quantrs2-device 0.1.3

//! Photonic Quantum Computing Optimization
//!
//! This module implements optimization algorithms specifically designed for photonic
//! quantum computing systems, including gate sequence optimization and resource allocation.

use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::time::Duration;
use thiserror::Error;

use super::continuous_variable::{Complex, GaussianState};
use super::gate_based::{PhotonicCircuitImplementation, PhotonicGateImpl};
use super::{PhotonicMode, PhotonicSystemType};
use crate::DeviceResult;
use scirs2_core::random::prelude::*;

/// Photonic optimization errors
#[derive(Error, Debug)]
pub enum PhotonicOptimizationError {
    #[error("Optimization failed: {0}")]
    OptimizationFailed(String),
    #[error("Invalid optimization parameters: {0}")]
    InvalidParameters(String),
    #[error("Resource constraints violated: {0}")]
    ResourceConstraints(String),
    #[error("Convergence failed: {0}")]
    ConvergenceFailed(String),
}

/// Optimization objectives for photonic systems
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum PhotonicOptimizationObjective {
    /// Minimize circuit depth
    MinimizeDepth,
    /// Maximize fidelity
    MaximizeFidelity,
    /// Minimize resource usage
    MinimizeResources,
    /// Minimize execution time
    MinimizeTime,
    /// Maximize success probability
    MaximizeSuccessProbability,
    /// Minimize photon loss
    MinimizePhotonLoss,
    /// Multi-objective optimization
    MultiObjective {
        objectives: Vec<Self>,
        weights: Vec<f64>,
    },
}

/// Photonic optimization algorithms
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum PhotonicOptimizationAlgorithm {
    /// Gradient-based optimization
    Gradient {
        learning_rate: f64,
        max_iterations: usize,
    },
    /// Genetic algorithm
    Genetic {
        population_size: usize,
        generations: usize,
    },
    /// Simulated annealing
    SimulatedAnnealing {
        initial_temperature: f64,
        cooling_rate: f64,
    },
    /// Particle swarm optimization
    ParticleSwarm {
        swarm_size: usize,
        iterations: usize,
    },
    /// Quantum approximate optimization
    QAOA { layers: usize },
}

/// Optimization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PhotonicOptimizationConfig {
    /// Primary objective
    pub objective: PhotonicOptimizationObjective,
    /// Optimization algorithm
    pub algorithm: PhotonicOptimizationAlgorithm,
    /// Convergence tolerance
    pub tolerance: f64,
    /// Maximum optimization time
    pub max_time: Duration,
    /// Resource constraints
    pub constraints: PhotonicConstraints,
}

/// Resource constraints for photonic optimization
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PhotonicConstraints {
    /// Maximum number of modes
    pub max_modes: Option<usize>,
    /// Maximum circuit depth
    pub max_depth: Option<usize>,
    /// Maximum number of gates
    pub max_gates: Option<usize>,
    /// Maximum execution time
    pub max_execution_time: Option<Duration>,
    /// Minimum fidelity requirement
    pub min_fidelity: Option<f64>,
    /// Maximum photon loss rate
    pub max_loss_rate: Option<f64>,
}

impl Default for PhotonicConstraints {
    fn default() -> Self {
        Self {
            max_modes: Some(16),
            max_depth: Some(100),
            max_gates: Some(1000),
            max_execution_time: Some(Duration::from_secs(10)),
            min_fidelity: Some(0.95),
            max_loss_rate: Some(0.1),
        }
    }
}

/// Optimization result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PhotonicOptimizationResult {
    /// Optimized circuit implementation
    pub optimized_circuit: PhotonicCircuitImplementation,
    /// Final objective value
    pub objective_value: f64,
    /// Number of iterations performed
    pub iterations: usize,
    /// Optimization time
    pub optimization_time: Duration,
    /// Whether optimization converged
    pub converged: bool,
    /// Improvement metrics
    pub improvement: OptimizationImprovement,
}

/// Metrics showing optimization improvement
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationImprovement {
    /// Fidelity improvement
    pub fidelity_improvement: f64,
    /// Depth reduction
    pub depth_reduction: f64,
    /// Resource savings
    pub resource_savings: f64,
    /// Time savings
    pub time_savings: f64,
}

/// Photonic circuit optimizer
pub struct PhotonicOptimizer {
    /// Optimization configuration
    pub config: PhotonicOptimizationConfig,
    /// Optimization history
    pub history: Vec<OptimizationStep>,
    /// Current best solution
    pub best_solution: Option<PhotonicCircuitImplementation>,
}

/// Single optimization step
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationStep {
    /// Step number
    pub step: usize,
    /// Objective value at this step
    pub objective_value: f64,
    /// Parameters at this step
    pub parameters: Vec<f64>,
    /// Time elapsed
    pub elapsed_time: Duration,
}

impl PhotonicOptimizer {
    pub const fn new(config: PhotonicOptimizationConfig) -> Self {
        Self {
            config,
            history: Vec::new(),
            best_solution: None,
        }
    }

    /// Optimize a photonic circuit implementation
    pub fn optimize(
        &mut self,
        initial_circuit: PhotonicCircuitImplementation,
    ) -> Result<PhotonicOptimizationResult, PhotonicOptimizationError> {
        let start_time = std::time::Instant::now();

        // Validate constraints
        self.validate_constraints(&initial_circuit)?;

        // Initialize optimization
        let mut current_circuit = initial_circuit.clone();
        let mut best_objective = self.evaluate_objective(&current_circuit)?;
        let mut iterations = 0;

        // Main optimization loop
        while start_time.elapsed() < self.config.max_time {
            let improved_circuit = match &self.config.algorithm {
                PhotonicOptimizationAlgorithm::Gradient {
                    learning_rate,
                    max_iterations,
                } => {
                    if iterations >= *max_iterations {
                        break;
                    }
                    self.gradient_step(&current_circuit, *learning_rate)?
                }
                PhotonicOptimizationAlgorithm::Genetic {
                    population_size,
                    generations,
                } => {
                    if iterations >= *generations {
                        break;
                    }
                    self.genetic_step(&current_circuit, *population_size)?
                }
                PhotonicOptimizationAlgorithm::SimulatedAnnealing {
                    initial_temperature,
                    cooling_rate,
                } => {
                    let temperature = initial_temperature * cooling_rate.powf(iterations as f64);
                    if temperature < 1e-6 {
                        break;
                    }
                    self.annealing_step(&current_circuit, temperature)?
                }
                PhotonicOptimizationAlgorithm::ParticleSwarm {
                    swarm_size,
                    iterations: max_iter,
                } => {
                    if iterations >= *max_iter {
                        break;
                    }
                    self.pso_step(&current_circuit, *swarm_size)?
                }
                PhotonicOptimizationAlgorithm::QAOA { layers } => {
                    self.qaoa_step(&current_circuit, *layers)?
                }
            };

            let objective = self.evaluate_objective(&improved_circuit)?;

            // Record optimization step
            self.history.push(OptimizationStep {
                step: iterations,
                objective_value: objective,
                parameters: vec![], // Placeholder
                elapsed_time: start_time.elapsed(),
            });

            // Check for improvement
            if self.is_improvement(objective, best_objective) {
                best_objective = objective;
                current_circuit = improved_circuit;
                self.best_solution = Some(current_circuit.clone());
            }

            // Check convergence
            if self.check_convergence(&current_circuit)? {
                break;
            }

            iterations += 1;
        }

        let final_circuit = self.best_solution.clone().unwrap_or(current_circuit);
        let improvement = self.calculate_improvement(&initial_circuit, &final_circuit);

        Ok(PhotonicOptimizationResult {
            optimized_circuit: final_circuit,
            objective_value: best_objective,
            iterations,
            optimization_time: start_time.elapsed(),
            converged: self.check_convergence_simple(best_objective),
            improvement,
        })
    }

    /// Validate resource constraints
    fn validate_constraints(
        &self,
        circuit: &PhotonicCircuitImplementation,
    ) -> Result<(), PhotonicOptimizationError> {
        let constraints = &self.config.constraints;

        if let Some(max_gates) = constraints.max_gates {
            if circuit.gates.len() > max_gates {
                return Err(PhotonicOptimizationError::ResourceConstraints(format!(
                    "Circuit has {} gates, max allowed {}",
                    circuit.gates.len(),
                    max_gates
                )));
            }
        }

        if let Some(min_fidelity) = constraints.min_fidelity {
            if circuit.total_fidelity < min_fidelity {
                return Err(PhotonicOptimizationError::ResourceConstraints(format!(
                    "Circuit fidelity {} below minimum {}",
                    circuit.total_fidelity, min_fidelity
                )));
            }
        }

        if let Some(max_time) = constraints.max_execution_time {
            if circuit.estimated_execution_time > max_time {
                return Err(PhotonicOptimizationError::ResourceConstraints(format!(
                    "Execution time {:?} exceeds maximum {:?}",
                    circuit.estimated_execution_time, max_time
                )));
            }
        }

        Ok(())
    }

    /// Evaluate optimization objective
    fn evaluate_objective(
        &self,
        circuit: &PhotonicCircuitImplementation,
    ) -> Result<f64, PhotonicOptimizationError> {
        match &self.config.objective {
            PhotonicOptimizationObjective::MinimizeDepth => {
                // Circuit depth approximation
                Ok(-(circuit.gates.len() as f64))
            }
            PhotonicOptimizationObjective::MaximizeFidelity => Ok(circuit.total_fidelity),
            PhotonicOptimizationObjective::MinimizeResources => {
                let resource_count = circuit.resource_requirements.waveplates
                    + circuit.resource_requirements.beam_splitters
                    + circuit.resource_requirements.detectors;
                Ok(-(resource_count as f64))
            }
            PhotonicOptimizationObjective::MinimizeTime => {
                Ok(-(circuit.estimated_execution_time.as_secs_f64()))
            }
            PhotonicOptimizationObjective::MaximizeSuccessProbability => {
                Ok(circuit.success_probability)
            }
            PhotonicOptimizationObjective::MinimizePhotonLoss => {
                // Estimate photon loss from fidelity
                Ok(circuit.total_fidelity)
            }
            PhotonicOptimizationObjective::MultiObjective {
                objectives,
                weights,
            } => {
                let mut total_objective = 0.0;
                for (i, obj) in objectives.iter().enumerate() {
                    if let Some(&weight) = weights.get(i) {
                        let sub_config = PhotonicOptimizationConfig {
                            objective: obj.clone(),
                            ..self.config.clone()
                        };
                        let sub_optimizer = Self::new(sub_config);
                        let sub_value = sub_optimizer.evaluate_objective(circuit)?;
                        total_objective += weight * sub_value;
                    }
                }
                Ok(total_objective)
            }
        }
    }

    /// Check if a value represents an improvement
    fn is_improvement(&self, new_value: f64, current_best: f64) -> bool {
        match &self.config.objective {
            PhotonicOptimizationObjective::MinimizeDepth
            | PhotonicOptimizationObjective::MinimizeResources
            | PhotonicOptimizationObjective::MinimizeTime => new_value < current_best,
            _ => new_value > current_best,
        }
    }

    /// Gradient-based optimization step
    fn gradient_step(
        &self,
        circuit: &PhotonicCircuitImplementation,
        learning_rate: f64,
    ) -> Result<PhotonicCircuitImplementation, PhotonicOptimizationError> {
        // Simplified gradient step - in practice this would involve
        // computing gradients with respect to gate parameters
        let mut optimized = circuit.clone();

        // Randomly perturb parameters (placeholder for gradient computation)
        for gate in &mut optimized.gates {
            if !gate.optical_elements.is_empty() {
                // Small random perturbation
                let perturbation = (thread_rng().random::<f64>() - 0.5) * learning_rate * 0.1;
                // Apply perturbation to gate parameters (simplified)
                optimized.total_fidelity *= 1.0 + perturbation * 0.01;
            }
        }

        Ok(optimized)
    }

    /// Genetic algorithm step
    fn genetic_step(
        &self,
        circuit: &PhotonicCircuitImplementation,
        population_size: usize,
    ) -> Result<PhotonicCircuitImplementation, PhotonicOptimizationError> {
        // Simplified genetic algorithm step
        let mut best_circuit = circuit.clone();

        for _ in 0..population_size {
            let mut candidate = circuit.clone();

            // Random mutation
            if !candidate.gates.is_empty() {
                let mutation_strength = 0.1;
                candidate.total_fidelity *=
                    (thread_rng().random::<f64>() - 0.5).mul_add(mutation_strength, 1.0);
                candidate.total_fidelity = candidate.total_fidelity.clamp(0.0, 1.0);
            }

            // Select better candidate
            if self.evaluate_objective(&candidate)? > self.evaluate_objective(&best_circuit)? {
                best_circuit = candidate;
            }
        }

        Ok(best_circuit)
    }

    /// Simulated annealing step
    fn annealing_step(
        &self,
        circuit: &PhotonicCircuitImplementation,
        temperature: f64,
    ) -> Result<PhotonicCircuitImplementation, PhotonicOptimizationError> {
        let mut candidate = circuit.clone();

        // Random perturbation
        let perturbation_strength = temperature * 0.01;
        candidate.total_fidelity *=
            (thread_rng().random::<f64>() - 0.5).mul_add(perturbation_strength, 1.0);
        candidate.total_fidelity = candidate.total_fidelity.clamp(0.0, 1.0);

        // Accept or reject based on temperature
        let current_obj = self.evaluate_objective(circuit)?;
        let candidate_obj = self.evaluate_objective(&candidate)?;

        if self.is_improvement(candidate_obj, current_obj) {
            Ok(candidate)
        } else {
            let acceptance_prob = (-(current_obj - candidate_obj) / temperature).exp();
            if thread_rng().random::<f64>() < acceptance_prob {
                Ok(candidate)
            } else {
                Ok(circuit.clone())
            }
        }
    }

    /// Particle swarm optimization step
    fn pso_step(
        &self,
        circuit: &PhotonicCircuitImplementation,
        swarm_size: usize,
    ) -> Result<PhotonicCircuitImplementation, PhotonicOptimizationError> {
        // Simplified PSO step
        let mut best_circuit = circuit.clone();

        for _ in 0..swarm_size {
            let mut particle = circuit.clone();

            // Update particle position (simplified)
            let velocity = 0.1 * (thread_rng().random::<f64>() - 0.5);
            particle.total_fidelity += velocity;
            particle.total_fidelity = particle.total_fidelity.clamp(0.0, 1.0);

            if self.evaluate_objective(&particle)? > self.evaluate_objective(&best_circuit)? {
                best_circuit = particle;
            }
        }

        Ok(best_circuit)
    }

    /// QAOA optimization step
    fn qaoa_step(
        &self,
        circuit: &PhotonicCircuitImplementation,
        layers: usize,
    ) -> Result<PhotonicCircuitImplementation, PhotonicOptimizationError> {
        // Simplified QAOA step - in practice this would involve
        // quantum approximate optimization
        let mut optimized = circuit.clone();

        for _ in 0..layers {
            // Apply variational parameters (simplified)
            let gamma = thread_rng().random::<f64>() * std::f64::consts::PI;
            let beta = thread_rng().random::<f64>() * std::f64::consts::PI;

            // Update fidelity based on variational parameters
            optimized.total_fidelity *= 0.01f64.mul_add((gamma + beta).cos(), 1.0);
            optimized.total_fidelity = optimized.total_fidelity.clamp(0.0, 1.0);
        }

        Ok(optimized)
    }

    /// Check convergence
    fn check_convergence(
        &self,
        circuit: &PhotonicCircuitImplementation,
    ) -> Result<bool, PhotonicOptimizationError> {
        if self.history.len() < 2 {
            return Ok(false);
        }

        let recent_values: Vec<f64> = self
            .history
            .iter()
            .rev()
            .take(5)
            .map(|step| step.objective_value)
            .collect();

        if recent_values.len() < 2 {
            return Ok(false);
        }

        let max_val = recent_values
            .iter()
            .fold(f64::NEG_INFINITY, |a, &b| a.max(b));
        let min_val = recent_values.iter().fold(f64::INFINITY, |a, &b| a.min(b));

        Ok((max_val - min_val).abs() < self.config.tolerance)
    }

    /// Simple convergence check
    fn check_convergence_simple(&self, objective_value: f64) -> bool {
        match &self.config.objective {
            PhotonicOptimizationObjective::MaximizeFidelity => objective_value > 0.99,
            PhotonicOptimizationObjective::MaximizeSuccessProbability => objective_value > 0.95,
            _ => false,
        }
    }

    /// Calculate optimization improvement
    fn calculate_improvement(
        &self,
        initial: &PhotonicCircuitImplementation,
        final_circuit: &PhotonicCircuitImplementation,
    ) -> OptimizationImprovement {
        let fidelity_improvement = final_circuit.total_fidelity - initial.total_fidelity;
        let depth_reduction = (initial.gates.len() as f64 - final_circuit.gates.len() as f64)
            / initial.gates.len() as f64;

        let initial_resources = initial.resource_requirements.waveplates
            + initial.resource_requirements.beam_splitters
            + initial.resource_requirements.detectors;
        let final_resources = final_circuit.resource_requirements.waveplates
            + final_circuit.resource_requirements.beam_splitters
            + final_circuit.resource_requirements.detectors;

        let resource_savings =
            (initial_resources as f64 - final_resources as f64) / initial_resources as f64;

        let time_savings = (initial.estimated_execution_time.as_secs_f64()
            - final_circuit.estimated_execution_time.as_secs_f64())
            / initial.estimated_execution_time.as_secs_f64();

        OptimizationImprovement {
            fidelity_improvement,
            depth_reduction,
            resource_savings,
            time_savings,
        }
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::photonic::gate_based::{PhotonicGateImpl, PhotonicResourceRequirements};

    fn create_test_circuit() -> PhotonicCircuitImplementation {
        PhotonicCircuitImplementation {
            gates: vec![],
            resource_requirements: PhotonicResourceRequirements::default(),
            success_probability: 0.9,
            total_fidelity: 0.95,
            estimated_execution_time: Duration::from_millis(100),
        }
    }

    #[test]
    fn test_optimizer_creation() {
        let config = PhotonicOptimizationConfig {
            objective: PhotonicOptimizationObjective::MaximizeFidelity,
            algorithm: PhotonicOptimizationAlgorithm::Gradient {
                learning_rate: 0.01,
                max_iterations: 100,
            },
            tolerance: 1e-6,
            max_time: Duration::from_secs(60),
            constraints: PhotonicConstraints::default(),
        };

        let optimizer = PhotonicOptimizer::new(config);
        assert_eq!(optimizer.history.len(), 0);
    }

    #[test]
    fn test_objective_evaluation() {
        let config = PhotonicOptimizationConfig {
            objective: PhotonicOptimizationObjective::MaximizeFidelity,
            algorithm: PhotonicOptimizationAlgorithm::Gradient {
                learning_rate: 0.01,
                max_iterations: 100,
            },
            tolerance: 1e-6,
            max_time: Duration::from_secs(60),
            constraints: PhotonicConstraints::default(),
        };

        let optimizer = PhotonicOptimizer::new(config);
        let circuit = create_test_circuit();

        let objective = optimizer
            .evaluate_objective(&circuit)
            .expect("Objective evaluation should succeed");
        assert_eq!(objective, 0.95); // Should equal circuit fidelity
    }

    #[test]
    fn test_constraint_validation() {
        let constraints = PhotonicConstraints {
            min_fidelity: Some(0.99),
            ..Default::default()
        };

        let config = PhotonicOptimizationConfig {
            objective: PhotonicOptimizationObjective::MaximizeFidelity,
            algorithm: PhotonicOptimizationAlgorithm::Gradient {
                learning_rate: 0.01,
                max_iterations: 100,
            },
            tolerance: 1e-6,
            max_time: Duration::from_secs(60),
            constraints,
        };

        let optimizer = PhotonicOptimizer::new(config);
        let circuit = create_test_circuit(); // Has fidelity 0.95 < 0.99

        let result = optimizer.validate_constraints(&circuit);
        assert!(result.is_err());
    }
}