//! Quantum-Inspired Optimization for Tensor Operations
//!
//! This module provides quantum-inspired optimization algorithms for enhancing
//! tensor core performance through variational principles and quantum-like
//! state management in classical systems.

use crate::error::CoreResult;
use std::time::Duration;

/// Quantum-inspired optimization engine for advanced tensor operations
#[allow(dead_code)]
#[derive(Debug)]
pub struct QuantumInspiredOptimizer {
    /// Quantum state approximation
    quantum_state: QuantumStateApproximation,
    /// Variational parameters
    variational_params: Vec<f64>,
    /// Optimization history
    optimization_history: Vec<OptimizationStep>,
    /// Entanglement patterns
    entanglement_patterns: Vec<EntanglementPattern>,
}

/// Quantum state approximation for classical systems
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct QuantumStateApproximation {
    /// State amplitudes
    amplitudes: Vec<f64>,
    /// Phase information
    phases: Vec<f64>,
    /// Coherence time
    coherence_time: Duration,
    /// Decoherence rate
    decoherence_rate: f64,
}

/// Optimization step in quantum-inspired algorithm
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct OptimizationStep {
    /// Step number
    step: usize,
    /// Parameter values
    parameters: Vec<f64>,
    /// Objective function value
    objective_value: f64,
    /// Gradient estimate
    gradient: Vec<f64>,
    /// Uncertainty estimate
    uncertainty: f64,
}

/// Entanglement pattern for optimization
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct EntanglementPattern {
    /// Connected parameter indices
    connected_params: Vec<usize>,
    /// Entanglement strength
    strength: f64,
    /// Pattern type
    pattern_type: EntanglementType,
}

/// Types of entanglement patterns
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum EntanglementType {
    Bipartite,
    Multipartite,
    GHZ,
    Bell,
    Custom(String),
}

impl QuantumInspiredOptimizer {
    /// Create a new quantum-inspired optimizer
    pub fn new(numparams: usize) -> CoreResult<Self> {
        let quantum_state = QuantumStateApproximation {
            amplitudes: vec![1.0 / (numparams as f64).sqrt(); numparams],
            phases: vec![0.0; numparams],
            coherence_time: Duration::from_millis(100),
            decoherence_rate: 0.001,
        };

        Ok(Self {
            quantum_state,
            variational_params: vec![0.0; numparams],
            optimization_history: Vec::new(),
            entanglement_patterns: Vec::new(),
        })
    }

    /// Perform quantum-inspired optimization step
    pub fn optimize_step(
        &mut self,
        objective_function: &dyn Fn(&[f64]) -> f64,
        learningrate: f64,
    ) -> CoreResult<OptimizationStep> {
        // Quantum-inspired parameter update using variational principles
        let mut new_params = self.variational_params.clone();
        let mut gradient = vec![0.0; new_params.len()];

        // Estimate gradient using quantum-inspired finite differences
        for i in 0..new_params.len() {
            let h = 1e-5;

            // Forward difference with quantum amplitude weighting
            let mut params_plus = new_params.clone();
            params_plus[i] += h * self.quantum_state.amplitudes[i].abs();
            let f_plus = objective_function(&params_plus);

            // Backward difference
            let mut params_minus = new_params.clone();
            params_minus[i] -= h * self.quantum_state.amplitudes[i].abs();
            let f_minus = objective_function(&params_minus);

            gradient[i] = (f_plus - f_minus) / (2.0 * h * self.quantum_state.amplitudes[i].abs());
        }

        // Update parameters with quantum-inspired learning rate scaling
        for i in 0..new_params.len() {
            let quantum_scaling =
                self.quantum_state.amplitudes[i].abs() * (1.0 + self.quantum_state.phases[i].cos());
            new_params[i] -= learningrate * gradient[i] * quantum_scaling;
        }

        // Update quantum state (simulate decoherence)
        for (i, grad) in gradient
            .iter()
            .enumerate()
            .take(self.quantum_state.amplitudes.len())
        {
            self.quantum_state.amplitudes[i] *= (1.0 - self.decoherence_rate() * 0.01).max(0.1f64);
            self.quantum_state.phases[i] += 0.01 * grad; // Phase evolution
        }

        let current_value = objective_function(&new_params);
        let uncertainty = gradient.iter().map(|g| g.powi(2)).sum::<f64>().sqrt();

        let step = OptimizationStep {
            step: self.optimization_history.len(),
            parameters: new_params.clone(),
            objective_value: current_value,
            gradient,
            uncertainty,
        };

        self.variational_params = new_params;
        self.optimization_history.push(step.clone());

        Ok(step)
    }

    /// Add entanglement pattern between parameters
    pub fn add_entanglement(
        &mut self,
        param_indices: Vec<usize>,
        strength: f64,
        pattern_type: EntanglementType,
    ) -> CoreResult<()> {
        let pattern = EntanglementPattern {
            connected_params: param_indices,
            strength,
            pattern_type,
        };

        self.entanglement_patterns.push(pattern);
        Ok(())
    }

    /// Get current parameters
    pub fn get_parameters(&self) -> &[f64] {
        &self.variational_params
    }

    /// Get optimization history
    pub fn get_history(&self) -> &[OptimizationStep] {
        &self.optimization_history
    }

    /// Get convergence metrics
    pub fn get_convergence_metrics(&self) -> ConvergenceMetrics {
        if self.optimization_history.is_empty() {
            return ConvergenceMetrics::default();
        }

        let current_value = self
            .optimization_history
            .last()
            .expect("Operation failed")
            .objective_value;
        let best_value = self
            .optimization_history
            .iter()
            .map(|step| step.objective_value)
            .fold(f64::INFINITY, f64::min);

        let convergence_rate = if self.optimization_history.len() > 1 {
            let recent_steps = self.optimization_history.len().min(10);
            let start_idx = self.optimization_history.len() - recent_steps;
            let start_value = self.optimization_history[start_idx].objective_value;
            (start_value - current_value) / (recent_steps as f64)
        } else {
            0.0
        };

        ConvergenceMetrics {
            best_objective_value: best_value,
            current_objective_value: current_value,
            convergence_rate,
            optimization_steps: self.optimization_history.len(),
            quantum_coherence: self.quantum_state.amplitudes.iter().map(|a| a.abs()).sum(),
        }
    }
}

/// Convergence metrics for quantum-inspired optimization
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct ConvergenceMetrics {
    /// Best objective value found
    pub best_objective_value: f64,
    /// Current objective value
    pub current_objective_value: f64,
    /// Convergence rate
    pub convergence_rate: f64,
    /// Number of optimization steps
    pub optimization_steps: usize,
    /// Quantum coherence measure
    pub quantum_coherence: f64,
}

impl Default for ConvergenceMetrics {
    fn default() -> Self {
        Self {
            best_objective_value: f64::INFINITY,
            current_objective_value: f64::INFINITY,
            convergence_rate: 0.0,
            optimization_steps: 0,
            quantum_coherence: 0.0,
        }
    }
}

impl QuantumInspiredOptimizer {
    /// Access to decoherence rate for internal calculations
    pub fn decoherence_rate(&self) -> f64 {
        self.quantum_state.decoherence_rate
    }
}

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

    #[test]
    fn test_quantum_optimizer_creation() {
        let optimizer = QuantumInspiredOptimizer::new(10);
        assert!(optimizer.is_ok());

        let opt = optimizer.expect("Operation failed");
        assert_eq!(opt.get_parameters().len(), 10);
        assert_eq!(opt.get_history().len(), 0);
    }

    #[test]
    fn test_optimization_step() {
        let mut optimizer = QuantumInspiredOptimizer::new(2).expect("Operation failed");

        // Simple quadratic function: f(x) = x^2 + y^2
        let objective = |params: &[f64]| params.iter().map(|x| x.powi(2)).sum();

        let step = optimizer.optimize_step(&objective, 0.01);
        assert!(step.is_ok());

        let step = step.expect("Operation failed");
        assert_eq!(step.step, 0);
        assert_eq!(step.parameters.len(), 2);
        assert!(step.objective_value >= 0.0);
    }

    #[test]
    fn test_entanglement_pattern() {
        let mut optimizer = QuantumInspiredOptimizer::new(4).expect("Operation failed");

        let result = optimizer.add_entanglement(vec![0, 1], 0.5, EntanglementType::Bipartite);
        assert!(result.is_ok());
        assert_eq!(optimizer.entanglement_patterns.len(), 1);
    }

    #[test]
    fn test_convergence_metrics() {
        let optimizer = QuantumInspiredOptimizer::new(2).expect("Operation failed");
        let metrics = optimizer.get_convergence_metrics();

        // Empty optimizer should have default metrics
        assert_eq!(metrics.optimization_steps, 0);
        assert_eq!(metrics.best_objective_value, f64::INFINITY);
        assert_eq!(metrics.convergence_rate, 0.0);
    }
}
