quantrs2-device 0.1.3

//! Main VQA execution coordinator
//!
//! This module provides the main execution engine for variational
//! quantum algorithms with comprehensive orchestration capabilities.

use super::{
    circuits::ParametricCircuit,
    hardware::HardwareConfig,
    noise::NoiseMitigationConfig,
    objectives::{ObjectiveEvaluator, ObjectiveFunction, ObjectiveResult},
    statistical::VQAStatistics,
};
use crate::DeviceResult;
use scirs2_core::ndarray::Array1;
use scirs2_core::random::prelude::*;
use std::collections::HashMap;
use std::time::{Duration, Instant};

/// VQA execution configuration
#[derive(Debug, Clone)]
pub struct VQAExecutorConfig {
    /// Maximum iterations
    pub max_iterations: usize,
    /// Convergence tolerance
    pub tolerance: f64,
    /// Hardware configuration
    pub hardware: HardwareConfig,
    /// Noise mitigation configuration
    pub noise_mitigation: NoiseMitigationConfig,
    /// Optimizer settings
    pub optimizer: OptimizerConfig,
}

/// Optimizer configuration
#[derive(Debug, Clone)]
pub struct OptimizerConfig {
    /// Optimizer type
    pub optimizer_type: OptimizerType,
    /// Learning rate
    pub learning_rate: f64,
    /// Additional parameters
    pub parameters: HashMap<String, f64>,
}

/// Available optimizer types
#[derive(Debug, Clone)]
pub enum OptimizerType {
    /// Gradient descent
    GradientDescent,
    /// Adam optimizer
    Adam,
    /// L-BFGS-B
    LBFGSB,
    /// COBYLA
    COBYLA,
}

impl Default for VQAExecutorConfig {
    fn default() -> Self {
        Self {
            max_iterations: 1000,
            tolerance: 1e-6,
            hardware: HardwareConfig::default(),
            noise_mitigation: NoiseMitigationConfig::default(),
            optimizer: OptimizerConfig::default(),
        }
    }
}

impl Default for OptimizerConfig {
    fn default() -> Self {
        Self {
            optimizer_type: OptimizerType::Adam,
            learning_rate: 0.01,
            parameters: HashMap::new(),
        }
    }
}

/// VQA execution result
#[derive(Debug, Clone)]
pub struct VQAResult {
    /// Optimal parameters found
    pub optimal_parameters: Vec<f64>,
    /// Best objective value achieved
    pub best_value: f64,
    /// Number of iterations performed
    pub iterations: usize,
    /// Execution time
    pub execution_time: Duration,
    /// Convergence achieved
    pub converged: bool,
    /// Statistical analysis
    pub statistics: VQAStatistics,
    /// Optimization history
    pub history: Vec<f64>,
}

/// Main VQA executor
#[derive(Debug)]
pub struct VQAExecutor {
    /// Configuration
    pub config: VQAExecutorConfig,
}

impl VQAExecutor {
    /// Create new VQA executor
    pub fn new(
        config: super::config::VQAConfig,
        _calibration_manager: crate::calibration::CalibrationManager,
        _device: Option<String>,
    ) -> Self {
        Self {
            config: VQAExecutorConfig::default(),
        }
    }

    /// Create new VQA executor with config
    pub const fn with_config(config: VQAExecutorConfig) -> Self {
        Self { config }
    }

    /// Execute VQA optimization
    pub fn execute(
        &self,
        circuit: &mut ParametricCircuit,
        objective: &ObjectiveEvaluator,
    ) -> DeviceResult<VQAResult> {
        let start_time = Instant::now();
        let mut best_value = f64::INFINITY;
        let mut best_params = circuit.parameters.clone();
        let mut history = Vec::new();
        let mut converged = false;

        for iteration in 0..self.config.max_iterations {
            // Evaluate objective
            let result = objective.evaluate(&Array1::from_vec(circuit.parameters.clone()))?;
            history.push(result.value);

            // Update best if improved
            if result.value < best_value {
                best_value = result.value;
                best_params.clone_from(&circuit.parameters);
            }

            // Check convergence
            if result.value.abs() < self.config.tolerance {
                converged = true;
                break;
            }

            // Basic parameter update (simplified optimizer)
            self.update_parameters(circuit, &result)?;
        }

        let execution_time = start_time.elapsed();
        let statistics = super::statistical::analyze_convergence(&history);

        Ok(VQAResult {
            optimal_parameters: best_params,
            best_value,
            iterations: history.len(),
            execution_time,
            converged,
            statistics,
            history,
        })
    }

    /// Update circuit parameters based on objective result
    fn update_parameters(
        &self,
        circuit: &mut ParametricCircuit,
        _result: &ObjectiveResult,
    ) -> DeviceResult<()> {
        // Simple random perturbation for demonstration
        use scirs2_core::random::prelude::*;
        let mut rng = thread_rng();

        for param in &mut circuit.parameters {
            *param += rng.random_range(-0.1..0.1) * self.config.optimizer.learning_rate;
        }

        Ok(())
    }
}