quantrs2_device/quantum_network/distributed_protocols/

implementations.rs

//! Implementations for distributed protocols

use super::types::*;
use async_trait::async_trait;
use chrono::{DateTime, Duration as ChronoDuration, Utc};
use serde::{Deserialize, Serialize};
use std::collections::{BTreeMap, HashMap, HashSet, VecDeque};
use std::sync::{Arc, Mutex, RwLock};
use std::time::{Duration, Instant, SystemTime};
use tokio::sync::{mpsc, RwLock as AsyncRwLock, Semaphore};
use uuid::Uuid;

#[async_trait]
impl LoadBalancer for RoundRobinBalancer {
    fn select_nodes(
        &self,
        partitions: &[CircuitPartition],
        available_nodes: &HashMap<NodeId, NodeInfo>,
        _requirements: &ExecutionRequirements,
    ) -> Result<HashMap<Uuid, NodeId>> {
        let mut assignments = HashMap::new();
        let nodes: Vec<_> = available_nodes.keys().cloned().collect();

        if nodes.is_empty() {
            return Err(DistributedComputationError::ResourceAllocation(
                "No available nodes".to_string(),
            ));
        }

        for partition in partitions {
            let mut index = self
                .current_index
                .lock()
                .expect("Round-robin index mutex poisoned");
            let selected_node = nodes[*index % nodes.len()].clone();
            *index += 1;
            assignments.insert(partition.partition_id, selected_node);
        }

        Ok(assignments)
    }

    fn rebalance_load(
        &self,
        _current_allocation: &HashMap<Uuid, NodeId>,
        _nodes: &HashMap<NodeId, NodeInfo>,
    ) -> Option<HashMap<Uuid, NodeId>> {
        None // Round robin doesn't need rebalancing
    }

    fn predict_execution_time(&self, partition: &CircuitPartition, _node: &NodeInfo) -> Duration {
        partition.estimated_execution_time
    }

    async fn select_node(
        &self,
        available_nodes: &[NodeInfo],
        _requirements: &ResourceRequirements,
    ) -> Result<NodeId> {
        if available_nodes.is_empty() {
            return Err(DistributedComputationError::ResourceAllocation(
                "No available nodes".to_string(),
            ));
        }

        let mut index = self
            .current_index
            .lock()
            .expect("Round-robin index mutex poisoned");
        let selected_node = available_nodes[*index % available_nodes.len()]
            .node_id
            .clone();
        *index += 1;
        Ok(selected_node)
    }

    async fn update_node_metrics(
        &self,
        _node_id: &NodeId,
        _metrics: &PerformanceMetrics,
    ) -> Result<()> {
        Ok(()) // Round robin doesn't use metrics
    }

    fn get_balancer_metrics(&self) -> LoadBalancerMetrics {
        LoadBalancerMetrics {
            total_decisions: 0,
            average_decision_time: Duration::from_millis(1),
            prediction_accuracy: 1.0,
            load_distribution_variance: 0.0,
            total_requests: 0,
            successful_allocations: 0,
            failed_allocations: 0,
            average_response_time: Duration::from_millis(0),
            node_utilization: HashMap::new(),
        }
    }
}

impl Default for RoundRobinBalancer {
    fn default() -> Self {
        Self::new()
    }
}

impl RoundRobinBalancer {
    pub fn new() -> Self {
        Self {
            current_index: Arc::new(Mutex::new(0)),
        }
    }
}

/// Capability-based load balancer
#[derive(Debug)]
pub struct CapabilityBasedBalancer {
    capability_weights: HashMap<String, f64>,
    performance_history: Arc<RwLock<HashMap<NodeId, PerformanceHistory>>>,
}

/// ML-optimized load balancer
#[derive(Debug)]
pub struct MLOptimizedBalancer {
    model_path: String,
    feature_extractor: Arc<FeatureExtractor>,
    prediction_cache: Arc<Mutex<HashMap<String, NodeId>>>,
    training_data_collector: Arc<TrainingDataCollector>,
}

/// Training data collector for ML models
#[derive(Debug)]
pub struct TrainingDataCollector {
    data_buffer: Arc<Mutex<VecDeque<TrainingDataPoint>>>,
    collection_interval: Duration,
    max_buffer_size: usize,
}

/// Fault tolerance management system
#[derive(Debug)]
pub struct FaultToleranceManager {
    fault_detectors: Vec<Box<dyn FaultDetector + Send + Sync>>,
    recovery_strategies: HashMap<String, Box<dyn RecoveryStrategy + Send + Sync>>,
    checkpointing_system: Arc<CheckpointingSystem>,
    redundancy_manager: Arc<RedundancyManager>,
}

/// Trait for fault detection
#[async_trait]
pub trait FaultDetector: std::fmt::Debug {
    async fn detect_faults(&self, nodes: &HashMap<NodeId, NodeInfo>) -> Vec<Fault>;
    fn get_detection_confidence(&self) -> f64;
    fn get_false_positive_rate(&self) -> f64;
}

/// Fault representation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Fault {
    pub fault_id: Uuid,
    pub fault_type: FaultType,
    pub affected_nodes: Vec<NodeId>,
    pub severity: Severity,
    pub detection_time: DateTime<Utc>,
    pub predicted_impact: Impact,
}

/// Types of faults in distributed quantum systems
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum FaultType {
    NodeFailure,
    NetworkPartition,
    QuantumDecoherence,
    HardwareCalibrationDrift,
    SoftwareBug,
    ResourceExhaustion,
    SecurityBreach,
}

/// Fault severity levels
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq, PartialOrd, Ord)]
pub enum Severity {
    Low,
    Medium,
    High,
    Critical,
}

/// Predicted impact of a fault
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Impact {
    pub affected_computations: Vec<Uuid>,
    pub estimated_downtime: Duration,
    pub performance_degradation: f64,
    pub recovery_cost: f64,
}

/// Trait for recovery strategies
#[async_trait]
pub trait RecoveryStrategy: std::fmt::Debug {
    async fn recover_from_fault(
        &self,
        fault: &Fault,
        system_state: &SystemState,
    ) -> Result<RecoveryResult>;

    fn estimate_recovery_time(&self, fault: &Fault) -> Duration;
    fn calculate_recovery_cost(&self, fault: &Fault) -> f64;
}

/// System state snapshot
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SystemState {
    pub nodes: HashMap<NodeId, NodeInfo>,
    pub active_computations: HashMap<Uuid, ExecutionRequest>,
    pub distributed_states: HashMap<Uuid, DistributedQuantumState>,
    pub network_topology: NetworkTopology,
    pub resource_allocation: HashMap<NodeId, ResourceAllocation>,
}

/// Network topology representation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NetworkTopology {
    pub nodes: Vec<NodeId>,
    pub edges: Vec<(NodeId, NodeId)>,
    pub edge_weights: HashMap<(NodeId, NodeId), f64>,
    pub clustering_coefficient: f64,
    pub diameter: u32,
}

/// Resource allocation per node
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceAllocation {
    pub allocated_qubits: Vec<QubitId>,
    pub memory_allocated_mb: u32,
    pub cpu_allocated_percentage: f64,
    pub network_bandwidth_allocated_mbps: f64,
}

/// Recovery result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RecoveryResult {
    pub success: bool,
    pub recovery_time: Duration,
    pub restored_computations: Vec<Uuid>,
    pub failed_computations: Vec<Uuid>,
    pub performance_impact: f64,
}

/// Checkpointing system for fault tolerance
#[derive(Debug)]
pub struct CheckpointingSystem {
    checkpoint_storage: Arc<dyn CheckpointStorage + Send + Sync>,
    checkpoint_frequency: Duration,
    compression_enabled: bool,
    incremental_checkpoints: bool,
}

/// Trait for checkpoint storage
#[async_trait]
pub trait CheckpointStorage: std::fmt::Debug {
    async fn store_checkpoint(&self, checkpoint_id: Uuid, data: &CheckpointData) -> Result<()>;

    async fn load_checkpoint(&self, checkpoint_id: Uuid) -> Result<CheckpointData>;

    async fn list_checkpoints(&self) -> Result<Vec<Uuid>>;
    async fn delete_checkpoint(&self, checkpoint_id: Uuid) -> Result<()>;
}

/// Checkpoint data structure
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CheckpointData {
    pub timestamp: DateTime<Utc>,
    pub system_state: SystemState,
    pub computation_progress: HashMap<Uuid, ComputationProgress>,
    pub quantum_states: HashMap<Uuid, DistributedQuantumState>,
    pub metadata: HashMap<String, String>,
}

/// Computation progress tracking
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComputationProgress {
    pub completed_partitions: Vec<Uuid>,
    pub in_progress_partitions: Vec<Uuid>,
    pub pending_partitions: Vec<Uuid>,
    pub intermediate_results: HashMap<String, Vec<f64>>,
    pub execution_statistics: ExecutionStatistics,
}

/// Execution statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ExecutionStatistics {
    pub start_time: DateTime<Utc>,
    pub estimated_completion_time: DateTime<Utc>,
    pub gates_executed: u32,
    pub measurements_completed: u32,
    pub average_fidelity: f64,
    pub error_rate: f64,
}

/// Redundancy management for fault tolerance
#[derive(Debug)]
pub struct RedundancyManager {
    redundancy_strategies: HashMap<String, Box<dyn RedundancyStrategy + Send + Sync>>,
    replication_factor: u32,
    consistency_protocol: String,
}

/// Trait for redundancy strategies
pub trait RedundancyStrategy: std::fmt::Debug {
    fn replicate_computation(
        &self,
        computation: &ExecutionRequest,
        replication_factor: u32,
    ) -> Vec<ExecutionRequest>;

    fn aggregate_results(&self, results: &[ComputationResult]) -> Result<ComputationResult>;

    fn detect_byzantine_faults(&self, results: &[ComputationResult]) -> Vec<NodeId>;
}

/// Computation result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComputationResult {
    pub result_id: Uuid,
    pub computation_id: Uuid,
    pub node_id: NodeId,
    pub measurements: HashMap<u32, bool>,
    pub final_state: Option<LocalQuantumState>,
    pub execution_time: Duration,
    pub fidelity: f64,
    pub error_rate: f64,
    pub metadata: HashMap<String, String>,
}

/// Consensus engine trait for distributed decision making
#[async_trait]
pub trait ConsensusEngine: std::fmt::Debug {
    async fn reach_consensus<T: Serialize + for<'de> Deserialize<'de> + Clone + Send>(
        &self,
        proposal: T,
        participants: &[NodeId],
        timeout: Duration,
    ) -> Result<ConsensusResult<T>>;

    async fn elect_leader(&self, candidates: &[NodeId], timeout: Duration) -> Result<NodeId>;

    fn get_consensus_confidence(&self) -> f64;
}

/// Consensus result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ConsensusResult<T> {
    pub decision: T,
    pub consensus_achieved: bool,
    pub participating_nodes: Vec<NodeId>,
    pub consensus_time: Duration,
    pub confidence: f64,
}

/// Byzantine fault tolerant consensus
#[derive(Debug)]
pub struct ByzantineConsensus {
    fault_tolerance: u32,
    timeout: Duration,
    message_authenticator: Arc<MessageAuthenticator>,
}

/// Raft consensus implementation
#[derive(Debug)]
pub struct RaftConsensus {
    election_timeout: Duration,
    heartbeat_interval: Duration,
    log_replication: Arc<LogReplication>,
    leader_state: Arc<RwLock<LeaderState>>,
}

/// Leader state for Raft consensus
#[derive(Debug, Clone)]
pub struct LeaderState {
    pub current_leader: Option<NodeId>,
    pub term: u64,
    pub last_heartbeat: DateTime<Utc>,
}

/// Message authenticator for secure consensus
#[derive(Debug)]
pub struct MessageAuthenticator {
    authentication_method: String,
    key_rotation_interval: Duration,
    signature_verification: bool,
}

/// Log replication for Raft consensus
#[derive(Debug)]
pub struct LogReplication {
    log_entries: Arc<RwLock<Vec<LogEntry>>>,
    commit_index: Arc<RwLock<u64>>,
    last_applied: Arc<RwLock<u64>>,
}

/// Log entry for Raft consensus
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LogEntry {
    pub term: u64,
    pub index: u64,
    pub command: Command,
    pub timestamp: DateTime<Utc>,
}

/// Commands for consensus protocol
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum Command {
    AllocateResources {
        node_id: NodeId,
        resources: ResourceRequirements,
    },
    StartComputation {
        computation_id: Uuid,
        partition: CircuitPartition,
    },
    UpdateNodeStatus {
        node_id: NodeId,
        status: NodeStatus,
    },
    RebalanceLoad {
        new_allocation: HashMap<Uuid, NodeId>,
    },
    HandleFault {
        fault: Fault,
        recovery_action: String,
    },
}

/// Metrics collection system
#[derive(Debug)]
pub struct MetricsCollector {
    metrics_storage: Arc<dyn MetricsStorage + Send + Sync>,
    collection_interval: Duration,
    metrics_aggregator: Arc<MetricsAggregator>,
    alerting_system: Arc<AlertingSystem>,
}

/// Trait for metrics storage
#[async_trait]
pub trait MetricsStorage: std::fmt::Debug {
    async fn store_metric(&self, metric: &Metric) -> Result<()>;
    async fn query_metrics(&self, query: &MetricsQuery) -> Result<Vec<Metric>>;
    async fn aggregate_metrics(&self, aggregation: &AggregationQuery) -> Result<AggregatedMetrics>;
}

/// Individual metric data point
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Metric {
    pub metric_name: String,
    pub value: f64,
    pub timestamp: DateTime<Utc>,
    pub tags: HashMap<String, String>,
    pub node_id: Option<NodeId>,
}

/// Metrics query structure
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MetricsQuery {
    pub metric_names: Vec<String>,
    pub time_range: (DateTime<Utc>, DateTime<Utc>),
    pub filters: HashMap<String, String>,
    pub limit: Option<u32>,
}

/// Aggregation query
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AggregationQuery {
    pub metric_name: String,
    pub aggregation_function: AggregationFunction,
    pub time_range: (DateTime<Utc>, DateTime<Utc>),
    pub group_by: Vec<String>,
}

/// Aggregation functions
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AggregationFunction {
    Sum,
    Average,
    Min,
    Max,
    Count,
    Percentile(f64),
    StandardDeviation,
}

/// Aggregated metrics result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AggregatedMetrics {
    pub metric_name: String,
    pub aggregation_function: AggregationFunction,
    pub value: f64,
    pub time_range: (DateTime<Utc>, DateTime<Utc>),
    pub group_by_values: HashMap<String, f64>,
}

/// Metrics aggregation engine
#[derive(Debug)]
pub struct MetricsAggregator {
    aggregation_strategies: Vec<AggregationStrategy>,
    real_time_aggregation: bool,
    batch_size: u32,
}

/// Aggregation strategy
#[derive(Debug, Clone)]
pub struct AggregationStrategy {
    pub metric_pattern: String,
    pub aggregation_interval: Duration,
    pub functions: Vec<AggregationFunction>,
    pub retention_period: Duration,
}

/// Alerting system for monitoring
#[derive(Debug)]
pub struct AlertingSystem {
    alert_rules: Vec<AlertRule>,
    notification_channels: HashMap<String, Box<dyn NotificationChannel + Send + Sync>>,
    alert_history: Arc<RwLock<VecDeque<Alert>>>,
}

/// Alert rule definition
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AlertRule {
    pub rule_id: String,
    pub metric_name: String,
    pub condition: AlertCondition,
    pub threshold: f64,
    pub severity: Severity,
    pub notification_channels: Vec<String>,
    pub cooldown_period: Duration,
}

/// Alert conditions
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AlertCondition {
    GreaterThan,
    LessThan,
    Equals,
    NotEquals,
    RateOfChange(f64),
    AnomalyDetection,
}

/// Alert notification
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Alert {
    pub alert_id: Uuid,
    pub rule_id: String,
    pub timestamp: DateTime<Utc>,
    pub severity: Severity,
    pub message: String,
    pub affected_nodes: Vec<NodeId>,
    pub metric_value: f64,
}

/// Trait for notification channels
#[async_trait]
pub trait NotificationChannel: std::fmt::Debug {
    async fn send_notification(&self, alert: &Alert) -> Result<()>;
    fn get_channel_type(&self) -> String;
    fn is_available(&self) -> bool;
}

/// Resource allocation system
#[derive(Debug)]
pub struct ResourceAllocator {
    allocation_strategies: HashMap<String, Box<dyn AllocationStrategy + Send + Sync>>,
    resource_monitor: Arc<ResourceMonitor>,
    allocation_history: Arc<RwLock<VecDeque<AllocationRecord>>>,
}

/// Trait for resource allocation strategies
pub trait AllocationStrategy: std::fmt::Debug {
    fn allocate_resources(
        &self,
        request: &ExecutionRequest,
        available_resources: &HashMap<NodeId, AvailableResources>,
    ) -> Result<AllocationPlan>;

    fn deallocate_resources(&self, allocation: &AllocationPlan) -> Result<()>;

    fn estimate_allocation_time(&self, request: &ExecutionRequest) -> Duration;
}

/// Available resources on a node
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AvailableResources {
    pub available_qubits: u32,
    pub available_memory_mb: u32,
    pub available_cpu_percentage: f64,
    pub available_network_bandwidth_mbps: f64,
    pub estimated_availability_time: Duration,
}

/// Resource allocation plan
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AllocationPlan {
    pub plan_id: Uuid,
    pub allocations: HashMap<NodeId, ResourceAllocation>,
    pub estimated_cost: f64,
    pub estimated_execution_time: Duration,
    pub allocation_timestamp: DateTime<Utc>,
}

/// Resource allocation record
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AllocationRecord {
    pub record_id: Uuid,
    pub allocation_plan: AllocationPlan,
    pub actual_execution_time: Option<Duration>,
    pub actual_cost: Option<f64>,
    pub success: Option<bool>,
    pub performance_metrics: Option<PerformanceMetrics>,
}

/// Resource monitoring system
#[derive(Debug)]
pub struct ResourceMonitor {
    monitoring_agents: HashMap<NodeId, Box<dyn MonitoringAgent + Send + Sync>>,
    monitoring_interval: Duration,
    resource_predictions: Arc<ResourcePredictor>,
}

/// Trait for monitoring agents
#[async_trait]
pub trait MonitoringAgent: std::fmt::Debug {
    async fn collect_resource_metrics(&self) -> Result<ResourceMetrics>;
    async fn predict_resource_usage(&self, horizon: Duration) -> Result<ResourceUsagePrediction>;
    fn get_agent_health(&self) -> AgentHealth;
}

/// Resource metrics from monitoring
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceMetrics {
    pub timestamp: DateTime<Utc>,
    pub cpu_utilization: f64,
    pub memory_utilization: f64,
    pub network_utilization: f64,
    pub qubit_utilization: f64,
    pub queue_length: u32,
    pub active_computations: u32,
}

/// Resource usage prediction
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceUsagePrediction {
    pub prediction_horizon: Duration,
    pub predicted_cpu_usage: f64,
    pub predicted_memory_usage: f64,
    pub predicted_network_usage: f64,
    pub predicted_qubit_usage: f64,
    pub confidence_interval: (f64, f64),
}

/// Monitoring agent health status
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AgentHealth {
    pub is_healthy: bool,
    pub last_successful_collection: DateTime<Utc>,
    pub error_rate: f64,
    pub response_time: Duration,
}

/// Resource predictor for capacity planning
#[derive(Debug)]
pub struct ResourcePredictor {
    prediction_models: HashMap<String, Box<dyn PredictionModel + Send + Sync>>,
    training_scheduler: Arc<TrainingScheduler>,
    model_evaluator: Arc<ModelEvaluator>,
}

/// Trait for prediction models
#[async_trait]
pub trait PredictionModel: std::fmt::Debug {
    async fn predict(
        &self,
        features: &HashMap<String, f64>,
        horizon: Duration,
    ) -> Result<PredictionResult>;

    async fn train(&mut self, training_data: &[TrainingDataPoint]) -> Result<TrainingResult>;

    fn get_model_accuracy(&self) -> f64;
}

/// Prediction result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PredictionResult {
    pub predicted_value: f64,
    pub confidence: f64,
    pub prediction_interval: (f64, f64),
    pub model_used: String,
}

/// Training result for ML models
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrainingResult {
    pub training_success: bool,
    pub model_accuracy: f64,
    pub training_time: Duration,
    pub validation_metrics: HashMap<String, f64>,
}

/// Training scheduler for ML models
#[derive(Debug)]
pub struct TrainingScheduler {
    training_schedule: HashMap<String, TrainingConfig>,
    auto_retraining: bool,
    performance_threshold: f64,
}

/// Training configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrainingConfig {
    pub model_name: String,
    pub training_frequency: Duration,
    pub training_data_size: u32,
    pub validation_split: f64,
    pub hyperparameters: HashMap<String, f64>,
}

/// Model evaluator for performance assessment
#[derive(Debug)]
pub struct ModelEvaluator {
    evaluation_metrics: Vec<String>,
    cross_validation_folds: u32,
    benchmark_datasets: HashMap<String, Vec<TrainingDataPoint>>,
}

// Implementation of Default trait for main config
impl Default for DistributedComputationConfig {
    fn default() -> Self {
        Self {
            max_partition_size: 50,
            min_partition_size: 5,
            load_balancing_strategy: LoadBalancingStrategy::CapabilityBased,
            fault_tolerance_level: FaultToleranceLevel::Basic {
                redundancy_factor: 2,
            },
            state_synchronization_interval: Duration::from_millis(100),
            entanglement_distribution_protocol: EntanglementDistributionProtocol::Direct,
            consensus_protocol: ConsensusProtocol::Raft {
                election_timeout: Duration::from_millis(500),
                heartbeat_interval: Duration::from_millis(100),
            },
            optimization_objectives: vec![
                OptimizationObjective::MinimizeLatency { weight: 0.3 },
                OptimizationObjective::MaximizeFidelity { weight: 0.4 },
                OptimizationObjective::MinimizeResourceUsage { weight: 0.3 },
            ],
        }
    }
}

// Basic implementations for the main orchestrator
impl DistributedQuantumOrchestrator {
    pub fn new(config: DistributedComputationConfig) -> Self {
        Self {
            config,
            nodes: Arc::new(RwLock::new(HashMap::new())),
            circuit_partitioner: Arc::new(CircuitPartitioner::new()),
            state_manager: Arc::new(DistributedStateManager::new()),
            load_balancer: Arc::new(CapabilityBasedBalancer::new()),
            _private: (),
        }
    }

    pub async fn submit_computation(&self, _request: ExecutionRequest) -> Result<Uuid> {
        // Simplified implementation - execution queue moved to internal implementation
        Ok(Uuid::new_v4())
    }

    async fn process_execution_queue(&self) -> Result<()> {
        // Simplified implementation
        Ok(())
    }
}

// Additional implementation methods
impl DistributedQuantumOrchestrator {
    async fn execute_distributed_computation(
        &self,
        request: ExecutionRequest,
    ) -> Result<ComputationResult> {
        // Partition the circuit
        let nodes = self.nodes.read().expect("Nodes RwLock poisoned").clone();
        let partitions =
            self.circuit_partitioner
                .partition_circuit(&request.circuit, &nodes, &self.config)?;

        // Simplified - resource allocation and execution simplified
        // Return dummy result for now
        Ok(ComputationResult {
            result_id: request.request_id,
            node_id: NodeId("simplified".to_string()),
            final_state: None,
            fidelity: 1.0,
            error_rate: 0.0,
            metadata: HashMap::new(),
            computation_id: request.request_id,
            measurements: HashMap::new(),
            execution_time: Duration::from_millis(0),
        })
    }

    async fn execute_partitions_parallel(
        &self,
        partitions: Vec<CircuitPartition>,
        allocation_plan: AllocationPlan,
    ) -> Result<Vec<ComputationResult>> {
        // Simplified implementation
        let mut results = Vec::new();

        for partition in partitions {
            if let Some(allocated_node) = allocation_plan.allocations.keys().next() {
                let result = self
                    .execute_partition_on_node(&partition, allocated_node)
                    .await?;
                results.push(result);
            }
        }

        Ok(results)
    }

    async fn execute_partition_on_node(
        &self,
        partition: &CircuitPartition,
        node_id: &NodeId,
    ) -> Result<ComputationResult> {
        // Simplified implementation
        Ok(ComputationResult {
            result_id: Uuid::new_v4(),
            computation_id: partition.partition_id,
            node_id: node_id.clone(),
            measurements: HashMap::new(),
            final_state: None,
            execution_time: Duration::from_millis(100),
            fidelity: 0.95,
            error_rate: 0.01,
            metadata: HashMap::new(),
        })
    }

    fn aggregate_partition_results(
        &self,
        results: Vec<ComputationResult>,
    ) -> Result<ComputationResult> {
        // Simplified aggregation
        if let Some(first_result) = results.first() {
            Ok(first_result.clone())
        } else {
            Err(DistributedComputationError::StateSynchronization(
                "No results to aggregate".to_string(),
            ))
        }
    }

    pub async fn register_node(&self, node_info: NodeInfo) -> Result<()> {
        let mut nodes = self.nodes.write().expect("Nodes RwLock poisoned");
        nodes.insert(node_info.node_id.clone(), node_info);
        Ok(())
    }

    pub async fn unregister_node(&self, node_id: &NodeId) -> Result<()> {
        let mut nodes = self.nodes.write().expect("Nodes RwLock poisoned");
        nodes.remove(node_id);
        Ok(())
    }

    pub async fn get_system_status(&self) -> SystemStatus {
        let nodes = self.nodes.read().expect("Nodes RwLock poisoned");

        SystemStatus {
            total_nodes: nodes.len() as u32,
            active_nodes: nodes
                .values()
                .filter(|n| matches!(n.status, NodeStatus::Active))
                .count() as u32,
            total_qubits: nodes.values().map(|n| n.capabilities.max_qubits).sum(),
            active_computations: 0, // Simplified
            system_health: 0.95,    // Simplified
        }
    }
}

/// System status summary
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SystemStatus {
    pub total_nodes: u32,
    pub active_nodes: u32,
    pub total_qubits: u32,
    pub active_computations: u32,
    pub system_health: f64,
}

// Basic implementations for supporting structures
impl Default for CircuitPartitioner {
    fn default() -> Self {
        Self::new()
    }
}

impl CircuitPartitioner {
    pub fn new() -> Self {
        Self {
            partitioning_strategies: vec![
                Box::new(GraphBasedPartitioning::new()),
                Box::new(LoadBalancedPartitioning::new()),
            ],
            optimization_engine: Arc::new(PartitionOptimizer::new()),
        }
    }

    pub fn partition_circuit(
        &self,
        circuit: &QuantumCircuit,
        nodes: &HashMap<NodeId, NodeInfo>,
        config: &DistributedComputationConfig,
    ) -> Result<Vec<CircuitPartition>> {
        // Use the first strategy for simplicity
        if let Some(strategy) = self.partitioning_strategies.first() {
            strategy.partition_circuit(circuit, nodes, config)
        } else {
            Err(DistributedComputationError::CircuitPartitioning(
                "No partitioning strategies available".to_string(),
            ))
        }
    }
}

impl Default for GraphBasedPartitioning {
    fn default() -> Self {
        Self::new()
    }
}

impl GraphBasedPartitioning {
    pub fn new() -> Self {
        Self {
            min_cut_algorithm: "Kernighan-Lin".to_string(),
            load_balancing_weight: 0.3,
            communication_weight: 0.7,
        }
    }
}

impl PartitioningStrategy for GraphBasedPartitioning {
    fn partition_circuit(
        &self,
        circuit: &QuantumCircuit,
        nodes: &HashMap<NodeId, NodeInfo>,
        config: &DistributedComputationConfig,
    ) -> Result<Vec<CircuitPartition>> {
        // Enhanced graph-based partitioning logic
        let mut partitions = Vec::new();

        if nodes.is_empty() {
            return Err(DistributedComputationError::CircuitPartitioning(
                "No nodes available for partitioning".to_string(),
            ));
        }

        // Build dependency graph of gates
        let gate_dependencies = self.build_gate_dependency_graph(&circuit.gates);

        // Use min-cut algorithm to partition gates
        let gate_partitions =
            self.min_cut_partition(&circuit.gates, &gate_dependencies, nodes.len());

        let nodes_vec: Vec<_> = nodes.iter().collect();

        for (partition_idx, gate_indices) in gate_partitions.iter().enumerate() {
            let node_idx = partition_idx % nodes_vec.len();
            let (node_id, node_info) = &nodes_vec[node_idx];

            let partition_gates: Vec<_> = gate_indices
                .iter()
                .map(|&idx| circuit.gates[idx].clone())
                .collect();

            // Calculate qubits involved in this partition
            let mut qubits_used = std::collections::HashSet::new();
            for gate in &partition_gates {
                qubits_used.extend(&gate.target_qubits);
                qubits_used.extend(&gate.control_qubits);
            }

            let qubits_needed = qubits_used.len() as u32;

            // Validate node capacity
            if qubits_needed > node_info.capabilities.max_qubits {
                return Err(DistributedComputationError::ResourceAllocation(format!(
                    "Node {} insufficient capacity: needs {} qubits, has {}",
                    node_id.0, qubits_needed, node_info.capabilities.max_qubits
                )));
            }

            // Calculate communication overhead between partitions
            let communication_cost = self.calculate_inter_partition_communication(
                gate_indices,
                &gate_partitions,
                &circuit.gates,
            );

            let estimated_time =
                self.estimate_partition_execution_time(&partition_gates, node_info);
            let gates_count = partition_gates.len() as u32;
            let memory_mb = self.estimate_memory_usage(&partition_gates);
            let entanglement_pairs_needed = self.count_entangling_operations(&partition_gates);

            let partition = CircuitPartition {
                partition_id: Uuid::new_v4(),
                node_id: (*node_id).clone(),
                gates: partition_gates.clone(),
                dependencies: self.calculate_partition_dependencies(
                    partition_idx,
                    &gate_partitions,
                    &gate_dependencies,
                ),
                input_qubits: qubits_used
                    .iter()
                    .map(|qubit_id| QubitId {
                        node_id: (*node_id).clone(),
                        local_id: qubit_id.local_id,
                        global_id: Uuid::new_v4(),
                    })
                    .collect(),
                output_qubits: qubits_used
                    .iter()
                    .map(|qubit_id| QubitId {
                        node_id: (*node_id).clone(),
                        local_id: qubit_id.local_id,
                        global_id: Uuid::new_v4(),
                    })
                    .collect(),
                classical_inputs: vec![],
                estimated_execution_time: estimated_time,
                resource_requirements: ResourceRequirements {
                    qubits_needed,
                    gates_count,
                    memory_mb,
                    execution_time_estimate: estimated_time,
                    entanglement_pairs_needed,
                    classical_communication_bits: communication_cost,
                },
            };
            partitions.push(partition);
        }

        Ok(partitions)
    }

    fn estimate_execution_time(&self, partition: &CircuitPartition, node: &NodeInfo) -> Duration {
        self.estimate_partition_execution_time(&partition.gates, node)
    }

    fn calculate_communication_overhead(
        &self,
        partitions: &[CircuitPartition],
        nodes: &HashMap<NodeId, NodeInfo>,
    ) -> f64 {
        // Calculate communication overhead based on inter-partition dependencies
        let mut total_overhead = 0.0;

        for partition in partitions {
            // Communication cost based on entanglement pairs needed
            total_overhead +=
                partition.resource_requirements.entanglement_pairs_needed as f64 * 0.5;

            // Add cost for classical communication
            total_overhead +=
                partition.resource_requirements.classical_communication_bits as f64 * 0.01;
        }

        total_overhead
    }
}

impl GraphBasedPartitioning {
    // Private helper methods for enhanced partitioning
    fn build_gate_dependency_graph(&self, gates: &[QuantumGate]) -> Vec<Vec<usize>> {
        let mut dependencies = vec![Vec::new(); gates.len()];

        for (i, gate) in gates.iter().enumerate() {
            for (j, other_gate) in gates.iter().enumerate().take(i) {
                // Check if gates share qubits (dependency)
                let gate_qubits: std::collections::HashSet<_> = gate
                    .target_qubits
                    .iter()
                    .chain(gate.control_qubits.iter())
                    .collect();
                let other_qubits: std::collections::HashSet<_> = other_gate
                    .target_qubits
                    .iter()
                    .chain(other_gate.control_qubits.iter())
                    .collect();

                if !gate_qubits.is_disjoint(&other_qubits) {
                    dependencies[i].push(j);
                }
            }
        }

        dependencies
    }

    fn min_cut_partition(
        &self,
        gates: &[QuantumGate],
        _dependencies: &[Vec<usize>],
        num_partitions: usize,
    ) -> Vec<Vec<usize>> {
        // Simplified min-cut algorithm using balanced partitioning
        let partition_size = gates.len() / num_partitions;
        let mut partitions = Vec::new();

        for i in 0..num_partitions {
            let start = i * partition_size;
            let end = if i == num_partitions - 1 {
                gates.len()
            } else {
                (i + 1) * partition_size
            };
            let partition: Vec<usize> = (start..end).collect();
            partitions.push(partition);
        }

        partitions
    }

    fn calculate_inter_partition_communication(
        &self,
        partition_indices: &[usize],
        all_partitions: &[Vec<usize>],
        gates: &[QuantumGate],
    ) -> u32 {
        let mut communication_bits = 0;

        for &gate_idx in partition_indices {
            let gate = &gates[gate_idx];

            // Check if this gate needs data from other partitions
            for other_partition in all_partitions {
                if other_partition != partition_indices {
                    for &other_gate_idx in other_partition {
                        if other_gate_idx < gate_idx {
                            let other_gate = &gates[other_gate_idx];

                            // Check for qubit overlap (indicates communication needed)
                            let gate_qubits: std::collections::HashSet<_> = gate
                                .target_qubits
                                .iter()
                                .chain(gate.control_qubits.iter())
                                .collect();
                            let other_qubits: std::collections::HashSet<_> = other_gate
                                .target_qubits
                                .iter()
                                .chain(other_gate.control_qubits.iter())
                                .collect();

                            if !gate_qubits.is_disjoint(&other_qubits) {
                                communication_bits += 1; // One bit of communication per shared qubit
                            }
                        }
                    }
                }
            }
        }

        communication_bits
    }

    const fn calculate_partition_dependencies(
        &self,
        _partition_idx: usize,
        _all_partitions: &[Vec<usize>],
        _gate_dependencies: &[Vec<usize>],
    ) -> Vec<Uuid> {
        // For now, return empty dependencies as this requires more complex logic
        // In a full implementation, this would map partition dependencies to UUIDs
        vec![]
    }

    fn estimate_partition_execution_time(
        &self,
        gates: &[QuantumGate],
        node_info: &NodeInfo,
    ) -> Duration {
        let base_gate_time = Duration::from_nanos(100_000); // 100 microseconds per gate
        let mut total_time = Duration::ZERO;

        for gate in gates {
            let gate_fidelity = node_info
                .capabilities
                .gate_fidelities
                .get(&gate.gate_type)
                .unwrap_or(&0.95);

            // Higher fidelity gates execute faster (better calibration)
            let adjusted_time =
                Duration::from_nanos((base_gate_time.as_nanos() as f64 / gate_fidelity) as u64);
            total_time += adjusted_time;
        }

        // Add coherence time impact if coherence times are available
        if !node_info.capabilities.coherence_times.is_empty() {
            let avg_coherence = node_info
                .capabilities
                .coherence_times
                .values()
                .map(|t| t.as_nanos())
                .sum::<u128>() as f64
                / node_info.capabilities.coherence_times.len() as f64;

            if total_time.as_nanos() as f64 > avg_coherence * 0.5 {
                // Add penalty for operations close to coherence time
                total_time = Duration::from_nanos((total_time.as_nanos() as f64 * 1.2) as u64);
            }
        }

        total_time
    }

    fn estimate_memory_usage(&self, gates: &[QuantumGate]) -> u32 {
        let max_qubit_id = gates
            .iter()
            .flat_map(|g| g.target_qubits.iter().chain(g.control_qubits.iter()))
            .map(|qubit_id| qubit_id.local_id)
            .max()
            .unwrap_or(0);

        // Memory for state vector: 2^n complex numbers (16 bytes each)
        let state_vector_mb = (1u64 << (max_qubit_id + 1)) * 16 / (1024 * 1024);

        // Add overhead for gate operations and classical storage
        let overhead_mb = gates.len() as u64 / 100; // 1MB per 100 gates

        std::cmp::max(state_vector_mb + overhead_mb, 10) as u32 // Minimum 10MB
    }

    fn count_entangling_operations(&self, gates: &[QuantumGate]) -> u32 {
        gates
            .iter()
            .filter(|g| {
                !g.control_qubits.is_empty()
                    || g.gate_type.contains("CX")
                    || g.gate_type.contains("CNOT")
                    || g.gate_type.contains("CZ")
                    || g.gate_type.contains("Bell")
            })
            .count() as u32
    }
}

impl Default for LoadBalancedPartitioning {
    fn default() -> Self {
        Self::new()
    }
}

impl LoadBalancedPartitioning {
    pub fn new() -> Self {
        Self {
            load_threshold: 0.8,
            rebalancing_strategy: "min_max".to_string(),
        }
    }
}

impl PartitioningStrategy for LoadBalancedPartitioning {
    fn partition_circuit(
        &self,
        circuit: &QuantumCircuit,
        nodes: &HashMap<NodeId, NodeInfo>,
        config: &DistributedComputationConfig,
    ) -> Result<Vec<CircuitPartition>> {
        // Similar simplified implementation
        let strategy = GraphBasedPartitioning::new();
        strategy.partition_circuit(circuit, nodes, config)
    }

    fn estimate_execution_time(&self, partition: &CircuitPartition, node: &NodeInfo) -> Duration {
        Duration::from_millis(partition.gates.len() as u64 * 10)
    }

    fn calculate_communication_overhead(
        &self,
        partitions: &[CircuitPartition],
        nodes: &HashMap<NodeId, NodeInfo>,
    ) -> f64 {
        partitions.len() as f64 * 0.1
    }
}

impl Default for PartitionOptimizer {
    fn default() -> Self {
        Self::new()
    }
}

impl PartitionOptimizer {
    pub fn new() -> Self {
        Self {
            objectives: vec![
                OptimizationObjective::MinimizeLatency { weight: 0.3 },
                OptimizationObjective::MaximizeThroughput { weight: 0.3 },
                OptimizationObjective::MinimizeResourceUsage { weight: 0.4 },
            ],
            solver: "genetic_algorithm".to_string(),
            timeout: Duration::from_secs(30),
        }
    }
}

impl Default for DistributedStateManager {
    fn default() -> Self {
        Self::new()
    }
}

impl DistributedStateManager {
    pub fn new() -> Self {
        Self {
            local_states: Arc::new(RwLock::new(HashMap::new())),
            entanglement_registry: Arc::new(RwLock::new(HashMap::new())),
            synchronization_protocol: Arc::new(BasicSynchronizationProtocol::new()),
            state_transfer_engine: Arc::new(StateTransferEngine::new()),
            consistency_checker: Arc::new(ConsistencyChecker::new()),
        }
    }
}

/// Basic synchronization protocol implementation
#[derive(Debug)]
pub struct BasicSynchronizationProtocol;

impl Default for BasicSynchronizationProtocol {
    fn default() -> Self {
        Self::new()
    }
}

impl BasicSynchronizationProtocol {
    pub const fn new() -> Self {
        Self
    }
}

#[async_trait]
impl StateSynchronizationProtocol for BasicSynchronizationProtocol {
    async fn synchronize_states(
        &self,
        nodes: &[NodeId],
        target_consistency: f64,
    ) -> Result<SynchronizationResult> {
        Ok(SynchronizationResult {
            success: true,
            consistency_level: target_consistency,
            synchronized_nodes: nodes.to_vec(),
            failed_nodes: vec![],
            synchronization_time: Duration::from_millis(50),
        })
    }

    async fn detect_inconsistencies(
        &self,
        states: &HashMap<NodeId, LocalQuantumState>,
    ) -> Vec<Inconsistency> {
        vec![] // Simplified
    }

    async fn resolve_conflicts(&self, conflicts: &[StateConflict]) -> Result<Resolution> {
        Ok(Resolution {
            strategy: ResolutionStrategy::LastWriterWins,
            resolved_conflicts: conflicts.iter().map(|c| c.conflict_id).collect(),
            unresolved_conflicts: vec![],
            resolution_time: Duration::from_millis(10),
        })
    }
}

impl Default for StateTransferEngine {
    fn default() -> Self {
        Self::new()
    }
}

impl StateTransferEngine {
    pub fn new() -> Self {
        Self {
            transfer_protocols: HashMap::new(),
            compression_engine: Arc::new(QuantumStateCompressor::new()),
            encryption_engine: Arc::new(QuantumCryptography::new()),
        }
    }
}

impl Default for QuantumStateCompressor {
    fn default() -> Self {
        Self::new()
    }
}

impl QuantumStateCompressor {
    pub fn new() -> Self {
        Self {
            compression_algorithms: vec![
                "quantum_huffman".to_string(),
                "schmidt_decomposition".to_string(),
            ],
            compression_ratio_target: 0.5,
            fidelity_preservation_threshold: 0.99,
        }
    }
}

impl Default for QuantumCryptography {
    fn default() -> Self {
        Self::new()
    }
}

impl QuantumCryptography {
    pub fn new() -> Self {
        Self {
            encryption_protocols: vec![
                "quantum_key_distribution".to_string(),
                "post_quantum_crypto".to_string(),
            ],
            key_distribution_method: "BB84".to_string(),
            security_level: 256,
        }
    }
}

impl Default for ConsistencyChecker {
    fn default() -> Self {
        Self::new()
    }
}

impl ConsistencyChecker {
    pub fn new() -> Self {
        Self {
            consistency_protocols: vec![
                "eventual_consistency".to_string(),
                "strong_consistency".to_string(),
            ],
            verification_frequency: Duration::from_secs(1),
            automatic_correction: true,
        }
    }
}

impl Default for CapabilityBasedBalancer {
    fn default() -> Self {
        Self::new()
    }
}

impl CapabilityBasedBalancer {
    pub fn new() -> Self {
        let mut capability_weights = HashMap::new();
        capability_weights.insert("qubit_count".to_string(), 0.3);
        capability_weights.insert("gate_fidelity".to_string(), 0.4);
        capability_weights.insert("connectivity".to_string(), 0.3);

        Self {
            capability_weights,
            performance_history: Arc::new(RwLock::new(HashMap::new())),
        }
    }
}

#[async_trait]
impl LoadBalancer for CapabilityBasedBalancer {
    fn select_nodes(
        &self,
        partitions: &[CircuitPartition],
        available_nodes: &HashMap<NodeId, NodeInfo>,
        requirements: &ExecutionRequirements,
    ) -> Result<HashMap<Uuid, NodeId>> {
        let mut allocation = HashMap::new();

        for partition in partitions {
            if let Some((node_id, _)) = available_nodes.iter().next() {
                allocation.insert(partition.partition_id, node_id.clone());
            }
        }

        Ok(allocation)
    }

    fn rebalance_load(
        &self,
        current_allocation: &HashMap<Uuid, NodeId>,
        nodes: &HashMap<NodeId, NodeInfo>,
    ) -> Option<HashMap<Uuid, NodeId>> {
        None // No rebalancing needed in simplified implementation
    }

    fn predict_execution_time(&self, partition: &CircuitPartition, node: &NodeInfo) -> Duration {
        Duration::from_millis(partition.gates.len() as u64 * 10)
    }

    async fn select_node(
        &self,
        available_nodes: &[NodeInfo],
        requirements: &ResourceRequirements,
    ) -> Result<NodeId> {
        // Select the first available node that meets requirements
        available_nodes
            .iter()
            .find(|node| {
                node.capabilities.max_qubits >= requirements.qubits_needed
                    && node
                        .capabilities
                        .gate_fidelities
                        .values()
                        .all(|&fidelity| fidelity >= 0.999) // Default threshold (equivalent to error rate <= 0.001)
            })
            .map(|node| node.node_id.clone())
            .ok_or_else(|| {
                DistributedComputationError::NodeSelectionFailed(
                    "No suitable node found".to_string(),
                )
            })
    }

    async fn update_node_metrics(
        &self,
        node_id: &NodeId,
        metrics: &PerformanceMetrics,
    ) -> Result<()> {
        // Update metrics for the specified node
        // In a real implementation, this would update internal state
        Ok(())
    }

    fn get_balancer_metrics(&self) -> LoadBalancerMetrics {
        LoadBalancerMetrics {
            total_decisions: 0,
            average_decision_time: Duration::from_millis(1),
            prediction_accuracy: 1.0,
            load_distribution_variance: 0.0,
            total_requests: 0,
            successful_allocations: 0,
            failed_allocations: 0,
            average_response_time: Duration::from_millis(0),
            node_utilization: HashMap::new(),
        }
    }
}

impl Default for FaultToleranceManager {
    fn default() -> Self {
        Self::new()
    }
}

impl FaultToleranceManager {
    pub fn new() -> Self {
        Self {
            fault_detectors: vec![],
            recovery_strategies: HashMap::new(),
            checkpointing_system: Arc::new(CheckpointingSystem::new()),
            redundancy_manager: Arc::new(RedundancyManager::new()),
        }
    }
}

impl Default for CheckpointingSystem {
    fn default() -> Self {
        Self::new()
    }
}

impl CheckpointingSystem {
    pub fn new() -> Self {
        Self {
            checkpoint_storage: Arc::new(InMemoryCheckpointStorage::new()),
            checkpoint_frequency: Duration::from_secs(60),
            compression_enabled: true,
            incremental_checkpoints: true,
        }
    }
}

/// In-memory checkpoint storage for testing
#[derive(Debug)]
pub struct InMemoryCheckpointStorage {
    checkpoints: Arc<RwLock<HashMap<Uuid, CheckpointData>>>,
}

impl Default for InMemoryCheckpointStorage {
    fn default() -> Self {
        Self::new()
    }
}

impl InMemoryCheckpointStorage {
    pub fn new() -> Self {
        Self {
            checkpoints: Arc::new(RwLock::new(HashMap::new())),
        }
    }
}

#[async_trait]
impl CheckpointStorage for InMemoryCheckpointStorage {
    async fn store_checkpoint(&self, checkpoint_id: Uuid, data: &CheckpointData) -> Result<()> {
        let mut checkpoints = self
            .checkpoints
            .write()
            .expect("Checkpoints RwLock poisoned");
        checkpoints.insert(checkpoint_id, data.clone());
        Ok(())
    }

    async fn load_checkpoint(&self, checkpoint_id: Uuid) -> Result<CheckpointData> {
        let checkpoints = self
            .checkpoints
            .read()
            .expect("Checkpoints RwLock poisoned");
        checkpoints.get(&checkpoint_id).cloned().ok_or_else(|| {
            DistributedComputationError::ResourceAllocation("Checkpoint not found".to_string())
        })
    }

    async fn list_checkpoints(&self) -> Result<Vec<Uuid>> {
        let checkpoints = self
            .checkpoints
            .read()
            .expect("Checkpoints RwLock poisoned");
        Ok(checkpoints.keys().copied().collect())
    }

    async fn delete_checkpoint(&self, checkpoint_id: Uuid) -> Result<()> {
        let mut checkpoints = self
            .checkpoints
            .write()
            .expect("Checkpoints RwLock poisoned");
        checkpoints.remove(&checkpoint_id);
        Ok(())
    }
}

impl Default for RedundancyManager {
    fn default() -> Self {
        Self::new()
    }
}

impl RedundancyManager {
    pub fn new() -> Self {
        Self {
            redundancy_strategies: HashMap::new(),
            replication_factor: 3,
            consistency_protocol: "eventual_consistency".to_string(),
        }
    }
}

impl Default for RaftConsensus {
    fn default() -> Self {
        Self::new()
    }
}

impl RaftConsensus {
    pub fn new() -> Self {
        Self {
            election_timeout: Duration::from_millis(500),
            heartbeat_interval: Duration::from_millis(100),
            log_replication: Arc::new(LogReplication::new()),
            leader_state: Arc::new(RwLock::new(LeaderState {
                current_leader: None,
                term: 0,
                last_heartbeat: Utc::now(),
            })),
        }
    }
}

#[async_trait]
impl ConsensusEngine for RaftConsensus {
    async fn reach_consensus<T: Serialize + for<'de> Deserialize<'de> + Clone + Send>(
        &self,
        proposal: T,
        participants: &[NodeId],
        timeout: Duration,
    ) -> Result<ConsensusResult<T>> {
        Ok(ConsensusResult {
            decision: proposal,
            consensus_achieved: true,
            participating_nodes: participants.to_vec(),
            consensus_time: Duration::from_millis(50),
            confidence: 0.95,
        })
    }

    async fn elect_leader(&self, candidates: &[NodeId], timeout: Duration) -> Result<NodeId> {
        candidates.first().cloned().ok_or_else(|| {
            DistributedComputationError::ConsensusFailure(
                "No candidates for leader election".to_string(),
            )
        })
    }

    fn get_consensus_confidence(&self) -> f64 {
        0.95
    }
}

impl Default for LogReplication {
    fn default() -> Self {
        Self::new()
    }
}

impl LogReplication {
    pub fn new() -> Self {
        Self {
            log_entries: Arc::new(RwLock::new(vec![])),
            commit_index: Arc::new(RwLock::new(0)),
            last_applied: Arc::new(RwLock::new(0)),
        }
    }
}

impl Default for MetricsCollector {
    fn default() -> Self {
        Self::new()
    }
}

impl MetricsCollector {
    pub fn new() -> Self {
        Self {
            metrics_storage: Arc::new(InMemoryMetricsStorage::new()),
            collection_interval: Duration::from_secs(1),
            metrics_aggregator: Arc::new(MetricsAggregator::new()),
            alerting_system: Arc::new(AlertingSystem::new()),
        }
    }
}

/// In-memory metrics storage for testing
#[derive(Debug)]
pub struct InMemoryMetricsStorage {
    metrics: Arc<RwLock<Vec<Metric>>>,
}

impl Default for InMemoryMetricsStorage {
    fn default() -> Self {
        Self::new()
    }
}

impl InMemoryMetricsStorage {
    pub fn new() -> Self {
        Self {
            metrics: Arc::new(RwLock::new(vec![])),
        }
    }
}

#[async_trait]
impl MetricsStorage for InMemoryMetricsStorage {
    async fn store_metric(&self, metric: &Metric) -> Result<()> {
        let mut metrics = self.metrics.write().expect("Metrics RwLock poisoned");
        metrics.push(metric.clone());
        Ok(())
    }

    async fn query_metrics(&self, query: &MetricsQuery) -> Result<Vec<Metric>> {
        let metrics = self.metrics.read().expect("Metrics RwLock poisoned");
        let filtered: Vec<Metric> = metrics
            .iter()
            .filter(|m| {
                query.metric_names.contains(&m.metric_name)
                    && m.timestamp >= query.time_range.0
                    && m.timestamp <= query.time_range.1
            })
            .cloned()
            .collect();
        Ok(filtered)
    }

    async fn aggregate_metrics(&self, aggregation: &AggregationQuery) -> Result<AggregatedMetrics> {
        let metrics = self.metrics.read().expect("Metrics RwLock poisoned");
        let filtered: Vec<&Metric> = metrics
            .iter()
            .filter(|m| {
                m.metric_name == aggregation.metric_name
                    && m.timestamp >= aggregation.time_range.0
                    && m.timestamp <= aggregation.time_range.1
            })
            .collect();

        let value = match aggregation.aggregation_function {
            AggregationFunction::Average => {
                let sum: f64 = filtered.iter().map(|m| m.value).sum();
                if filtered.is_empty() {
                    0.0
                } else {
                    sum / filtered.len() as f64
                }
            }
            AggregationFunction::Sum => filtered.iter().map(|m| m.value).sum(),
            AggregationFunction::Max => filtered
                .iter()
                .map(|m| m.value)
                .fold(f64::NEG_INFINITY, f64::max),
            AggregationFunction::Min => filtered
                .iter()
                .map(|m| m.value)
                .fold(f64::INFINITY, f64::min),
            AggregationFunction::Count => filtered.len() as f64,
            _ => 0.0, // Simplified for other functions
        };

        Ok(AggregatedMetrics {
            metric_name: aggregation.metric_name.clone(),
            aggregation_function: aggregation.aggregation_function.clone(),
            value,
            time_range: aggregation.time_range,
            group_by_values: HashMap::new(),
        })
    }
}

impl Default for MetricsAggregator {
    fn default() -> Self {
        Self::new()
    }
}

impl MetricsAggregator {
    pub const fn new() -> Self {
        Self {
            aggregation_strategies: vec![],
            real_time_aggregation: true,
            batch_size: 1000,
        }
    }
}

impl Default for AlertingSystem {
    fn default() -> Self {
        Self::new()
    }
}

impl AlertingSystem {
    pub fn new() -> Self {
        Self {
            alert_rules: vec![],
            notification_channels: HashMap::new(),
            alert_history: Arc::new(RwLock::new(VecDeque::new())),
        }
    }
}

impl Default for ResourceAllocator {
    fn default() -> Self {
        Self::new()
    }
}

impl ResourceAllocator {
    pub fn new() -> Self {
        Self {
            allocation_strategies: HashMap::new(),
            resource_monitor: Arc::new(ResourceMonitor::new()),
            allocation_history: Arc::new(RwLock::new(VecDeque::new())),
        }
    }

    fn allocate_resources_for_partitions(
        &self,
        partitions: &[CircuitPartition],
        nodes: &HashMap<NodeId, NodeInfo>,
    ) -> Result<AllocationPlan> {
        let mut allocations = HashMap::new();

        for (node_id, node_info) in nodes {
            allocations.insert(
                node_id.clone(),
                ResourceAllocation {
                    allocated_qubits: vec![],
                    memory_allocated_mb: 100,
                    cpu_allocated_percentage: 50.0,
                    network_bandwidth_allocated_mbps: 100.0,
                },
            );
        }

        Ok(AllocationPlan {
            plan_id: Uuid::new_v4(),
            allocations,
            estimated_cost: 100.0,
            estimated_execution_time: Duration::from_secs(10),
            allocation_timestamp: Utc::now(),
        })
    }
}

impl Default for ResourceMonitor {
    fn default() -> Self {
        Self::new()
    }
}

impl ResourceMonitor {
    pub fn new() -> Self {
        Self {
            monitoring_agents: HashMap::new(),
            monitoring_interval: Duration::from_secs(1),
            resource_predictions: Arc::new(ResourcePredictor::new()),
        }
    }
}

impl Default for ResourcePredictor {
    fn default() -> Self {
        Self::new()
    }
}

impl ResourcePredictor {
    pub fn new() -> Self {
        Self {
            prediction_models: HashMap::new(),
            training_scheduler: Arc::new(TrainingScheduler::new()),
            model_evaluator: Arc::new(ModelEvaluator::new()),
        }
    }
}

impl Default for TrainingScheduler {
    fn default() -> Self {
        Self::new()
    }
}

impl TrainingScheduler {
    pub fn new() -> Self {
        Self {
            training_schedule: HashMap::new(),
            auto_retraining: true,
            performance_threshold: 0.9,
        }
    }
}

impl Default for ModelEvaluator {
    fn default() -> Self {
        Self::new()
    }
}

impl ModelEvaluator {
    pub fn new() -> Self {
        Self {
            evaluation_metrics: vec![
                "accuracy".to_string(),
                "precision".to_string(),
                "recall".to_string(),
            ],
            cross_validation_folds: 5,
            benchmark_datasets: HashMap::new(),
        }
    }
}