        let emergent_features =
            self.extract_fusion_features(&quantum_evolution, &spike_patterns)?;

        Ok(QuantumBioFusionResult {
            classical_dataset,
            quantum_evolution,
            spike_patterns,
            entanglement_matrix,
            fusion_learning_curve,
            emergent_features,
            coherence_preservation,
        })
    }

    /// Transform existing dataset through quantum-neuromorphic fusion
    pub fn transform_with_fusion(
        &self,
        dataset: &Dataset,
        fusion_depth: usize,
        quantum_interference: bool,
        random_seed: Option<u64>,
    ) -> Result<QuantumBioFusionResult> {
        let data = &dataset.data;
        let n_samples = data.nrows();
        let n_features = data.ncols();

        if n_samples == 0 || n_features == 0 {
            return Err(DatasetsError::InvalidFormat(
                "Dataset must have samples and features".to_string(),
            ));
        }

        let mut rng = match random_seed {
            Some(_seed) => StdRng::seed_from_u64(_seed),
            None => StdRng::from_rng(&mut thread_rng()),
        };

        // Initialize quantum-biological network for transformation
        let mut quantum_neurons = self.initialize_quantum_neurons(n_features, &mut rng)?;
        let mut quantum_synapses = self.initialize_quantum_synapses(&quantum_neurons, &mut rng)?;

        let mut transformed_data = Array2::zeros((n_samples, n_features));
        let mut transformed_targets = Array1::zeros(n_samples);
        let mut quantum_evolution = Array3::zeros((fusion_depth, n_features, n_samples));
        let mut spike_patterns = Array3::zeros((fusion_depth, n_features, n_samples));
        let mut fusion_learning_curve = Vec::with_capacity(n_samples);
        let mut coherence_preservation = Vec::with_capacity(fusion_depth);

        // Transform each sample through quantum-biological fusion layers
        for sample_idx in 0..n_samples {
            let input_sample = data.row(sample_idx);
            let mut sample_learning = 0.0;

            // Initialize quantum neurons with input data
            self.encode_classical_to_quantum(&input_sample, &mut quantum_neurons)?;

            // Apply fusion transformation layers
            for fusion_layer in 0..fusion_depth {
                // Quantum layer: apply quantum operations
                if quantum_interference {
                    self.apply_quantum_interference(&mut quantum_neurons, &mut rng)?;
                }

                // Biological layer: neuromorphic processing
                let spike_response = self.quantum_influenced_spiking(
                    &mut quantum_neurons,
                    0.5, // base complexity
                    &mut rng,
                )?;

                // Quantum-biological entanglement updates
                let learning_delta = self.update_quantum_bio_entanglement(
                    &mut quantum_synapses,
                    &spike_response,
                    fusion_layer,
                )?;

                sample_learning += learning_delta;

                // Record states for this layer
                for neuron_idx in 0..n_features {
                    if neuron_idx < quantum_neurons.len() {
                        let quantum_amplitude =
                            (quantum_neurons[neuron_idx].quantum_state.0.powi(2)
                                + quantum_neurons[neuron_idx].quantum_state.1.powi(2))
                            .sqrt();
                        quantum_evolution[[fusion_layer, neuron_idx, sample_idx]] =
                            quantum_amplitude;
                        spike_patterns[[fusion_layer, neuron_idx, sample_idx]] =
                            spike_response[neuron_idx];
                    }
                }

                // Measure coherence preservation through fusion layers
                if sample_idx == 0 {
                    let coherence = self.measure_quantum_coherence(&quantum_neurons)?;
                    if fusion_layer < coherence_preservation.len() {
                        coherence_preservation[fusion_layer] = coherence;
                    } else {
                        coherence_preservation.push(coherence);
                    }
                }
            }

            // Extract transformed features from final quantum-biological state
            for feature_idx in 0..n_features {
                if feature_idx < quantum_neurons.len() {
                    let quantum_real = quantum_neurons[feature_idx].quantum_state.0;
                    let quantum_imag = quantum_neurons[feature_idx].quantum_state.1;
                    let biological_membrane = quantum_neurons[feature_idx].membrane_potential;

                    // Fusion transformation combining quantum and biological information
                    transformed_data[[sample_idx, feature_idx]] = quantum_real
                        * biological_membrane.cos()
                        + quantum_imag * biological_membrane.sin();
                }
            }

            // Assign transformed target
            transformed_targets[sample_idx] = dataset
                .target
                .as_ref()
                .map(|targets| targets[sample_idx])
                .unwrap_or(sample_learning.tanh());

            fusion_learning_curve.push(sample_learning / fusion_depth as f64);
        }

        let transformed_dataset = Dataset::new(transformed_data, Some(transformed_targets));
        let entanglement_matrix = self.extract_entanglement_matrix(&quantum_synapses)?;
        let emergent_features =
            self.extract_fusion_features(&quantum_evolution, &spike_patterns)?;

        Ok(QuantumBioFusionResult {
            classical_dataset: transformed_dataset,
            quantum_evolution,
            spike_patterns,
            entanglement_matrix,
            fusion_learning_curve,
            emergent_features,
            coherence_preservation,
        })
    }

    /// Analyze quantum-biological interference patterns
    pub fn analyze_interference_patterns(
        &self,
        fusion_result: &QuantumBioFusionResult,
    ) -> Result<QuantumInterference> {
        let quantum_data = &fusion_result.quantum_evolution;
        let biological_data = &fusion_result.spike_patterns;

        let (time_steps, n_qubits, n_samples) = quantum_data.dim();
        let mut constructive_strength = 0.0;
        let mut destructive_strength = 0.0;
        let mut phase_shift = 0.0;
        let mut pattern_count = 0;

        // Analyze interference between quantum and biological signals
        for sample_idx in 0..n_samples {
            for qubit_idx in 0..n_qubits {
                for time_idx in 0..(time_steps - 1) {
                    let quantum_amplitude = quantum_data[[time_idx, qubit_idx, sample_idx]];
                    let biological_spike = biological_data[[time_idx, qubit_idx, sample_idx]];

                    let quantum_next = quantum_data[[time_idx + 1, qubit_idx, sample_idx]];
                    let biological_next = biological_data[[time_idx + 1, qubit_idx, sample_idx]];

                    // Calculate interference
                    let quantum_phase = quantum_amplitude.atan2(quantum_next);
                    let biological_phase = biological_spike.atan2(biological_next);
                    let phase_difference = (quantum_phase - biological_phase).abs();

                    // Classify interference type
                    if phase_difference < PI / 4.0 {
                        // Constructive interference (phases aligned)
                        constructive_strength += quantum_amplitude * biological_spike;
                    } else if phase_difference > 3.0 * PI / 4.0 {
                        // Destructive interference (phases opposed)
                        destructive_strength += quantum_amplitude * biological_spike;
                    }

                    phase_shift += phase_difference;
                    pattern_count += 1;
                }
            }
        }

        // Normalize results
        constructive_strength /= pattern_count as f64;
        destructive_strength /= pattern_count as f64;
        phase_shift /= pattern_count as f64;

        // Create spatial interference pattern
        let spatial_pattern = self.generate_spatial_interference_pattern(
            constructive_strength,
            destructive_strength,
            n_qubits,
        )?;

        Ok(QuantumInterference {
            constructive_strength,
            destructive_strength,
            phase_shift,
            spatial_pattern,
        })
    }

    // Private helper methods for quantum-neuromorphic fusion

    fn initialize_quantum_neurons(
        &self,
        n_neurons: usize,
        rng: &mut StdRng,
    ) -> Result<Vec<QuantumNeuron>> {
        let mut _neurons = Vec::with_capacity(n_neurons);

        for neuron_idx in 0..n_neurons {
            // Initialize quantum state in superposition
            let theta = rng.random::<f64>() * PI;
            let phi = rng.random::<f64>() * 2.0 * PI;

            let quantum_state = (theta.cos() * phi.cos(), theta.sin() * phi.sin());

            // Generate entanglement connections
            let entanglement_map: Vec<usize> = (0..n_neurons)
                .filter(|&i| i != neuron_idx && rng.random::<f64>() < 0.1)
                .collect();

            _neurons.push(QuantumNeuron {
                membrane_potential: rng.random::<f64>() - 0.5,
                quantum_state,
                phase_evolution: 0.0,
                spike_threshold: 1.0,
                last_spike_time: None,
                decoherence_rate: 1.0 / self.quantum_decoherence_time.as_secs_f64(),
                entanglement_map,
            });
        }

        Ok(_neurons)
    }

    fn initialize_quantum_synapses(
        &self,
        neurons: &[QuantumNeuron],
        rng: &mut StdRng,
    ) -> Result<Vec<QuantumSynapse>> {
        let n_neurons = neurons.len();
        let n_synapses = (n_neurons * n_neurons) / 4; // Sparse connectivity
        let mut synapses = Vec::with_capacity(n_synapses);

        for _ in 0..n_synapses {
            let quantum_phase = rng.random::<f64>() * 2.0 * PI;
            let amplitude_real = rng.random::<f64>() - 0.5;
            let amplitude_imag = rng.random::<f64>() - 0.5;

            synapses.push(QuantumSynapse {
                classical_weight: rng.random::<f64>() - 0.5,
                quantum_amplitudes: (amplitude_real, amplitude_imag),
                quantum_phase,
                entangled_partner: if rng.random::<f64>() < 0.3 {
                    Some(rng.sample(Uniform::new(0, n_synapses).unwrap()))
                } else {
                    None
                },
                coherence_time: self.quantum_decoherence_time,
                coupling_strength: self.quantum_bio_coupling,
            });
        }

        Ok(synapses)
    }

    fn evolve_quantum_states(
        &self,
        neurons: &mut [QuantumNeuron],
        _synapses: &[QuantumSynapse],
        _time: f64,
        _step: usize,
        rng: &mut StdRng,
    ) -> Result<()> {
        for neuron in neurons.iter_mut() {
            // Quantum state evolution under Hamiltonian
            let dt = 0.01; // Time _step
            let omega = 1.0; // Base frequency

            // Schrödinger evolution: |ψ(t+dt)⟩ = e^(-iHdt)|ψ(t)⟩
            let phase_increment = omega * dt + neuron.phase_evolution;
            let cos_phase = phase_increment.cos();
            let sin_phase = phase_increment.sin();

            let new_real = neuron.quantum_state.0 * cos_phase - neuron.quantum_state.1 * sin_phase;
            let new_imag = neuron.quantum_state.0 * sin_phase + neuron.quantum_state.1 * cos_phase;

            neuron.quantum_state = (new_real, new_imag);
            neuron.phase_evolution += phase_increment;

            // Apply decoherence
            let decoherence_factor = (-neuron.decoherence_rate * dt).exp();
            neuron.quantum_state.0 *= decoherence_factor;
            neuron.quantum_state.1 *= decoherence_factor;

            // Renormalize quantum state
            let norm = (neuron.quantum_state.0.powi(2) + neuron.quantum_state.1.powi(2)).sqrt();
            if norm > 1e-10 {
                neuron.quantum_state.0 /= norm;
                neuron.quantum_state.1 /= norm;
            }

            // Add quantum noise
            if self.quantum_neural_advantage {
                let noise_strength = 0.01;
                neuron.quantum_state.0 += noise_strength * (rng.random::<f64>() - 0.5);
                neuron.quantum_state.1 += noise_strength * (rng.random::<f64>() - 0.5);
            }
        }

        Ok(())
    }

    fn quantum_influenced_spiking(
        &self,
        neurons: &mut [QuantumNeuron],
        complexity: f64,
        rng: &mut StdRng,
    ) -> Result<Array1<f64>> {
        let n_neurons = neurons.len();
        let mut spike_response = Array1::zeros(n_neurons);

        for (neuron_idx, neuron) in neurons.iter_mut().enumerate() {
            // Quantum influence on membrane potential
            let quantum_probability =
                neuron.quantum_state.0.powi(2) + neuron.quantum_state.1.powi(2);
            let quantum_influence = self.quantum_bio_coupling * quantum_probability * complexity;

            // Update membrane potential with quantum influence
            neuron.membrane_potential += quantum_influence;

            // Add biological noise
            neuron.membrane_potential += 0.05 * (rng.random::<f64>() - 0.5);

            // Check for spike generation
            let dynamic_threshold = neuron.spike_threshold * (1.0 + 0.1 * quantum_influence);

            if neuron.membrane_potential > dynamic_threshold {
                spike_response[neuron_idx] = 1.0;
                neuron.membrane_potential = 0.0; // Reset after spike
                neuron.last_spike_time = Some(Instant::now());

                // Quantum state collapse upon spike
                if self.quantum_neural_advantage {
                    let collapse_probability = rng.random::<f64>();
                    if collapse_probability > 0.5 {
                        neuron.quantum_state = (1.0, 0.0); // Collapse to |0⟩
                    } else {
                        neuron.quantum_state = (0.0, 1.0); // Collapse to |1⟩
                    }
                }
            }

            // Membrane potential decay
            neuron.membrane_potential *= 0.95;
        }

        Ok(spike_response)
    }

    fn quantum_bio_learning(
        &self,
        synapses: &mut [QuantumSynapse],
        _spike_response: &Array1<f64>,
        time_step: usize,
    ) -> Result<f64> {
        let mut total_learning = 0.0;

        for synapse in synapses.iter_mut() {
            // Quantum-enhanced Hebbian learning
            let learning_rate = self.adaptive_learning_rate * (1.0 + time_step as f64 * 0.001);

            // Combine quantum and classical information for learning
            let quantum_strength = (synapse.quantum_amplitudes.0.powi(2)
                + synapse.quantum_amplitudes.1.powi(2))
            .sqrt();

            let classical_change = learning_rate * quantum_strength * synapse.coupling_strength;

            // Update classical weight
            synapse.classical_weight += classical_change;
            synapse.classical_weight = synapse.classical_weight.clamp(-2.0, 2.0);

            // Update quantum amplitudes with phase evolution
            let phase_evolution = synapse.quantum_phase + 0.01 * time_step as f64;
            synapse.quantum_amplitudes.0 *= phase_evolution.cos();
            synapse.quantum_amplitudes.1 *= phase_evolution.sin();

            total_learning += classical_change.abs();
        }

        Ok(total_learning / synapses.len() as f64)
    }

    fn measure_quantum_coherence(&self, neurons: &[QuantumNeuron]) -> Result<f64> {
        let mut total_coherence = 0.0;

        for neuron in neurons {
            // Measure quantum coherence as |⟨ψ|ψ⟩|²
            let coherence = neuron.quantum_state.0.powi(2) + neuron.quantum_state.1.powi(2);
            total_coherence += coherence;
        }

        Ok(total_coherence / neurons.len() as f64)
    }

    fn fusion_target_assignment(
        &self,
        neurons: &[QuantumNeuron],
        complexity: f64,
        rng: &mut StdRng,
    ) -> Result<f64> {
        // Quantum-biological target assignment using both quantum and classical information
        let mut quantum_contribution = 0.0;
        let mut biological_contribution = 0.0;

        for neuron in neurons {
            quantum_contribution += neuron.quantum_state.0 * neuron.quantum_state.1;
            biological_contribution += neuron.membrane_potential.tanh();
        }

        let fusion_target = self.quantum_bio_coupling * quantum_contribution
            + (1.0 - self.quantum_bio_coupling) * biological_contribution;

        // Add complexity-dependent noise
        let noise = complexity * (rng.random::<f64>() - 0.5) * 0.1;

        Ok((fusion_target + noise).tanh())
    }

    fn extract_entanglement_matrix(&self, synapses: &[QuantumSynapse]) -> Result<Array2<f64>> {
        let n_synapses = synapses.len();
        let matrix_size = (n_synapses as f64).sqrt().ceil() as usize;
        let mut entanglement_matrix = Array2::zeros((matrix_size, matrix_size));

        for (synapse_idx, synapse) in synapses.iter().enumerate() {
            let row = synapse_idx / matrix_size;
            let col = synapse_idx % matrix_size;

            if row < matrix_size && col < matrix_size {
                // Entanglement strength based on quantum amplitudes and coupling
                let entanglement_strength = (synapse.quantum_amplitudes.0.powi(2)
                    + synapse.quantum_amplitudes.1.powi(2))
                    * synapse.coupling_strength;
                entanglement_matrix[[row, col]] = entanglement_strength;
            }
        }

        Ok(entanglement_matrix)
    }

    fn extract_fusion_features(
        &self,
        quantum_evolution: &Array3<f64>,
        spike_patterns: &Array3<f64>,
    ) -> Result<Array2<f64>> {
        let (time_steps, n_features, n_samples) = quantum_evolution.dim();
        let mut fusion_features = Array2::zeros((n_samples, n_features));

        // Extract features that capture quantum-biological dynamics
        for sample_idx in 0..n_samples {
            for feature_idx in 0..n_features {
                let quantum_slice = quantum_evolution.slice(s![.., feature_idx, sample_idx]);
                let spike_slice = spike_patterns.slice(s![.., feature_idx, sample_idx]);

                // Quantum feature: temporal coherence
                let quantum_coherence = quantum_slice.variance();

                // Biological feature: spike rate and timing
                let spike_rate = spike_slice.sum() / time_steps as f64;

                // Fusion feature: quantum-biological correlation
                let correlation = self.calculate_correlation(&quantum_slice, &spike_slice)?;

                fusion_features[[sample_idx, feature_idx]] =
                    0.4 * quantum_coherence + 0.4 * spike_rate + 0.2 * correlation;
            }
        }

        Ok(fusion_features)
    }

    fn encode_classical_to_quantum(
        &self,
        classical_data: &scirs2_core::ndarray::ArrayView1<f64>,
        quantum_neurons: &mut [QuantumNeuron],
    ) -> Result<()> {
        for (idx, &value) in classical_data.iter().enumerate() {
            if idx < quantum_neurons.len() {
                // Encode classical value into quantum superposition
                let theta = value.abs().min(PI);
                let phi = value.signum() * PI / 2.0;

                quantum_neurons[idx].quantum_state =
                    (theta.cos() * phi.cos(), theta.sin() * phi.sin());

                // Initialize membrane potential
                quantum_neurons[idx].membrane_potential = value * 0.5;
            }
        }

        Ok(())
    }

    fn apply_quantum_interference(
        &self,
        neurons: &mut [QuantumNeuron],
        rng: &mut StdRng,
    ) -> Result<()> {
        // Apply quantum interference between entangled neurons
        for neuron_idx in 0..neurons.len() {
            let entangled_indices = neurons[neuron_idx].entanglement_map.clone();

            for &partner_idx in &entangled_indices {
                if partner_idx < neurons.len() && partner_idx != neuron_idx {
                    // Apply interference between entangled neurons
                    let interference_phase = rng.random::<f64>() * 2.0 * PI;
                    let interference_strength = 0.1;

                    let real_interference = interference_strength * interference_phase.cos();
                    let imag_interference = interference_strength * interference_phase.sin();

                    neurons[neuron_idx].quantum_state.0 += real_interference;
                    neurons[neuron_idx].quantum_state.1 += imag_interference;

                    neurons[partner_idx].quantum_state.0 -= real_interference;
                    neurons[partner_idx].quantum_state.1 -= imag_interference;
                }
            }
        }

        Ok(())
    }

    fn update_quantum_bio_entanglement(
        &self,
        synapses: &mut [QuantumSynapse],
        spike_response: &Array1<f64>,
        layer: usize,
    ) -> Result<f64> {
        let mut entanglement_update = 0.0;

        for synapse in synapses.iter_mut() {
            // Update entanglement based on spike activity and quantum state
            let layer_factor = 1.0 / (1.0 + layer as f64 * 0.1);
            let spike_influence = spike_response.sum() / spike_response.len() as f64;

            // Quantum amplitude evolution with biological feedback
            let amplitude_update = self.adaptive_learning_rate * layer_factor * spike_influence;

            synapse.quantum_amplitudes.0 += amplitude_update;
            synapse.quantum_amplitudes.1 += amplitude_update * 0.5;

            // Update coupling strength
            synapse.coupling_strength += 0.001 * amplitude_update;
            synapse.coupling_strength = synapse.coupling_strength.clamp(0.0, 1.0);

            entanglement_update += amplitude_update.abs();
        }

        Ok(entanglement_update / synapses.len() as f64)
    }

    fn generate_spatial_interference_pattern(
        &self,
        constructive: f64,
        destructive: f64,
        size: usize,
    ) -> Result<Array2<f64>> {
        let mut pattern = Array2::zeros((size, size));

        for i in 0..size {
            for j in 0..size {
                let x = i as f64 / size as f64;
                let y = j as f64 / size as f64;

                // Create interference pattern using wave equations
                let constructive_wave = constructive * (2.0 * PI * x).sin() * (2.0 * PI * y).cos();
                let destructive_wave = destructive * (PI * x).cos() * (PI * y).sin();

                pattern[[i, j]] = constructive_wave - destructive_wave;
            }
        }

        Ok(pattern)
    }

    fn calculate_correlation(
        &self,
        quantum_data: &scirs2_core::ndarray::ArrayView1<f64>,
        biological_data: &scirs2_core::ndarray::ArrayView1<f64>,
    ) -> Result<f64> {
        if quantum_data.len() != biological_data.len() {
            return Ok(0.0);
        }

        let n = quantum_data.len() as f64;
        let quantum_mean = quantum_data.sum() / n;
        let biological_mean = biological_data.sum() / n;

        let mut numerator = 0.0;
        let mut quantum_var = 0.0;
        let mut biological_var = 0.0;

        for i in 0..quantum_data.len() {
            let quantum_dev = quantum_data[i] - quantum_mean;
            let biological_dev = biological_data[i] - biological_mean;

            numerator += quantum_dev * biological_dev;
            quantum_var += quantum_dev.powi(2);
            biological_var += biological_dev.powi(2);
        }

        let denominator = (quantum_var * biological_var).sqrt();
        if denominator > 1e-10 {
            Ok(numerator / denominator)
        } else {
            Ok(0.0)
        }
    }
}

/// Convenience function to create quantum-neuromorphic fusion processor
#[allow(dead_code)]
pub fn create_quantum_neuromorphic_fusion() -> QuantumNeuromorphicFusion {
    QuantumNeuromorphicFusion::default()
}

/// Convenience function to create quantum-neuromorphic fusion with custom parameters
#[allow(dead_code)]
pub fn create_fusion_with_params(
    quantum_coupling: f64,
    coherence_time_ms: u64,
    adaptive_learning: bool,
) -> QuantumNeuromorphicFusion {
    QuantumNeuromorphicFusion::new(
        quantum_coupling,
        Duration::from_millis(coherence_time_ms),
        adaptive_learning,
    )
}

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

    #[test]
    fn test_quantum_neuromorphic_fusion_creation() {
        let fusion = QuantumNeuromorphicFusion::default();
        assert!(fusion.quantum_bio_coupling > 0.0);
        assert!(fusion.quantum_neural_advantage);
        assert_eq!(fusion.adaptive_learning_rate, 0.001);
    }

    #[test]
    fn test_fusion_dataset_generation() {
        let fusion = QuantumNeuromorphicFusion::default();
        let result = fusion
            .generate_fusion_dataset(20, 5, 0.5, Some(42))
            .unwrap();

        assert_eq!(result.classical_dataset.n_samples(), 20);
        assert_eq!(result.classical_dataset.n_features(), 5);
        assert_eq!(result.quantum_evolution.dim(), (100, 5, 20));
        assert_eq!(result.spike_patterns.dim(), (100, 5, 20));
        assert_eq!(result.fusion_learning_curve.len(), 20);
        assert!(!result.coherence_preservation.is_empty());
    }

    #[test]
    fn test_dataset_transformation_with_fusion() {
        let data =
            Array2::from_shape_vec((10, 4), (0..40).map(|x| x as f64 * 0.1).collect()).unwrap();
        let targets = Array1::from((0..10).map(|x| (x % 3) as f64).collect::<Vec<_>>());
        let dataset = Dataset::new(data, Some(targets));

        let fusion = QuantumNeuromorphicFusion::default();
        let result = fusion
            .transform_with_fusion(&dataset, 5, true, Some(42))
            .unwrap();

        assert_eq!(result.classical_dataset.n_samples(), 10);
        assert_eq!(result.classical_dataset.n_features(), 4);
        assert_eq!(result.quantum_evolution.dim(), (5, 4, 10)); // fusion_depth, features, samples
        assert_eq!(result.spike_patterns.dim(), (5, 4, 10));
    }

    #[test]
    fn test_interference_pattern_analysis() {
        let fusion = QuantumNeuromorphicFusion::default();
        let result = fusion.generate_fusion_dataset(5, 3, 0.3, Some(42)).unwrap();

        let interference = fusion.analyze_interference_patterns(&result).unwrap();

        assert!(interference.constructive_strength.is_finite());
        assert!(interference.destructive_strength.is_finite());
        assert!(interference.phase_shift >= 0.0);
        assert_eq!(interference.spatial_pattern.dim(), (3, 3)); // Size based on n_features
    }

    #[test]
    fn test_quantum_advantage_configuration() {
        let fusion = QuantumNeuromorphicFusion::default().with_quantum_advantage(false);

        assert!(!fusion.quantum_neural_advantage);
    }

    #[test]
    fn test_fusion_with_custom_parameters() {
        let fusion = create_fusion_with_params(0.8, 500, true);

        assert_eq!(fusion.quantum_bio_coupling, 0.8);
        assert_eq!(fusion.quantum_decoherence_time, Duration::from_millis(500));
        assert!(fusion.adaptive_learning_rate > 0.0);
    }
}