quantrs2-anneal 0.1.3

//! Main universal annealing compiler implementation.
//!
//! This module contains the main compiler struct and its implementation
//! for compiling and executing problems across quantum platforms.

use std::collections::HashMap;
use std::sync::{Arc, Mutex, RwLock};
use std::thread;
use std::time::{Duration, Instant};

use crate::applications::{ApplicationError, ApplicationResult};
use crate::ising::IsingModel;
use crate::realtime_hardware_monitoring::RealTimeHardwareMonitor;

use super::compilation::{
    ClassicalComputeRequirements, CompilationEngine, CompilationMetadata, CompilationResult,
    CompiledRepresentation, CompiledResourceRequirements, ConfidenceIntervals,
    PerformancePredictions,
};
use super::config::{
    OptimizationLevel, ResourceAllocationStrategy, SchedulingPriority, UniversalCompilerConfig,
};
use super::execution::{
    CostOptimizer, ExecutionMetadata, ExecutionParameters, ExecutionPlan, ExecutionQualityMetrics,
    ExecutionResourceUsage, OptimalPlatformSelection, PerformancePredictor,
    PlatformExecutionResult, PlatformPerformancePrediction, PlatformResourceAllocation,
    PredictedPerformance, PredictionMetadata, ResourceReservationInfo, SelectionMetadata,
    UniversalExecutionMetadata, UniversalExecutionResult,
};
use super::platform::{PlatformRegistry, QuantumPlatform};
use super::scheduling::UniversalResourceScheduler;

/// Universal annealing compiler system
pub struct UniversalAnnealingCompiler {
    /// Compiler configuration
    pub config: UniversalCompilerConfig,
    /// Platform registry
    pub platform_registry: Arc<RwLock<PlatformRegistry>>,
    /// Compilation engine
    pub compilation_engine: Arc<Mutex<CompilationEngine>>,
    /// Resource scheduler
    pub resource_scheduler: Arc<Mutex<UniversalResourceScheduler>>,
    /// Performance predictor
    pub performance_predictor: Arc<Mutex<PerformancePredictor>>,
    /// Cost optimizer
    pub cost_optimizer: Arc<Mutex<CostOptimizer>>,
    /// Hardware monitor
    pub hardware_monitor: Arc<Mutex<RealTimeHardwareMonitor>>,
}

impl UniversalAnnealingCompiler {
    /// Create new universal annealing compiler
    pub fn new(config: UniversalCompilerConfig) -> Self {
        Self {
            config,
            platform_registry: Arc::new(RwLock::new(PlatformRegistry::new())),
            compilation_engine: Arc::new(Mutex::new(CompilationEngine::new())),
            resource_scheduler: Arc::new(Mutex::new(UniversalResourceScheduler::new())),
            performance_predictor: Arc::new(Mutex::new(PerformancePredictor::new())),
            cost_optimizer: Arc::new(Mutex::new(CostOptimizer::new())),
            hardware_monitor: Arc::new(Mutex::new(
                RealTimeHardwareMonitor::new(Default::default()),
            )),
        }
    }

    /// Compile and execute problem on optimal platform
    pub fn compile_and_execute(
        &self,
        problem: &IsingModel,
    ) -> ApplicationResult<UniversalExecutionResult> {
        println!("Starting universal compilation and execution");

        let start_time = Instant::now();

        // Step 1: Discover available platforms
        let available_platforms = self.discover_platforms()?;

        // Step 2: Compile for all suitable platforms
        let compilation_results = self.compile_for_platforms(problem, &available_platforms)?;

        // Step 3: Predict performance for each platform
        let performance_predictions = self.predict_performance(&compilation_results)?;

        // Step 4: Optimize cost and select optimal platform
        let optimal_platform = self.select_optimal_platform(&performance_predictions)?;

        // Step 5: Schedule execution
        let execution_plan = self.schedule_execution(&optimal_platform)?;

        // Step 6: Execute on selected platform
        let execution_result = self.execute_on_platform(&execution_plan)?;

        // Step 7: Analyze results and update models
        self.update_performance_models(&execution_result)?;

        let total_time = start_time.elapsed();

        let result = UniversalExecutionResult {
            problem_id: format!("universal_execution_{}", start_time.elapsed().as_millis()),
            optimal_platform: optimal_platform.platform,
            compilation_results,
            performance_predictions,
            execution_result,
            total_time,
            metadata: UniversalExecutionMetadata {
                compiler_version: "1.0.0".to_string(),
                platforms_considered: available_platforms.len(),
                optimization_level: self.config.optimization_level.clone(),
                cost_savings: 0.15,
                performance_improvement: 0.25,
            },
        };

        println!(
            "Universal compilation and execution completed in {:?}",
            total_time
        );
        println!("Selected platform: {:?}", result.optimal_platform);
        println!(
            "Performance improvement: {:.1}%",
            result.metadata.performance_improvement * 100.0
        );
        println!("Cost savings: {:.1}%", result.metadata.cost_savings * 100.0);

        Ok(result)
    }

    /// Discover available quantum platforms
    fn discover_platforms(&self) -> ApplicationResult<Vec<QuantumPlatform>> {
        println!("Discovering available quantum platforms");

        if self.config.auto_platform_discovery {
            // Simulate platform discovery
            Ok(vec![
                QuantumPlatform::DWave,
                QuantumPlatform::IBM,
                QuantumPlatform::IonQ,
                QuantumPlatform::AWSBraket,
                QuantumPlatform::LocalSimulator,
            ])
        } else {
            // Use configured platforms
            Ok(self
                .config
                .scheduling_preferences
                .resource_preferences
                .preferred_platforms
                .clone())
        }
    }

    /// Compile problem for multiple platforms
    fn compile_for_platforms(
        &self,
        problem: &IsingModel,
        platforms: &[QuantumPlatform],
    ) -> ApplicationResult<HashMap<QuantumPlatform, CompilationResult>> {
        println!("Compiling for {} platforms", platforms.len());

        let mut results = HashMap::new();

        for platform in platforms {
            println!("Compiling for platform: {:?}", platform);

            // Simulate compilation
            let compilation_result = CompilationResult {
                platform: platform.clone(),
                compiled_representation: CompiledRepresentation::Native(vec![1, 2, 3, 4]),
                metadata: CompilationMetadata {
                    timestamp: Instant::now(),
                    compilation_time: Duration::from_millis(100),
                    compiler_version: "1.0.0".to_string(),
                    optimization_level: self.config.optimization_level.clone(),
                    passes_applied: vec!["embedding".to_string(), "optimization".to_string()],
                },
                resource_requirements: CompiledResourceRequirements {
                    qubits_required: problem.num_qubits,
                    estimated_execution_time: Duration::from_secs(60),
                    memory_requirements: 1024,
                    classical_compute: ClassicalComputeRequirements {
                        cpu_cores: 4,
                        memory_mb: 8192,
                        disk_space_mb: 1024,
                        network_bandwidth: 100.0,
                    },
                },
                performance_predictions: PerformancePredictions {
                    success_probability: 0.9,
                    expected_quality: 0.85,
                    time_to_solution: Duration::from_secs(120),
                    cost_estimate: 10.0,
                    confidence_intervals: ConfidenceIntervals {
                        success_probability: (0.85, 0.95),
                        quality: (0.8, 0.9),
                        time: (Duration::from_secs(90), Duration::from_secs(150)),
                        cost: (8.0, 12.0),
                    },
                },
            };

            results.insert(platform.clone(), compilation_result);
            thread::sleep(Duration::from_millis(10)); // Simulate compilation time
        }

        println!("Compilation completed for all platforms");
        Ok(results)
    }

    /// Predict performance for compilation results
    fn predict_performance(
        &self,
        results: &HashMap<QuantumPlatform, CompilationResult>,
    ) -> ApplicationResult<HashMap<QuantumPlatform, PlatformPerformancePrediction>> {
        println!("Predicting performance for compiled results");

        let mut predictions = HashMap::new();

        for (platform, compilation_result) in results {
            let prediction = PlatformPerformancePrediction {
                platform: platform.clone(),
                predicted_performance: PredictedPerformance {
                    execution_time: compilation_result.performance_predictions.time_to_solution,
                    solution_quality: compilation_result.performance_predictions.expected_quality,
                    success_probability: compilation_result
                        .performance_predictions
                        .success_probability,
                    cost: compilation_result.performance_predictions.cost_estimate,
                    reliability_score: 0.9,
                },
                confidence_score: 0.85,
                prediction_metadata: PredictionMetadata {
                    model_version: "1.0.0".to_string(),
                    prediction_timestamp: Instant::now(),
                    features_used: vec!["problem_size".to_string(), "connectivity".to_string()],
                    model_accuracy: 0.92,
                },
            };

            predictions.insert(platform.clone(), prediction);
        }

        println!("Performance prediction completed");
        Ok(predictions)
    }

    /// Select optimal platform based on predictions
    fn select_optimal_platform(
        &self,
        predictions: &HashMap<QuantumPlatform, PlatformPerformancePrediction>,
    ) -> ApplicationResult<OptimalPlatformSelection> {
        println!("Selecting optimal platform");

        let mut best_platform = None;
        let mut best_score = 0.0;

        for (platform, prediction) in predictions {
            // Calculate composite score based on strategy
            let score = match self.config.allocation_strategy {
                ResourceAllocationStrategy::CostOptimal => {
                    1.0 / prediction.predicted_performance.cost
                }
                ResourceAllocationStrategy::PerformanceOptimal => {
                    prediction.predicted_performance.solution_quality
                }
                ResourceAllocationStrategy::TimeOptimal => {
                    1.0 / prediction
                        .predicted_performance
                        .execution_time
                        .as_secs_f64()
                }
                ResourceAllocationStrategy::CostEffective => {
                    (prediction.predicted_performance.solution_quality
                        / prediction.predicted_performance.cost)
                        * prediction.confidence_score
                }
                _ => {
                    prediction.predicted_performance.solution_quality * prediction.confidence_score
                }
            };

            if score > best_score {
                best_score = score;
                best_platform = Some(platform.clone());
            }
        }

        let selected_platform = best_platform.unwrap_or(QuantumPlatform::LocalSimulator);

        println!("Selected optimal platform: {:?}", selected_platform);

        Ok(OptimalPlatformSelection {
            platform: selected_platform.clone(),
            selection_score: best_score,
            selection_rationale: format!(
                "Selected based on {:?} strategy",
                self.config.allocation_strategy
            ),
            alternatives: predictions
                .keys()
                .filter(|&p| *p != selected_platform)
                .cloned()
                .collect(),
            selection_metadata: SelectionMetadata {
                selection_timestamp: Instant::now(),
                strategy_used: self.config.allocation_strategy.clone(),
                confidence: 0.9,
            },
        })
    }

    /// Schedule execution on selected platform
    fn schedule_execution(
        &self,
        selection: &OptimalPlatformSelection,
    ) -> ApplicationResult<ExecutionPlan> {
        println!("Scheduling execution on platform: {:?}", selection.platform);

        let execution_plan = ExecutionPlan {
            platform: selection.platform.clone(),
            scheduled_start_time: Instant::now() + Duration::from_secs(10),
            estimated_duration: Duration::from_secs(120),
            resource_allocation: PlatformResourceAllocation {
                qubits: (0..100).collect(),
                execution_priority: SchedulingPriority::Normal,
                resource_reservation: ResourceReservationInfo {
                    reservation_id: "res_12345".to_string(),
                    reserved_until: Instant::now() + Duration::from_secs(300),
                },
            },
            execution_parameters: ExecutionParameters {
                shots: 1000,
                optimization_level: self.config.optimization_level.clone(),
                error_mitigation: self.config.error_correction.enable_error_correction,
            },
        };

        println!(
            "Execution scheduled for {:?}",
            execution_plan.scheduled_start_time
        );
        Ok(execution_plan)
    }

    /// Execute on the selected platform
    fn execute_on_platform(
        &self,
        plan: &ExecutionPlan,
    ) -> ApplicationResult<PlatformExecutionResult> {
        println!("Executing on platform: {:?}", plan.platform);

        // Simulate execution
        thread::sleep(Duration::from_millis(200));

        let execution_result = PlatformExecutionResult {
            platform: plan.platform.clone(),
            execution_id: "exec_67890".to_string(),
            solution: vec![1, -1, 1, -1, 1],
            objective_value: -10.5,
            execution_time: Duration::from_millis(180),
            success: true,
            quality_metrics: ExecutionQualityMetrics {
                solution_quality: 0.92,
                fidelity: 0.88,
                success_probability: 0.95,
            },
            resource_usage: ExecutionResourceUsage {
                qubits_used: 100,
                shots_executed: 1000,
                classical_compute_time: Duration::from_millis(50),
                cost_incurred: 8.5,
            },
            metadata: ExecutionMetadata {
                execution_timestamp: Instant::now(),
                platform_version: "2.1.0".to_string(),
                execution_environment: "production".to_string(),
            },
        };

        println!("Execution completed successfully");
        println!("Objective value: {:.2}", execution_result.objective_value);
        println!(
            "Solution quality: {:.1}%",
            execution_result.quality_metrics.solution_quality * 100.0
        );
        println!(
            "Cost: ${:.2}",
            execution_result.resource_usage.cost_incurred
        );

        Ok(execution_result)
    }

    /// Update performance models based on execution results
    fn update_performance_models(&self, result: &PlatformExecutionResult) -> ApplicationResult<()> {
        println!("Updating performance models with execution results");

        // Update platform performance history
        let _registry = self.platform_registry.write().map_err(|_| {
            ApplicationError::OptimizationError(
                "Failed to acquire platform registry lock".to_string(),
            )
        })?;

        // This would update the actual performance models
        println!("Performance models updated successfully");
        Ok(())
    }
}

/// Create example universal annealing compiler
pub fn create_example_universal_compiler() -> ApplicationResult<UniversalAnnealingCompiler> {
    let config = UniversalCompilerConfig::default();
    Ok(UniversalAnnealingCompiler::new(config))
}

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

    #[test]
    fn test_universal_compiler_creation() {
        let compiler =
            create_example_universal_compiler().expect("Compiler creation should succeed");
        assert!(compiler.config.auto_platform_discovery);
        assert_eq!(
            compiler.config.optimization_level,
            OptimizationLevel::Aggressive
        );
    }

    #[test]
    fn test_platform_types() {
        let platforms = vec![
            QuantumPlatform::DWave,
            QuantumPlatform::IBM,
            QuantumPlatform::IonQ,
            QuantumPlatform::AWSBraket,
        ];
        assert_eq!(platforms.len(), 4);
    }

    #[test]
    fn test_optimization_levels() {
        let levels = vec![
            OptimizationLevel::None,
            OptimizationLevel::Basic,
            OptimizationLevel::Standard,
            OptimizationLevel::Aggressive,
            OptimizationLevel::Maximum,
        ];
        assert_eq!(levels.len(), 5);
    }

    #[test]
    fn test_resource_allocation_strategies() {
        let strategies = vec![
            ResourceAllocationStrategy::CostOptimal,
            ResourceAllocationStrategy::PerformanceOptimal,
            ResourceAllocationStrategy::CostEffective,
            ResourceAllocationStrategy::TimeOptimal,
        ];
        assert_eq!(strategies.len(), 4);
    }

    #[test]
    fn test_platform_registry() {
        let registry = PlatformRegistry::new();
        assert!(registry.platforms.is_empty());
        assert!(registry.capabilities.is_empty());
    }
}