quantrs2-device 0.1.3

//! Auto-generated module
//!
//! 🤖 Generated with [SplitRS](https://github.com/cool-japan/splitrs)

use async_trait::async_trait;
use chrono::{DateTime, Datelike, Duration as ChronoDuration, Timelike, Utc};
use serde::{Deserialize, Serialize};
use std::collections::{BTreeMap, HashMap, VecDeque};
use std::sync::{Arc, Mutex, RwLock};
use std::time::Duration;
use thiserror::Error;
use tokio::sync::{mpsc, Semaphore};
use uuid::Uuid;

use crate::quantum_network::distributed_protocols::{
    NodeId, NodeInfo, PerformanceHistory, PerformanceMetrics, TrainingDataPoint,
};

use super::type_definitions::*;

/// Example usage and integration test
#[cfg(test)]
mod tests {
    use super::*;
    #[tokio::test]
    async fn test_ml_network_optimizer() {
        let optimizer = MLNetworkOptimizer::new();
        let network_state = NetworkState {
            nodes: HashMap::new(),
            topology: NetworkTopology {
                nodes: vec![],
                edges: vec![],
                edge_weights: HashMap::new(),
                clustering_coefficient: 0.5,
                diameter: 5,
            },
            performance_metrics: HashMap::new(),
            load_metrics: HashMap::new(),
            entanglement_quality: HashMap::new(),
            centrality_measures: HashMap::new(),
        };
        let objectives = vec![
            OptimizationObjective::MinimizeLatency { weight: 1.0 },
            OptimizationObjective::MaximizeThroughput { weight: 0.8 },
            OptimizationObjective::MaximizeFidelity { weight: 0.9 },
        ];
        let result = optimizer
            .optimize_network_performance(&network_state, &objectives)
            .await;
        assert!(result.is_ok());
        let optimization_result =
            result.expect("Network optimization should succeed with valid input");
        assert!(optimization_result.overall_improvement_estimate > 0.0);
    }
    #[tokio::test]
    async fn test_quantum_traffic_shaper() {
        let shaper = QuantumTrafficShaper::new();
        let predictions = OptimizationPredictions {
            performance_improvement: 0.3,
            implementation_steps: vec![],
            target_nodes: vec![],
            critical_weight: 1.0,
            entanglement_weight: 0.9,
            operations_weight: 0.8,
            error_correction_weight: 0.7,
            classical_weight: 0.6,
            background_weight: 0.3,
            best_effort_weight: 0.1,
            critical_queue_size_ratio: 0.4,
            entanglement_queue_size_ratio: 0.3,
            operations_queue_size_ratio: 0.15,
            error_correction_queue_size_ratio: 0.08,
            classical_queue_size_ratio: 0.04,
            background_queue_size_ratio: 0.02,
            best_effort_queue_size_ratio: 0.01,
            critical_service_rate: 1000.0,
            entanglement_service_rate: 800.0,
            operations_service_rate: 600.0,
            error_correction_service_rate: 400.0,
            classical_service_rate: 200.0,
            background_service_rate: 100.0,
            best_effort_service_rate: 50.0,
            critical_coherence_threshold: 0.001,
            entanglement_red_min: 0.7,
            entanglement_red_max: 0.9,
            optimal_initial_window: 10.0,
            optimal_max_window: 1000.0,
            optimal_backoff_factor: 0.5,
            optimal_rtt_smoothing: 0.125,
        };
        let result = shaper.optimize_traffic_flow(&predictions).await;
        assert!(result.is_ok());
        let traffic_result = result.expect("Traffic shaping should succeed with valid predictions");
        assert_eq!(traffic_result.new_priority_weights.len(), 7);
        assert!(
            traffic_result.new_priority_weights[&Priority::CriticalQuantumState]
                >= traffic_result.new_priority_weights[&Priority::BestEffort]
        );
    }
}