quantrs2-circuit 0.1.3

//! `SciRS2` optimization integration for parameter tuning
//!
//! This module integrates `SciRS2`'s advanced optimization capabilities for quantum circuit
//! parameter optimization, variational algorithms, and machine learning-enhanced optimization.

use crate::builder::Circuit;
use crate::scirs2_matrices::SparseMatrix;
use quantrs2_core::{
    error::{QuantRS2Error, QuantRS2Result},
    gate::GateOp,
    qubit::QubitId,
};
use scirs2_core::Complex64;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::sync::{Arc, Mutex};

// Placeholder types representing SciRS2 optimization interface
// In the real implementation, these would be imported from SciRS2

/// Optimization objective function
pub trait ObjectiveFunction: Send + Sync {
    /// Evaluate the objective at given parameters
    fn evaluate(&self, parameters: &[f64]) -> f64;

    /// Compute gradient if available
    fn gradient(&self, parameters: &[f64]) -> Option<Vec<f64>> {
        None
    }

    /// Compute Hessian if available
    fn hessian(&self, parameters: &[f64]) -> Option<Vec<Vec<f64>>> {
        None
    }

    /// Get parameter bounds
    fn bounds(&self) -> Vec<(f64, f64)>;

    /// Get objective name
    fn name(&self) -> &str;
}

/// `SciRS2` optimization algorithms
#[derive(Debug, Clone, PartialEq)]
pub enum OptimizationAlgorithm {
    /// Gradient descent variants
    GradientDescent { learning_rate: f64, momentum: f64 },
    /// Adam optimizer
    Adam {
        learning_rate: f64,
        beta1: f64,
        beta2: f64,
        epsilon: f64,
    },
    /// L-BFGS-B
    LBFGSB {
        max_iterations: usize,
        tolerance: f64,
    },
    /// Nelder-Mead simplex
    NelderMead {
        max_iterations: usize,
        tolerance: f64,
    },
    /// Simulated annealing
    SimulatedAnnealing {
        initial_temperature: f64,
        cooling_rate: f64,
        min_temperature: f64,
    },
    /// Genetic algorithm
    GeneticAlgorithm {
        population_size: usize,
        mutation_rate: f64,
        crossover_rate: f64,
    },
    /// Particle swarm optimization
    ParticleSwarm {
        num_particles: usize,
        inertia_weight: f64,
        cognitive_weight: f64,
        social_weight: f64,
    },
    /// Bayesian optimization
    BayesianOptimization {
        acquisition_function: AcquisitionFunction,
        kernel: KernelType,
        num_initial_samples: usize,
    },
    /// Quantum approximate optimization algorithm (QAOA)
    QAOA {
        num_layers: usize,
        classical_optimizer: Box<Self>,
    },
}

/// Acquisition functions for Bayesian optimization
#[derive(Debug, Clone, PartialEq)]
pub enum AcquisitionFunction {
    ExpectedImprovement,
    ProbabilityOfImprovement,
    UpperConfidenceBound { kappa: f64 },
    Thompson,
}

/// Kernel types for Gaussian processes
#[derive(Debug, Clone, PartialEq)]
pub enum KernelType {
    RBF { length_scale: f64 },
    Matern { nu: f64, length_scale: f64 },
    Linear { variance: f64 },
    Periodic { period: f64, length_scale: f64 },
}

/// Optimization configuration
pub struct OptimizationConfig {
    /// Optimization algorithm
    pub algorithm: OptimizationAlgorithm,
    /// Maximum number of function evaluations
    pub max_evaluations: usize,
    /// Convergence tolerance
    pub tolerance: f64,
    /// Random seed for reproducibility
    pub seed: Option<u64>,
    /// Parallel evaluation of objective
    pub parallel: bool,
    /// Number of threads for parallel evaluation
    pub num_threads: Option<usize>,
    /// Progress callback
    pub progress_callback: Option<Box<dyn Fn(usize, f64) + Send + Sync>>,
    /// Early stopping criteria
    pub early_stopping: Option<EarlyStoppingCriteria>,
}

impl std::fmt::Debug for OptimizationConfig {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        f.debug_struct("OptimizationConfig")
            .field("algorithm", &self.algorithm)
            .field("max_evaluations", &self.max_evaluations)
            .field("tolerance", &self.tolerance)
            .field("seed", &self.seed)
            .field("parallel", &self.parallel)
            .field("num_threads", &self.num_threads)
            .field(
                "progress_callback",
                &self.progress_callback.as_ref().map(|_| "Some(callback)"),
            )
            .field("early_stopping", &self.early_stopping)
            .finish()
    }
}

impl Clone for OptimizationConfig {
    fn clone(&self) -> Self {
        Self {
            algorithm: self.algorithm.clone(),
            max_evaluations: self.max_evaluations,
            tolerance: self.tolerance,
            seed: self.seed,
            parallel: self.parallel,
            num_threads: self.num_threads,
            progress_callback: None, // Function pointers can't be cloned
            early_stopping: self.early_stopping.clone(),
        }
    }
}

/// Early stopping criteria
#[derive(Debug, Clone)]
pub struct EarlyStoppingCriteria {
    /// Patience (number of iterations without improvement)
    pub patience: usize,
    /// Minimum change to be considered an improvement
    pub min_delta: f64,
    /// Monitor best value or last value
    pub monitor_best: bool,
}

/// Optimization result
#[derive(Debug, Clone)]
pub struct OptimizationResult {
    /// Optimal parameters
    pub optimal_parameters: Vec<f64>,
    /// Optimal objective value
    pub optimal_value: f64,
    /// Number of function evaluations
    pub num_evaluations: usize,
    /// Convergence status
    pub converged: bool,
    /// Optimization history
    pub history: OptimizationHistory,
    /// Additional algorithm-specific information
    pub algorithm_info: HashMap<String, String>,
    /// Total optimization time
    pub optimization_time: std::time::Duration,
}

/// Optimization history tracking
#[derive(Debug, Clone)]
pub struct OptimizationHistory {
    /// Parameter values at each iteration
    pub parameters: Vec<Vec<f64>>,
    /// Objective values at each iteration
    pub objective_values: Vec<f64>,
    /// Gradient norms (if available)
    pub gradient_norms: Vec<f64>,
    /// Step sizes
    pub step_sizes: Vec<f64>,
    /// Timestamps
    pub timestamps: Vec<std::time::Instant>,
}

/// Quantum circuit parameter optimizer using `SciRS2`
pub struct QuantumCircuitOptimizer {
    /// Current circuit template
    circuit_template: CircuitTemplate,
    /// Optimization configuration
    config: OptimizationConfig,
    /// Parameter history
    history: Arc<Mutex<OptimizationHistory>>,
    /// Best parameters found so far
    best_parameters: Arc<Mutex<Option<Vec<f64>>>>,
    /// Best objective value
    best_value: Arc<Mutex<f64>>,
}

/// Parameterized circuit template
#[derive(Debug, Clone)]
pub struct CircuitTemplate {
    /// Circuit structure with parameter placeholders
    pub structure: Vec<ParameterizedGate>,
    /// Parameter names and bounds
    pub parameters: Vec<Parameter>,
    /// Number of qubits
    pub num_qubits: usize,
}

/// Parameterized gate in circuit template
#[derive(Debug, Clone)]
pub struct ParameterizedGate {
    /// Gate name
    pub gate_name: String,
    /// Qubits the gate acts on
    pub qubits: Vec<usize>,
    /// Parameter indices
    pub parameter_indices: Vec<usize>,
    /// Fixed parameters (if any)
    pub fixed_parameters: Vec<f64>,
}

/// Parameter definition
#[derive(Debug, Clone)]
pub struct Parameter {
    /// Parameter name
    pub name: String,
    /// Lower bound
    pub lower_bound: f64,
    /// Upper bound
    pub upper_bound: f64,
    /// Initial value
    pub initial_value: f64,
    /// Whether parameter is discrete
    pub discrete: bool,
}

impl QuantumCircuitOptimizer {
    /// Create a new quantum circuit optimizer
    #[must_use]
    pub fn new(template: CircuitTemplate, config: OptimizationConfig) -> Self {
        Self {
            circuit_template: template,
            config,
            history: Arc::new(Mutex::new(OptimizationHistory {
                parameters: Vec::new(),
                objective_values: Vec::new(),
                gradient_norms: Vec::new(),
                step_sizes: Vec::new(),
                timestamps: Vec::new(),
            })),
            best_parameters: Arc::new(Mutex::new(None)),
            best_value: Arc::new(Mutex::new(f64::INFINITY)),
        }
    }

    /// Optimize circuit parameters
    pub fn optimize(
        &mut self,
        objective: Arc<dyn ObjectiveFunction>,
    ) -> QuantRS2Result<OptimizationResult> {
        let start_time = std::time::Instant::now();

        // Get initial parameters
        let initial_params: Vec<f64> = self
            .circuit_template
            .parameters
            .iter()
            .map(|p| p.initial_value)
            .collect();

        // Validate parameter bounds
        let bounds = objective.bounds();
        if bounds.len() != initial_params.len() {
            return Err(QuantRS2Error::InvalidInput(
                "Parameter count mismatch with bounds".to_string(),
            ));
        }

        // Run optimization based on algorithm
        let result = match &self.config.algorithm {
            OptimizationAlgorithm::GradientDescent {
                learning_rate,
                momentum,
            } => self.optimize_gradient_descent(
                objective,
                &initial_params,
                *learning_rate,
                *momentum,
            ),
            OptimizationAlgorithm::Adam {
                learning_rate,
                beta1,
                beta2,
                epsilon,
            } => self.optimize_adam(
                objective,
                &initial_params,
                *learning_rate,
                *beta1,
                *beta2,
                *epsilon,
            ),
            OptimizationAlgorithm::LBFGSB {
                max_iterations,
                tolerance,
            } => self.optimize_lbfgs(objective, &initial_params, *max_iterations, *tolerance),
            OptimizationAlgorithm::NelderMead {
                max_iterations,
                tolerance,
            } => self.optimize_nelder_mead(objective, &initial_params, *max_iterations, *tolerance),
            OptimizationAlgorithm::SimulatedAnnealing {
                initial_temperature,
                cooling_rate,
                min_temperature,
            } => self.optimize_simulated_annealing(
                objective,
                &initial_params,
                *initial_temperature,
                *cooling_rate,
                *min_temperature,
            ),
            OptimizationAlgorithm::BayesianOptimization {
                acquisition_function,
                kernel,
                num_initial_samples,
            } => self.optimize_bayesian(
                objective,
                &initial_params,
                acquisition_function,
                kernel,
                *num_initial_samples,
            ),
            _ => Err(QuantRS2Error::InvalidInput(
                "Algorithm not yet implemented".to_string(),
            )),
        }?;

        let history = self
            .history
            .lock()
            .map_err(|e| QuantRS2Error::RuntimeError(format!("Failed to lock history: {}", e)))?
            .clone();

        Ok(OptimizationResult {
            optimal_parameters: result.0,
            optimal_value: result.1,
            num_evaluations: result.2,
            converged: result.3,
            history,
            algorithm_info: HashMap::new(),
            optimization_time: start_time.elapsed(),
        })
    }

    /// Gradient descent optimization
    fn optimize_gradient_descent(
        &self,
        objective: Arc<dyn ObjectiveFunction>,
        initial_params: &[f64],
        learning_rate: f64,
        momentum: f64,
    ) -> QuantRS2Result<(Vec<f64>, f64, usize, bool)> {
        let mut params = initial_params.to_vec();
        let mut velocity = vec![0.0; params.len()];
        let mut evaluations = 0;
        let mut best_value = f64::INFINITY;

        for iteration in 0..self.config.max_evaluations {
            // Evaluate objective
            let value = objective.evaluate(&params);
            evaluations += 1;

            // Update best
            if value < best_value {
                best_value = value;
                if let Ok(mut guard) = self.best_parameters.lock() {
                    *guard = Some(params.clone());
                }
                if let Ok(mut guard) = self.best_value.lock() {
                    *guard = best_value;
                }
            }

            // Record history
            self.record_iteration(&params, value, iteration);

            // Check convergence
            if iteration > 0 {
                let prev_value = self
                    .history
                    .lock()
                    .ok()
                    .and_then(|h| h.objective_values.get(iteration - 1).copied())
                    .unwrap_or(value);
                if (prev_value - value).abs() < self.config.tolerance {
                    return Ok((params, best_value, evaluations, true));
                }
            }

            // Compute gradient (numerical if not available)
            let gradient = if let Some(grad) = objective.gradient(&params) {
                grad
            } else {
                self.numerical_gradient(&*objective, &params)?
            };

            // Update parameters with momentum
            for i in 0..params.len() {
                velocity[i] = momentum.mul_add(velocity[i], -(learning_rate * gradient[i]));
                params[i] += velocity[i];

                // Apply bounds
                let bounds = objective.bounds();
                params[i] = params[i].max(bounds[i].0).min(bounds[i].1);
            }

            // Progress callback
            if let Some(callback) = &self.config.progress_callback {
                callback(iteration, value);
            }
        }

        Ok((params, best_value, evaluations, false))
    }

    /// Adam optimization algorithm
    fn optimize_adam(
        &self,
        objective: Arc<dyn ObjectiveFunction>,
        initial_params: &[f64],
        learning_rate: f64,
        beta1: f64,
        beta2: f64,
        epsilon: f64,
    ) -> QuantRS2Result<(Vec<f64>, f64, usize, bool)> {
        let mut params = initial_params.to_vec();
        let mut m = vec![0.0; params.len()]; // First moment
        let mut v = vec![0.0; params.len()]; // Second moment
        let mut evaluations = 0;
        let mut best_value = f64::INFINITY;

        for iteration in 0..self.config.max_evaluations {
            let t = iteration + 1;

            // Evaluate objective
            let value = objective.evaluate(&params);
            evaluations += 1;

            // Update best
            if value < best_value {
                best_value = value;
                if let Ok(mut guard) = self.best_parameters.lock() {
                    *guard = Some(params.clone());
                }
                if let Ok(mut guard) = self.best_value.lock() {
                    *guard = best_value;
                }
            }

            // Record history
            self.record_iteration(&params, value, iteration);

            // Check convergence
            if iteration > 0 {
                let prev_value = self
                    .history
                    .lock()
                    .ok()
                    .and_then(|h| h.objective_values.get(iteration - 1).copied())
                    .unwrap_or(value);
                if (prev_value - value).abs() < self.config.tolerance {
                    return Ok((params, best_value, evaluations, true));
                }
            }

            // Compute gradient
            let gradient = if let Some(grad) = objective.gradient(&params) {
                grad
            } else {
                self.numerical_gradient(&*objective, &params)?
            };

            // Update biased first and second moment estimates
            for i in 0..params.len() {
                m[i] = beta1.mul_add(m[i], (1.0 - beta1) * gradient[i]);
                v[i] = beta2.mul_add(v[i], (1.0 - beta2) * gradient[i] * gradient[i]);

                // Bias correction
                let m_hat = m[i] / (1.0 - beta1.powi(t as i32));
                let v_hat = v[i] / (1.0 - beta2.powi(t as i32));

                // Update parameters
                params[i] -= learning_rate * m_hat / (v_hat.sqrt() + epsilon);

                // Apply bounds
                let bounds = objective.bounds();
                params[i] = params[i].max(bounds[i].0).min(bounds[i].1);
            }

            // Progress callback
            if let Some(callback) = &self.config.progress_callback {
                callback(iteration, value);
            }
        }

        Ok((params, best_value, evaluations, false))
    }

    /// L-BFGS-B optimization (simplified implementation)
    fn optimize_lbfgs(
        &self,
        objective: Arc<dyn ObjectiveFunction>,
        initial_params: &[f64],
        max_iterations: usize,
        tolerance: f64,
    ) -> QuantRS2Result<(Vec<f64>, f64, usize, bool)> {
        // This is a simplified placeholder for L-BFGS-B
        // In practice, this would use SciRS2's optimized implementation
        self.optimize_gradient_descent(objective, initial_params, 0.01, 0.9)
    }

    /// Nelder-Mead simplex optimization
    fn optimize_nelder_mead(
        &self,
        objective: Arc<dyn ObjectiveFunction>,
        initial_params: &[f64],
        max_iterations: usize,
        tolerance: f64,
    ) -> QuantRS2Result<(Vec<f64>, f64, usize, bool)> {
        let n = initial_params.len();
        let mut simplex = Vec::new();
        let mut evaluations = 0;

        // Initialize simplex
        simplex.push(initial_params.to_vec());
        for i in 0..n {
            let mut vertex = initial_params.to_vec();
            vertex[i] += if vertex[i] == 0.0 {
                0.00025
            } else {
                vertex[i] * 0.05
            };
            simplex.push(vertex);
        }

        // Evaluate initial simplex
        let mut values: Vec<f64> = simplex
            .iter()
            .map(|params| {
                evaluations += 1;
                objective.evaluate(params)
            })
            .collect();

        for iteration in 0..max_iterations {
            // Sort simplex by objective values
            let mut indices: Vec<usize> = (0..simplex.len()).collect();
            indices.sort_by(|&i, &j| {
                values[i]
                    .partial_cmp(&values[j])
                    .unwrap_or(std::cmp::Ordering::Equal)
            });

            let best_value = values[indices[0]];
            let worst_idx = indices[n];
            let second_worst_idx = indices[n - 1];

            // Record best iteration
            self.record_iteration(&simplex[indices[0]], best_value, iteration);

            // Check convergence
            let range = values[worst_idx] - values[indices[0]];
            if range < tolerance {
                return Ok((simplex[indices[0]].clone(), best_value, evaluations, true));
            }

            // Compute centroid (excluding worst point)
            let mut centroid = vec![0.0; n];
            for i in 0..n {
                for j in 0..n {
                    centroid[j] += simplex[indices[i]][j];
                }
            }
            for j in 0..n {
                centroid[j] /= n as f64;
            }

            // Reflection
            let alpha = 1.0;
            let mut reflected = vec![0.0; n];
            for j in 0..n {
                reflected[j] = centroid[j] + alpha * (centroid[j] - simplex[worst_idx][j]);
            }

            // Apply bounds
            let bounds = objective.bounds();
            for j in 0..n {
                reflected[j] = reflected[j].max(bounds[j].0).min(bounds[j].1);
            }

            let reflected_value = objective.evaluate(&reflected);
            evaluations += 1;

            if values[indices[0]] <= reflected_value && reflected_value < values[second_worst_idx] {
                // Accept reflection
                simplex[worst_idx] = reflected;
                values[worst_idx] = reflected_value;
            } else if reflected_value < values[indices[0]] {
                // Expansion
                let gamma = 2.0;
                let mut expanded = vec![0.0; n];
                for j in 0..n {
                    expanded[j] = centroid[j] + gamma * (reflected[j] - centroid[j]);
                    expanded[j] = expanded[j].max(bounds[j].0).min(bounds[j].1);
                }

                let expanded_value = objective.evaluate(&expanded);
                evaluations += 1;

                if expanded_value < reflected_value {
                    simplex[worst_idx] = expanded;
                    values[worst_idx] = expanded_value;
                } else {
                    simplex[worst_idx] = reflected;
                    values[worst_idx] = reflected_value;
                }
            } else {
                // Contraction
                let rho = 0.5;
                let mut contracted = vec![0.0; n];
                for j in 0..n {
                    contracted[j] = centroid[j] + rho * (simplex[worst_idx][j] - centroid[j]);
                    contracted[j] = contracted[j].max(bounds[j].0).min(bounds[j].1);
                }

                let contracted_value = objective.evaluate(&contracted);
                evaluations += 1;

                if contracted_value < values[worst_idx] {
                    simplex[worst_idx] = contracted;
                    values[worst_idx] = contracted_value;
                } else {
                    // Shrink
                    let sigma = 0.5;
                    for i in 1..=n {
                        for j in 0..n {
                            simplex[i][j] = simplex[indices[0]][j]
                                + sigma * (simplex[i][j] - simplex[indices[0]][j]);
                            simplex[i][j] = simplex[i][j].max(bounds[j].0).min(bounds[j].1);
                        }
                        values[i] = objective.evaluate(&simplex[i]);
                        evaluations += 1;
                    }
                }
            }

            // Progress callback
            if let Some(callback) = &self.config.progress_callback {
                callback(iteration, best_value);
            }
        }

        // Find best point
        let mut best_idx = 0;
        let mut best_value = values[0];
        for i in 1..values.len() {
            if values[i] < best_value {
                best_value = values[i];
                best_idx = i;
            }
        }

        Ok((simplex[best_idx].clone(), best_value, evaluations, false))
    }

    /// Simulated annealing optimization
    fn optimize_simulated_annealing(
        &self,
        objective: Arc<dyn ObjectiveFunction>,
        initial_params: &[f64],
        initial_temperature: f64,
        cooling_rate: f64,
        min_temperature: f64,
    ) -> QuantRS2Result<(Vec<f64>, f64, usize, bool)> {
        use scirs2_core::random::prelude::*;
        let mut rng = thread_rng();

        let mut current_params = initial_params.to_vec();
        let mut current_value = objective.evaluate(&current_params);
        let mut best_params = current_params.clone();
        let mut best_value = current_value;
        let mut temperature = initial_temperature;
        let mut evaluations = 1;

        let bounds = objective.bounds();

        for iteration in 0..self.config.max_evaluations {
            if temperature < min_temperature {
                break;
            }

            // Generate neighbor solution
            let mut neighbor_params = current_params.clone();
            for i in 0..neighbor_params.len() {
                let range = bounds[i].1 - bounds[i].0;
                let step = rng.random_range(-0.1..0.1) * range * temperature / initial_temperature;
                neighbor_params[i] = (neighbor_params[i] + step)
                    .max(bounds[i].0)
                    .min(bounds[i].1);
            }

            let neighbor_value = objective.evaluate(&neighbor_params);
            evaluations += 1;

            // Accept or reject based on Metropolis criterion
            let delta = neighbor_value - current_value;
            if delta < 0.0 || rng.random::<f64>() < (-delta / temperature).exp() {
                current_params = neighbor_params;
                current_value = neighbor_value;

                if current_value < best_value {
                    best_params.clone_from(&current_params);
                    best_value = current_value;
                }
            }

            // Record iteration
            self.record_iteration(&current_params, current_value, iteration);

            // Cool down
            temperature *= cooling_rate;

            // Progress callback
            if let Some(callback) = &self.config.progress_callback {
                callback(iteration, best_value);
            }
        }

        Ok((
            best_params,
            best_value,
            evaluations,
            temperature < min_temperature,
        ))
    }

    /// Bayesian optimization (simplified implementation)
    fn optimize_bayesian(
        &self,
        objective: Arc<dyn ObjectiveFunction>,
        initial_params: &[f64],
        acquisition_function: &AcquisitionFunction,
        kernel: &KernelType,
        num_initial_samples: usize,
    ) -> QuantRS2Result<(Vec<f64>, f64, usize, bool)> {
        // This is a simplified placeholder for Bayesian optimization
        // Real implementation would use SciRS2's Gaussian process implementation
        self.optimize_nelder_mead(
            objective,
            initial_params,
            self.config.max_evaluations,
            self.config.tolerance,
        )
    }

    /// Compute numerical gradient
    fn numerical_gradient(
        &self,
        objective: &dyn ObjectiveFunction,
        params: &[f64],
    ) -> QuantRS2Result<Vec<f64>> {
        let epsilon = 1e-8;
        let mut gradient = vec![0.0; params.len()];

        for i in 0..params.len() {
            let mut params_plus = params.to_vec();
            let mut params_minus = params.to_vec();

            params_plus[i] += epsilon;
            params_minus[i] -= epsilon;

            let f_plus = objective.evaluate(&params_plus);
            let f_minus = objective.evaluate(&params_minus);

            gradient[i] = (f_plus - f_minus) / (2.0 * epsilon);
        }

        Ok(gradient)
    }

    /// Record optimization iteration
    fn record_iteration(&self, params: &[f64], value: f64, iteration: usize) {
        if let Ok(mut history) = self.history.lock() {
            history.parameters.push(params.to_vec());
            history.objective_values.push(value);
            history.gradient_norms.push(0.0); // Placeholder
            history.step_sizes.push(0.0); // Placeholder
            history.timestamps.push(std::time::Instant::now());
        }
    }

    /// Get current best parameters
    #[must_use]
    pub fn get_best_parameters(&self) -> Option<Vec<f64>> {
        self.best_parameters.lock().ok().and_then(|g| g.clone())
    }

    /// Get current best value
    #[must_use]
    pub fn get_best_value(&self) -> f64 {
        self.best_value.lock().ok().map_or(f64::INFINITY, |g| *g)
    }

    /// Build circuit from parameters
    pub fn build_circuit(&self, parameters: &[f64]) -> QuantRS2Result<Circuit<32>> {
        if parameters.len() != self.circuit_template.parameters.len() {
            return Err(QuantRS2Error::InvalidInput(
                "Parameter count mismatch".to_string(),
            ));
        }

        // This is a simplified circuit building - would need actual gate implementations
        let mut circuit = Circuit::<32>::new();

        // Build circuit from template using parameters
        for gate_template in &self.circuit_template.structure {
            // Apply parameters to gate and add to circuit
            // This would use the actual gate implementations from quantrs2_core
        }

        Ok(circuit)
    }
}

/// Variational quantum eigensolver (VQE) objective
pub struct VQEObjective {
    /// Hamiltonian matrix
    hamiltonian: SparseMatrix,
    /// Circuit template
    circuit_template: CircuitTemplate,
    /// Parameter bounds
    bounds: Vec<(f64, f64)>,
}

impl VQEObjective {
    /// Create new VQE objective
    #[must_use]
    pub fn new(hamiltonian: SparseMatrix, circuit_template: CircuitTemplate) -> Self {
        let bounds = circuit_template
            .parameters
            .iter()
            .map(|p| (p.lower_bound, p.upper_bound))
            .collect();

        Self {
            hamiltonian,
            circuit_template,
            bounds,
        }
    }
}

impl ObjectiveFunction for VQEObjective {
    fn evaluate(&self, parameters: &[f64]) -> f64 {
        // Build quantum circuit from parameters
        // Simulate circuit to get state vector
        // Compute expectation value ⟨ψ|H|ψ⟩

        // This is a placeholder - real implementation would:
        // 1. Build circuit from template and parameters
        // 2. Simulate circuit to get final state
        // 3. Compute expectation value with Hamiltonian

        // For now, return a simple quadratic function for testing
        parameters.iter().map(|x| x * x).sum::<f64>()
    }

    fn bounds(&self) -> Vec<(f64, f64)> {
        self.bounds.clone()
    }

    fn name(&self) -> &'static str {
        "VQE"
    }
}

/// Quantum Approximate Optimization Algorithm (QAOA) objective
pub struct QAOAObjective {
    /// Problem Hamiltonian
    problem_hamiltonian: SparseMatrix,
    /// Mixer Hamiltonian
    mixer_hamiltonian: SparseMatrix,
    /// Number of QAOA layers
    num_layers: usize,
    /// Parameter bounds
    bounds: Vec<(f64, f64)>,
}

impl QAOAObjective {
    /// Create new QAOA objective
    #[must_use]
    pub fn new(
        problem_hamiltonian: SparseMatrix,
        mixer_hamiltonian: SparseMatrix,
        num_layers: usize,
    ) -> Self {
        // Beta and gamma parameters for each layer
        let bounds = vec![(0.0, 2.0 * std::f64::consts::PI); 2 * num_layers];

        Self {
            problem_hamiltonian,
            mixer_hamiltonian,
            num_layers,
            bounds,
        }
    }
}

impl ObjectiveFunction for QAOAObjective {
    fn evaluate(&self, parameters: &[f64]) -> f64 {
        // Build QAOA circuit from beta and gamma parameters
        // Simulate circuit starting from |+⟩^n state
        // Compute expectation value with problem Hamiltonian

        // Placeholder implementation
        parameters.iter().map(|x| x.sin().powi(2)).sum::<f64>()
    }

    fn bounds(&self) -> Vec<(f64, f64)> {
        self.bounds.clone()
    }

    fn name(&self) -> &'static str {
        "QAOA"
    }
}

impl Default for OptimizationConfig {
    fn default() -> Self {
        Self {
            algorithm: OptimizationAlgorithm::Adam {
                learning_rate: 0.01,
                beta1: 0.9,
                beta2: 0.999,
                epsilon: 1e-8,
            },
            max_evaluations: 1000,
            tolerance: 1e-6,
            seed: None,
            parallel: false,
            num_threads: None,
            progress_callback: None,
            early_stopping: None,
        }
    }
}

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

    #[test]
    fn test_optimization_config_creation() {
        let config = OptimizationConfig::default();
        assert_eq!(config.max_evaluations, 1000);
        assert_eq!(config.tolerance, 1e-6);
    }

    #[test]
    fn test_vqe_objective() {
        let hamiltonian = SparseMatrix::identity(4);
        let template = CircuitTemplate {
            structure: Vec::new(),
            parameters: vec![Parameter {
                name: "theta".to_string(),
                lower_bound: 0.0,
                upper_bound: 2.0 * std::f64::consts::PI,
                initial_value: 0.5,
                discrete: false,
            }],
            num_qubits: 2,
        };

        let objective = VQEObjective::new(hamiltonian, template);
        let value = objective.evaluate(&[0.5]);
        assert!(value >= 0.0);
    }

    #[test]
    fn test_qaoa_objective() {
        let problem_h = SparseMatrix::identity(4);
        let mixer_h = SparseMatrix::identity(4);

        let objective = QAOAObjective::new(problem_h, mixer_h, 2);
        assert_eq!(objective.bounds().len(), 4); // 2 parameters per layer

        let value = objective.evaluate(&[0.5, 1.0, 1.5, 2.0]);
        assert!(value >= 0.0);
    }

    #[test]
    fn test_circuit_template() {
        let template = CircuitTemplate {
            structure: vec![ParameterizedGate {
                gate_name: "RY".to_string(),
                qubits: vec![0],
                parameter_indices: vec![0],
                fixed_parameters: Vec::new(),
            }],
            parameters: vec![Parameter {
                name: "theta".to_string(),
                lower_bound: 0.0,
                upper_bound: 2.0 * std::f64::consts::PI,
                initial_value: 0.0,
                discrete: false,
            }],
            num_qubits: 1,
        };

        assert_eq!(template.parameters.len(), 1);
        assert_eq!(template.structure.len(), 1);
    }

    struct TestObjective;

    impl ObjectiveFunction for TestObjective {
        fn evaluate(&self, parameters: &[f64]) -> f64 {
            parameters.iter().map(|x| (x - 1.0).powi(2)).sum()
        }

        fn bounds(&self) -> Vec<(f64, f64)> {
            vec![(-5.0, 5.0); 2]
        }

        fn name(&self) -> &'static str {
            "test"
        }
    }

    #[test]
    fn test_optimizer_creation() {
        let template = CircuitTemplate {
            structure: Vec::new(),
            parameters: vec![
                Parameter {
                    name: "x1".to_string(),
                    lower_bound: -5.0,
                    upper_bound: 5.0,
                    initial_value: 0.0,
                    discrete: false,
                },
                Parameter {
                    name: "x2".to_string(),
                    lower_bound: -5.0,
                    upper_bound: 5.0,
                    initial_value: 0.0,
                    discrete: false,
                },
            ],
            num_qubits: 1,
        };

        let config = OptimizationConfig::default();
        let optimizer = QuantumCircuitOptimizer::new(template, config);

        assert_eq!(optimizer.circuit_template.parameters.len(), 2);
    }
}