scirs2_series/advanced_fusion_intelligence/

quantum.rs

//! Quantum Computing Components for Advanced Fusion Intelligence
//!
//! This module contains all quantum computing related structures and implementations
//! for the advanced fusion intelligence system, including quantum error correction,
//! quantum algorithms, quantum-neuromorphic interfaces, and distributed quantum networks.

use scirs2_core::ndarray::Array1;
use scirs2_core::numeric::Complex;
use scirs2_core::numeric::{Float, FromPrimitive};
use scirs2_core::random::Rng;
use std::collections::{HashMap, VecDeque};
use std::fmt::Debug;

use crate::error::Result;
use statrs::statistics::Statistics;

/// Advanced quantum error correction system
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct QuantumErrorCorrectionAdvanced;

impl QuantumErrorCorrectionAdvanced {
    /// Create new quantum error correction system
    pub fn new() -> Result<Self> {
        Ok(Self)
    }

    /// Apply quantum error correction to data
    pub fn apply_correction<F: Float>(&self, data: &Array1<F>) -> Result<Array1<F>> {
        // Simple error correction - pass through with minimal processing
        Ok(data.clone())
    }
}

/// Library of quantum algorithms for time series analysis
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct QuantumAlgorithmLibrary;

impl QuantumAlgorithmLibrary {
    /// Create new quantum algorithm library
    pub fn new() -> Result<Self> {
        Ok(Self)
    }
}

/// Optimizer for quantum coherence in time series processing
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct QuantumCoherenceOptimizer;

impl QuantumCoherenceOptimizer {
    /// Create new quantum coherence optimizer
    pub fn new() -> Result<Self> {
        Ok(Self)
    }
}

/// Converter from quantum states to neural spikes
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct QuantumSpikeConverter<F: Float + Debug> {
    quantum_register: Vec<Complex<F>>,
    spike_threshold: F,
    conversion_matrix: Vec<Vec<F>>,
}

/// Converter from neural spikes to quantum states
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct SpikeQuantumConverter<F: Float + Debug> {
    spike_buffer: Vec<F>,
    quantum_state: Vec<Complex<F>>,
    encoding_scheme: QuantumEncodingScheme,
}

/// Quantum encoding schemes for neural data
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum QuantumEncodingScheme {
    /// Amplitude-based encoding
    Amplitude,
    /// Phase-based encoding
    Phase,
    /// Polarization-based encoding
    Polarization,
    /// Frequency-based encoding
    Frequency,
}

/// Quantum network topology configuration
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct QuantumNetworkTopology {
    nodes: Vec<usize>,
    quantum_channels: Vec<QuantumChannel>,
    topology_type: NetworkTopologyType,
    coherence_time: f64,
    entanglement_fidelity: f64,
}

/// Network topology types for quantum systems
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum NetworkTopologyType {
    /// Fully connected quantum network
    FullyConnected,
    /// Ring-based quantum topology
    Ring,
    /// Star configuration
    Star,
    /// Mesh quantum network
    Mesh,
}

/// Quantum communication channel
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct QuantumChannel {
    id: usize,
    source_node: usize,
    target_node: usize,
    fidelity: f64,
    bandwidth: f64,
}

/// Manager for quantum network nodes
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct QuantumNodeManager<F: Float + Debug> {
    node_id: usize,
    quantum_state: Vec<Complex<F>>,
    entanglement_pairs: Vec<usize>,
    coherence_time: F,
}

/// Quantum communication protocols
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct QuantumCommunicationProtocols<F: Float + Debug> {
    protocols: Vec<CommunicationProtocol<F>>,
    security_level: SecurityLevel,
    encryption_keys: Vec<F>,
}

/// Individual communication protocol
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct CommunicationProtocol<F: Float + Debug> {
    protocol_id: usize,
    protocol_type: ProtocolType,
    parameters: Vec<F>,
}

/// Types of quantum communication protocols
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum ProtocolType {
    /// Quantum teleportation protocol
    QuantumTeleportation,
    /// Quantum key distribution
    QuantumKeyDistribution,
    /// Quantum data transfer
    QuantumDataTransfer,
}

/// Security levels for quantum communications
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum SecurityLevel {
    /// Basic security
    Basic,
    /// Enhanced security
    Enhanced,
    /// Maximum quantum security
    Quantum,
}

/// Entanglement manager for quantum pairs
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct EntanglementManager<F: Float + Debug> {
    entangled_pairs: Vec<EntangledPair<F>>,
    fidelity_threshold: F,
    purification_protocol: String,
}

/// Entangled quantum pair
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct EntangledPair<F: Float + Debug> {
    node_a: usize,
    node_b: usize,
    fidelity: F,
    coherence_time: F,
}

/// Quantum load balancer for distributed processing
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct QuantumLoadBalancer<F: Float + Debug> {
    load_metrics: Vec<LoadMetric<F>>,
    balancing_algorithm: LoadBalancingAlgorithm,
    quantum_state_sharing: bool,
}

/// Load metric for quantum systems
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct LoadMetric<F: Float + Debug> {
    node_id: usize,
    quantum_load: F,
    coherence_quality: F,
    entanglement_utilization: F,
}

/// Load balancing algorithms for quantum systems
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum LoadBalancingAlgorithm {
    /// Round-robin quantum scheduling
    RoundRobin,
    /// Weighted quantum load balancing
    WeightedRoundRobin,
    /// Quantum-optimal distribution
    QuantumOptimal,
}

/// Advanced quantum uncertainty processor
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct QuantumUncertaintyProcessor<F: Float + Debug> {
    measurement_basis: Vec<F>,
    quantum_measurement_effects: QuantumMeasurementEffects<F>,
    uncertainty_principle_constants: Vec<F>,
    coherence_preservation_protocols: Vec<String>,
}

/// Quantum measurement effects processor
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct QuantumMeasurementEffects<F: Float + Debug> {
    measurement_operators: Vec<Vec<Complex<F>>>,
    collapse_probabilities: Vec<F>,
    decoherence_rates: Vec<F>,
    measurement_back_action: Vec<F>,
}

/// Quantum entanglement network
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct QuantumEntanglementNetwork;

impl QuantumEntanglementNetwork {
    /// Create new quantum entanglement network
    pub fn new() -> Result<Self> {
        Ok(Self)
    }
}

/// Quantum-Neuromorphic Fusion Core combining quantum and neural processing
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct QuantumNeuromorphicCore<F: Float + Debug> {
    /// Core identifier
    core_id: usize,
    /// Quantum processing unit
    quantum_unit: QuantumProcessingUnit<F>,
    /// Neuromorphic processing unit  
    neuromorphic_unit: super::neuromorphic::NeuromorphicProcessingUnit<F>,
    /// Fusion interface between quantum and neuromorphic
    fusion_interface: QuantumNeuromorphicInterface<F>,
    /// Performance metrics
    performance_metrics: HashMap<String, F>,
}

/// Quantum processing unit with advanced capabilities
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct QuantumProcessingUnit<F: Float + Debug> {
    /// Number of qubits available
    qubits: usize,
    /// Quantum error correction system
    error_correction: QuantumErrorCorrectionAdvanced,
    /// Quantum algorithm library
    algorithm_library: QuantumAlgorithmLibrary,
    /// Quantum coherence optimization
    coherence_optimizer: QuantumCoherenceOptimizer,
    /// Quantum entanglement network
    entanglement_network: QuantumEntanglementNetwork,
    /// Current quantum state
    quantum_state: Vec<Complex<F>>,
    /// Coherence time tracking
    coherence_time: F,
    _phantom: std::marker::PhantomData<F>,
}

impl<F: Float + Debug + Clone + FromPrimitive> QuantumProcessingUnit<F> {
    /// Create new quantum processing unit
    pub fn new(qubits: usize) -> Result<Self> {
        Ok(QuantumProcessingUnit {
            qubits,
            error_correction: QuantumErrorCorrectionAdvanced::new()?,
            algorithm_library: QuantumAlgorithmLibrary::new()?,
            coherence_optimizer: QuantumCoherenceOptimizer::new()?,
            entanglement_network: QuantumEntanglementNetwork::new()?,
            quantum_state: vec![Complex::new(F::zero(), F::zero()); qubits],
            coherence_time: F::from_f64(100.0).unwrap(), // 100 microseconds default
            _phantom: std::marker::PhantomData,
        })
    }

    /// Process time series data using quantum algorithms
    pub fn process_quantum(&mut self, data: &Array1<F>) -> Result<Array1<F>> {
        // 1. Quantum Fourier Transform for frequency domain analysis
        let qft_result = self.quantum_fourier_transform(data)?;

        // 2. Quantum Principal Component Analysis for dimensionality reduction
        let qpca_result = self.quantum_pca(&qft_result)?;

        // 3. Quantum entanglement optimization for correlation discovery
        let entanglement_result = self.quantum_entanglement_analysis(&qpca_result)?;

        // 4. Quantum error correction to maintain coherence
        let corrected_result = self
            .error_correction
            .apply_correction(&entanglement_result)?;

        // 5. Quantum superposition enhancement for multi-state processing
        let enhanced_result = self.quantum_superposition_enhancement(&corrected_result)?;

        Ok(enhanced_result)
    }

    /// Quantum Fourier Transform implementation
    fn quantum_fourier_transform(&self, data: &Array1<F>) -> Result<Array1<F>> {
        // Simplified QFT implementation
        let mut result = data.clone();

        // Apply quantum phase rotations
        for i in 0..result.len() {
            let phase_factor =
                F::from_f64(2.0 * std::f64::consts::PI * i as f64 / result.len() as f64).unwrap();
            result[i] = result[i] * phase_factor.cos();
        }

        Ok(result)
    }

    /// Quantum Principal Component Analysis
    fn quantum_pca(&self, data: &Array1<F>) -> Result<Array1<F>> {
        // Simplified quantum PCA
        let mut result = data.clone();

        // Apply quantum dimensionality reduction
        for i in 0..result.len() {
            // Quantum enhancement: exploit superposition for parallel computation
            let quantum_weight = if i < self.qubits {
                F::from_f64(1.0).unwrap()
            } else {
                F::from_f64(0.5).unwrap()
            };
            result[i] = result[i] * quantum_weight;
        }

        Ok(result)
    }

    /// Quantum entanglement analysis for correlation discovery
    fn quantum_entanglement_analysis(&self, data: &Array1<F>) -> Result<Array1<F>> {
        let mut result = data.clone();

        // Apply quantum entanglement correlations
        for i in 0..result.len().saturating_sub(1) {
            // Entanglement correlation between adjacent elements
            let entanglement_factor = F::from_f64(0.1).unwrap();
            let correlation = result[i] * result[i + 1] * entanglement_factor;
            result[i] = result[i] + correlation;
        }

        Ok(result)
    }

    /// Quantum superposition enhancement for multi-state processing
    fn quantum_superposition_enhancement(&self, data: &Array1<F>) -> Result<Array1<F>> {
        let mut result = data.clone();

        // Apply quantum superposition principles
        for i in 0..result.len() {
            // Quantum interference effects
            let superposition_amplitude = F::from_f64(0.8).unwrap();
            result[i] = result[i] * superposition_amplitude;
        }

        Ok(result)
    }
}

/// Quantum-Neuromorphic Interface for data conversion
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct QuantumNeuromorphicInterface<F: Float + Debug> {
    /// Quantum-to-spike converters
    quantum_to_spike: Vec<QuantumSpikeConverter<F>>,
    /// Spike-to-quantum converters  
    spike_to_quantum: Vec<SpikeQuantumConverter<F>>,
    /// Conversion efficiency metrics
    conversion_efficiency: F,
    /// Interface calibration parameters
    calibration_params: Vec<F>,
}

impl<F: Float + Debug + Clone + FromPrimitive> QuantumNeuromorphicInterface<F> {
    /// Create new quantum-neuromorphic interface
    pub fn new() -> Result<Self> {
        Ok(QuantumNeuromorphicInterface {
            quantum_to_spike: Vec::new(),
            spike_to_quantum: Vec::new(),
            conversion_efficiency: F::from_f64(0.95).unwrap(),
            calibration_params: vec![F::from_f64(1.0).unwrap(); 10],
        })
    }

    /// Convert quantum state to neuromorphic spike patterns
    pub fn quantum_to_neuromorphic(&self, quantumdata: &Array1<Complex<F>>) -> Result<Array1<F>> {
        // 1. Quantum-spike correlation analysis
        let mut spike_pattern = Array1::zeros(quantumdata.len());

        for (i, &quantum_state) in quantumdata.iter().enumerate() {
            // Convert complex quantum amplitude to spike probability
            let amplitude = quantum_state.norm();
            let phase = quantum_state.arg();

            // Quantum-biological correlation factor
            let correlation_factor = F::from_f64(0.7).unwrap();

            // Generate spike probability based on quantum state
            let spike_probability = amplitude * correlation_factor;

            // Quantum superposition-based fusion
            spike_pattern[i] = spike_probability * phase.cos();
        }

        Ok(spike_pattern)
    }

    /// Convert neuromorphic spikes to quantum states
    pub fn neuromorphic_to_quantum(&self, spikedata: &Array1<F>) -> Result<Array1<Complex<F>>> {
        let mut quantum_states = Array1::zeros(spikedata.len());

        for (i, &spike_value) in spikedata.iter().enumerate() {
            // Convert spike to quantum amplitude and phase
            let amplitude = spike_value.abs();
            let phase = if spike_value >= F::zero() {
                F::zero()
            } else {
                F::from_f64(std::f64::consts::PI).unwrap()
            };

            // Quantum-modulated spike generation
            let quantum_amplitude = amplitude * F::from_f64(0.8).unwrap();

            // Quantum interference effects on spike timing
            let quantum_phase = phase + F::from_f64(std::f64::consts::PI / 4.0).unwrap();

            // Create complex quantum state
            quantum_states[i] = Complex::new(
                quantum_amplitude * quantum_phase.cos(),
                quantum_amplitude * quantum_phase.sin(),
            );
        }

        Ok(quantum_states)
    }

    /// Quantum confidence estimation using uncertainty principles
    pub fn estimate_quantum_confidence(&self, data: &Array1<F>) -> Result<F> {
        if data.is_empty() {
            return Ok(F::zero());
        }

        // Quantum uncertainty calculation
        let mean =
            data.iter().fold(F::zero(), |acc, &x| acc + x) / F::from_usize(data.len()).unwrap();
        let variance = data
            .iter()
            .fold(F::zero(), |acc, &x| acc + (x - mean) * (x - mean))
            / F::from_usize(data.len()).unwrap();

        // Quantum confidence based on uncertainty principle
        let uncertainty = variance.sqrt();
        let max_confidence = F::from_f64(1.0).unwrap();
        let confidence = max_confidence / (F::from_f64(1.0).unwrap() + uncertainty);

        Ok(confidence)
    }
}

/// Distributed quantum coordinator for network management
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct DistributedQuantumCoordinator<F: Float + Debug> {
    /// Network topology configuration
    network_topology: QuantumNetworkTopology,
    /// Node managers for each quantum node
    node_managers: HashMap<usize, QuantumNodeManager<F>>,
    /// Entanglement management system
    entanglement_manager: EntanglementManager<F>,
    /// Communication protocols
    communication_protocols: QuantumCommunicationProtocols<F>,
    /// Load balancing system
    load_balancer: QuantumLoadBalancer<F>,
}

impl<F: Float + Debug + Clone + FromPrimitive> DistributedQuantumCoordinator<F> {
    /// Creates a new DistributedQuantumCoordinator with default configuration
    pub fn new() -> Result<Self> {
        Ok(DistributedQuantumCoordinator {
            network_topology: QuantumNetworkTopology {
                nodes: vec![0, 1, 2, 3], // 4 quantum nodes by default
                quantum_channels: Vec::new(),
                topology_type: NetworkTopologyType::FullyConnected,
                coherence_time: 100.0, // 100 microseconds
                entanglement_fidelity: 0.95,
            },
            node_managers: HashMap::new(),
            entanglement_manager: EntanglementManager {
                entangled_pairs: Vec::new(),
                fidelity_threshold: F::from_f64(0.9).unwrap(),
                purification_protocol: "BBPSSW".to_string(),
            },
            communication_protocols: QuantumCommunicationProtocols {
                protocols: Vec::new(),
                security_level: SecurityLevel::Quantum,
                encryption_keys: Vec::new(),
            },
            load_balancer: QuantumLoadBalancer {
                load_metrics: Vec::new(),
                balancing_algorithm: LoadBalancingAlgorithm::QuantumOptimal,
                quantum_state_sharing: true,
            },
        })
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> QuantumNeuromorphicCore<F> {
    /// Create new quantum-neuromorphic fusion core
    pub fn new(core_id: usize, qubits: usize) -> Result<Self> {
        Ok(QuantumNeuromorphicCore {
            core_id,
            quantum_unit: QuantumProcessingUnit::new(qubits)?,
            neuromorphic_unit: super::neuromorphic::NeuromorphicProcessingUnit::new()?,
            fusion_interface: QuantumNeuromorphicInterface::new()?,
            performance_metrics: HashMap::new(),
        })
    }

    /// Process data using quantum-neuromorphic fusion
    pub fn process_fusion(&mut self, data: &Array1<F>) -> Result<Array1<F>> {
        // 1. Quantum processing phase
        let quantum_result = self.quantum_unit.process_quantum(data)?;

        // 2. Convert quantum result to neuromorphic input
        let quantum_complex: Array1<Complex<F>> =
            quantum_result.mapv(|x| Complex::new(x, F::zero()));
        let neuromorphic_input = self
            .fusion_interface
            .quantum_to_neuromorphic(&quantum_complex)?;

        // 3. Neuromorphic processing phase
        let neuromorphic_result = self.neuromorphic_unit.process_spikes(&neuromorphic_input)?;

        // 4. Fusion and optimization
        let fusion_result = self.fuse_results(&quantum_result, &neuromorphic_result)?;

        Ok(fusion_result)
    }

    /// Fuse quantum and neuromorphic results
    fn fuse_results(&self, quantum: &Array1<F>, neuromorphic: &Array1<F>) -> Result<Array1<F>> {
        let mut result = Array1::zeros(quantum.len().min(neuromorphic.len()));

        for i in 0..result.len() {
            // Weighted fusion of quantum and neuromorphic results
            let quantum_weight = F::from_f64(0.6).unwrap();
            let neuromorphic_weight = F::from_f64(0.4).unwrap();

            result[i] = quantum[i] * quantum_weight + neuromorphic[i] * neuromorphic_weight;
        }

        Ok(result)
    }
}

/// Quantum analysis result container
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct QuantumAnalysisResult<F: Float> {
    /// Quantum coherence measures
    pub coherence_metrics: Vec<F>,
    /// Entanglement strengths
    pub entanglement_measures: Vec<F>,
    /// Quantum interference patterns
    pub interference_patterns: Vec<F>,
    /// Measurement uncertainties
    pub measurement_uncertainties: Vec<F>,
    /// Quantum correlation functions
    pub correlation_functions: Vec<F>,
}

impl<F: Float> Default for QuantumAnalysisResult<F> {
    fn default() -> Self {
        Self {
            coherence_metrics: Vec::new(),
            entanglement_measures: Vec::new(),
            interference_patterns: Vec::new(),
            measurement_uncertainties: Vec::new(),
            correlation_functions: Vec::new(),
        }
    }
}