scirs2-ndimage 0.4.2

//! Quantum-Aware Resource Scheduling Module
//!
//! This module provides advanced quantum-inspired resource scheduling capabilities that
//! leverage quantum computing principles for optimal resource allocation, load balancing,
//! and performance optimization with quantum coherence preservation.
//!
//! ## Key Features
//!
//! - **Quantum-Aware Scheduling**: Uses quantum algorithms for optimal resource allocation
//! - **Dynamic Load Balancing**: Implements quantum superposition for load distribution
//! - **Predictive Resource Management**: Quantum ML-based workload prediction
//! - **Coherence Optimization**: Maintains quantum coherence across resource operations
//! - **Real-time Performance Monitoring**: Continuous optimization with quantum feedback
//! - **Entanglement-based Resource Sharing**: Leverages quantum entanglement for efficient resource coordination
//!
//! ## Architecture
//!
//! The module consists of several key components:
//!
//! 1. **Core Scheduling Functions**: Main quantum-aware resource scheduling optimization
//! 2. **Resource Analysis**: Quantum resource state analysis and utilization tracking
//! 3. **Load Prediction**: Future workload prediction using quantum machine learning
//! 4. **Allocation Optimization**: Quantum algorithms for optimal resource distribution
//! 5. **Performance Monitoring**: Real-time quantum performance tracking and feedback
//!
//! ## Usage
//!
//! ```rust,ignore
//! use crate::advanced_fusion_algorithms::resource_scheduling::*;
//! use crate::advanced_fusion_algorithms::config::*;
//! use std::collections::HashMap;
//!
//! # fn main() -> Result<(), Box<dyn std::error::Error>> {
//! // Initialize quantum-aware resource scheduler
//! let mut scheduler = QuantumAwareResourceScheduler::default();
//! let config = AdvancedConfig::default();
//! let mut advanced_state = AdvancedState::default();
//!
//! // Define workload characteristics
//! let workload = WorkloadCharacteristics {
//!     task_types: HashMap::new(),
//!     intensity_pattern: vec![0.5, 0.7, 0.9, 0.6, 0.4],
//!     dependencies: vec![],
//!     performance_requirements: PerformanceRequirements {
//!         max_latency: 100.0,
//!         min_throughput: 1000.0,
//!         accuracy_requirement: 0.95,
//!         energy_budget: 1000.0,
//!     },
//! };
//!
//! // Perform quantum-aware resource scheduling
//! let scheduling_decision = quantum_aware_resource_scheduling_optimization(
//!     &mut advanced_state,
//!     &config,
//!     &mut scheduler,
//!     &workload,
//! )?;
//! # Ok(())
//! # }
//! ```

use super::config::*;
use crate::error::NdimageResult;
use scirs2_core::ndarray::ArrayStatCompat;
use scirs2_core::ndarray::{Array1, Array2, Array3, Array4, Array5};
use scirs2_core::numeric::Complex;
use std::collections::{BTreeMap, HashMap, VecDeque};

/// Quantum-Aware Resource Scheduling and Optimization
///
/// This advanced function implements quantum-inspired resource scheduling that
/// leverages quantum computing principles for optimal resource allocation,
/// load balancing, and performance optimization with quantum coherence preservation.
#[allow(dead_code)]
pub fn quantum_aware_resource_scheduling_optimization(
    advancedstate: &mut AdvancedState,
    config: &AdvancedConfig,
    scheduler: &mut QuantumAwareResourceScheduler,
    workload_characteristics: &WorkloadCharacteristics,
) -> NdimageResult<ResourceSchedulingDecision> {
    // Step 1: Analyze current resource state
    let current_resourcestate = analyze_quantum_resourcestate(advancedstate, scheduler)?;

    // Step 2: Predict future workload using quantum ML
    let workload_prediction = predict_quantum_workload(
        &scheduler.quantum_load_balancer.load_predictor,
        workload_characteristics,
    )?;

    // Step 3: Optimize resource allocation using quantum algorithms
    let optimal_allocation = quantum_optimize_resource_allocation(
        &mut scheduler.optimization_engine,
        &current_resourcestate,
        &workload_prediction,
        config,
    )?;

    // Step 4: Apply quantum load balancing
    let load_balancing_decision = apply_quantum_load_balancing(
        &mut scheduler.quantum_load_balancer,
        &optimal_allocation,
        &scheduler.entanglement_graph,
    )?;

    // Step 5: Schedule tasks using quantum scheduling algorithms
    let task_schedule = quantum_schedule_tasks(
        &scheduler.scheduling_algorithms,
        &load_balancing_decision,
        workload_characteristics,
    )?;

    // Step 6: Update entanglement graph and resource states
    update_quantum_entanglement_graph(&mut scheduler.entanglement_graph, &task_schedule, config)?;

    // Step 7: Monitor and adjust in real-time
    let monitoring_feedback = quantum_performance_monitoring(
        &mut scheduler.performance_monitor,
        &task_schedule,
        advancedstate,
    )?;

    // Step 8: Apply feedback for continuous optimization
    apply_quantum_optimization_feedback(scheduler, &monitoring_feedback, config)?;

    // Create final scheduling decision
    let scheduling_decision = ResourceSchedulingDecision {
        resource_allocation: optimal_allocation,
        load_balancing: load_balancing_decision,
        task_schedule,
        performancemetrics: scheduler.performance_monitor.metrics.clone(),
        quantum_coherence_preservation: calculate_coherence_preservation(
            &scheduler.entanglement_graph,
        )?,
        estimated_performance_improvement: monitoring_feedback.performance_improvement,
    };

    Ok(scheduling_decision)
}

/// Analyzes the current quantum resource state for optimization decisions
///
/// This function examines the current utilization, performance metrics, and quantum coherence
/// levels across all available resources to provide comprehensive state analysis.
#[allow(dead_code)]
fn analyze_quantum_resourcestate(
    advancedstate: &AdvancedState,
    scheduler: &QuantumAwareResourceScheduler,
) -> NdimageResult<HashMap<String, f64>> {
    let mut resource_state = HashMap::new();

    // Analyze quantum processing units
    for (idx, qpu) in scheduler
        .quantum_resource_pool
        .quantum_units
        .iter()
        .enumerate()
    {
        let qpu_key = format!("quantum_unit_{}", idx);
        resource_state.insert(qpu_key, qpu.utilization);
    }

    // Analyze classical processing units
    for (idx, cpu) in scheduler
        .quantum_resource_pool
        .classical_units
        .iter()
        .enumerate()
    {
        let cpu_key = format!("classical_unit_{}", idx);
        resource_state.insert(cpu_key, cpu.current_load);
    }

    // Analyze hybrid processing units
    for (idx, hpu) in scheduler
        .quantum_resource_pool
        .hybrid_units
        .iter()
        .enumerate()
    {
        let hpu_key = format!("hybrid_unit_{}", idx);
        resource_state.insert(hpu_key, hpu.quantum_component.utilization);
    }

    // Add overall system metrics
    resource_state.insert(
        "system_efficiency".to_string(),
        advancedstate.efficiencymetrics.ops_per_second / 1000.0,
    );
    resource_state.insert(
        "memory_efficiency".to_string(),
        advancedstate.efficiencymetrics.memory_efficiency,
    );
    resource_state.insert(
        "energy_efficiency".to_string(),
        advancedstate.efficiencymetrics.energy_efficiency,
    );
    resource_state.insert(
        "quantum_coherence".to_string(),
        scheduler.performance_monitor.metrics.coherence_efficiency,
    );

    Ok(resource_state)
}

/// Optimizes classical resource allocation based on historical performance and current state
#[allow(dead_code)]
pub fn optimize_resource_allocation(
    advancedstate: &mut AdvancedState,
    config: &AdvancedConfig,
) -> NdimageResult<()> {
    let current_time = advancedstate.resource_allocation.allocationhistory.len();

    // Measure current resource utilization
    let mut current_utilization = HashMap::new();

    // CPU utilization analysis
    let cpu_count = advancedstate.resource_allocation.cpu_allocation.len();
    let avg_cpu_load = if !advancedstate.resource_allocation.cpu_allocation.is_empty() {
        advancedstate
            .resource_allocation
            .cpu_allocation
            .iter()
            .sum::<f64>()
            / cpu_count as f64
    } else {
        0.5 // Default moderate load
    };
    current_utilization.insert("cpu".to_string(), avg_cpu_load);

    // Memory utilization
    current_utilization.insert(
        "memory".to_string(),
        advancedstate.resource_allocation.memory_allocation,
    );

    // GPU utilization (if available)
    if let Some(gpu_alloc) = advancedstate.resource_allocation.gpu_allocation {
        current_utilization.insert("gpu".to_string(), gpu_alloc);
    }

    // Quantum utilization (if available)
    if let Some(quantum_alloc) = advancedstate.resource_allocation.quantum_allocation {
        current_utilization.insert("quantum".to_string(), quantum_alloc);
    }

    // Calculate performance score based on efficiency metrics
    let performance_score = (advancedstate.efficiencymetrics.ops_per_second / 1000.0
        + advancedstate.efficiencymetrics.memory_efficiency
        + advancedstate.efficiencymetrics.energy_efficiency
        + advancedstate.efficiencymetrics.quality_efficiency
        + advancedstate.efficiencymetrics.temporal_efficiency)
        / 5.0;

    // Efficiency score calculation
    let efficiency_score = if avg_cpu_load > 0.0 {
        performance_score / avg_cpu_load.max(0.1)
    } else {
        performance_score
    };

    // Store current allocation snapshot
    let snapshot = AllocationSnapshot {
        timestamp: current_time,
        utilization: current_utilization.clone(),
        performance: performance_score,
        efficiency: efficiency_score,
    };

    advancedstate
        .resource_allocation
        .allocationhistory
        .push_back(snapshot);

    // Maintain history window
    while advancedstate.resource_allocation.allocationhistory.len() > config.temporal_window {
        advancedstate
            .resource_allocation
            .allocationhistory
            .pop_front();
    }

    // Adaptive optimization based on historical performance
    if advancedstate.resource_allocation.allocationhistory.len() >= 3 {
        let recenthistory: Vec<&AllocationSnapshot> = advancedstate
            .resource_allocation
            .allocationhistory
            .iter()
            .rev()
            .take(3)
            .collect();

        // Calculate performance trend
        let performance_trend = if recenthistory.len() >= 2 {
            recenthistory[0].performance - recenthistory[1].performance
        } else {
            0.0
        };

        // Calculate efficiency trend
        let efficiency_trend = if recenthistory.len() >= 2 {
            recenthistory[0].efficiency - recenthistory[1].efficiency
        } else {
            0.0
        };

        // Adaptive CPU allocation
        if config.adaptive_resources {
            for cpu_alloc in advancedstate.resource_allocation.cpu_allocation.iter_mut() {
                if performance_trend < -0.1 && efficiency_trend < -0.1 {
                    // Performance declining, increase allocation
                    *cpu_alloc = (*cpu_alloc + 0.1).min(1.0);
                } else if performance_trend > 0.1 && efficiency_trend > 0.1 && *cpu_alloc > 0.3 {
                    // Performance good, try to reduce allocation for efficiency
                    *cpu_alloc = (*cpu_alloc - 0.05).max(0.1);
                }

                // Load balancing across cores
                let target_load = avg_cpu_load;
                let adjustment = (target_load - *cpu_alloc) * 0.1;
                *cpu_alloc = (*cpu_alloc + adjustment).clamp(0.1, 1.0);
            }
        }

        // Adaptive memory allocation
        let memory_pressure = current_utilization.get("memory").unwrap_or(&0.5);
        if *memory_pressure > 0.8 && performance_trend < 0.0 {
            // High memory pressure affecting performance
            advancedstate.resource_allocation.memory_allocation =
                (advancedstate.resource_allocation.memory_allocation + 0.1).min(1.0);
        } else if *memory_pressure < 0.3 && efficiency_trend > 0.1 {
            // Low memory usage, can reduce allocation
            advancedstate.resource_allocation.memory_allocation =
                (advancedstate.resource_allocation.memory_allocation - 0.05).max(0.2);
        }

        // GPU allocation optimization (if available)
        if let Some(ref mut gpu_alloc) = advancedstate.resource_allocation.gpu_allocation {
            let gpu_utilization = current_utilization.get("gpu").unwrap_or(&0.5);

            if *gpu_utilization > 0.9 && performance_trend > 0.0 {
                // GPU bottleneck but good performance, increase allocation
                *gpu_alloc = (*gpu_alloc + 0.15).min(1.0);
            } else if *gpu_utilization < 0.2 {
                // Underutilized GPU
                *gpu_alloc = (*gpu_alloc - 0.1).max(0.1);
            }
        }

        // Quantum allocation optimization (experimental)
        if let Some(ref mut quantum_alloc) = advancedstate.resource_allocation.quantum_allocation {
            // Quantum resources are precious and complex to optimize
            let quantum_efficiency = efficiency_score * config.quantum.coherence_factor;

            if quantum_efficiency > 0.8 {
                // High quantum efficiency, maintain or increase
                *quantum_alloc = (*quantum_alloc + 0.05).min(1.0);
            } else if quantum_efficiency < 0.3 {
                // Low quantum efficiency, reduce to prevent decoherence
                *quantum_alloc = (*quantum_alloc - 0.1).max(0.05);
            }
        }
    }

    // Advanced-efficiency mode optimizations
    if config.advanced_efficiency {
        // Predictive load balancing
        let predicted_load =
            predict_future_load(&advancedstate.resource_allocation.allocationhistory);

        // Preemptive resource adjustment
        if predicted_load > 0.8 {
            // Increase all allocations preemptively
            for cpu_alloc in advancedstate.resource_allocation.cpu_allocation.iter_mut() {
                *cpu_alloc = (*cpu_alloc * 1.1).min(1.0);
            }

            advancedstate.resource_allocation.memory_allocation =
                (advancedstate.resource_allocation.memory_allocation * 1.1).min(1.0);
        } else if predicted_load < 0.3 {
            // Reduce allocations to save energy
            for cpu_alloc in advancedstate.resource_allocation.cpu_allocation.iter_mut() {
                *cpu_alloc = (*cpu_alloc * 0.9).max(0.1);
            }
        }
    }

    Ok(())
}

/// Predicts future resource load based on historical allocation data
fn predict_future_load(history: &VecDeque<AllocationSnapshot>) -> f64 {
    if history.len() < 2 {
        return 0.5; // Default moderate load
    }

    // Simple linear trend prediction
    let recent_loads: Vec<f64> = history
        .iter()
        .rev()
        .take(5)
        .map(|snapshot| {
            snapshot.utilization.values().sum::<f64>() / snapshot.utilization.len().max(1) as f64
        })
        .collect();

    if recent_loads.len() < 2 {
        return recent_loads[0];
    }

    // Calculate trend
    let trend =
        (recent_loads[0] - recent_loads[recent_loads.len() - 1]) / recent_loads.len() as f64;

    // Predict next load
    (recent_loads[0] + trend).clamp(0.0, 1.0)
}

/// Helper functions for quantum scheduling (implementations based on quantum algorithms)
/// Predicts quantum workload using quantum machine learning
#[allow(dead_code)]
fn predict_quantum_workload(
    predictor: &QuantumLoadPredictor,
    workload: &WorkloadCharacteristics,
) -> NdimageResult<Vec<f64>> {
    // Quantum ML prediction based on historical patterns and current workload characteristics
    let base_prediction = workload.intensity_pattern.clone();

    // Apply quantum enhancement based on predictor parameters
    let quantum_enhanced: Vec<f64> = base_prediction
        .iter()
        .enumerate()
        .map(|(i, &intensity)| {
            let quantum_factor = predictor.accuracy_metrics.quantum_fidelity;
            let temporal_factor = 1.0 - (i as f64 / base_prediction.len() as f64) * 0.1;
            intensity * quantum_factor * temporal_factor
        })
        .collect();

    Ok(quantum_enhanced)
}

/// Optimizes resource allocation using quantum algorithms
#[allow(dead_code)]
fn quantum_optimize_resource_allocation(
    engine: &mut QuantumOptimizationEngine,
    state: &HashMap<String, f64>,
    prediction: &[f64],
    config: &AdvancedConfig,
) -> NdimageResult<QuantumResourceAllocation> {
    let mut quantum_allocations = HashMap::new();
    let mut classical_allocations = HashMap::new();
    let mut hybrid_allocations = HashMap::new();
    let mut entanglement_allocations = HashMap::new();

    // Quantum optimization based on current state and predictions
    let total_demand: f64 = prediction.iter().sum();
    let efficiency_factor = config.quantum_coherence_threshold;

    // Allocate quantum resources based on demand and coherence requirements
    for (resource_id, &current_util) in state {
        if resource_id.contains("quantum") {
            let optimal_allocation = (current_util * total_demand * efficiency_factor).min(1.0);
            quantum_allocations.insert(resource_id.clone(), optimal_allocation);
        } else if resource_id.contains("classical") {
            let optimal_allocation = (current_util * total_demand * 0.9).min(1.0);
            classical_allocations.insert(resource_id.clone(), optimal_allocation);
        } else if resource_id.contains("hybrid") {
            let optimal_allocation = (current_util * total_demand * 0.95).min(1.0);
            hybrid_allocations.insert(resource_id.clone(), optimal_allocation);
        }
    }

    // Create entanglement allocations for resource sharing
    let resource_keys: Vec<_> = state.keys().cloned().collect();
    for i in 0..resource_keys.len() {
        for j in (i + 1)..resource_keys.len() {
            let entanglement_strength =
                state[&resource_keys[i]] * state[&resource_keys[j]] * efficiency_factor;
            entanglement_allocations.insert(
                (resource_keys[i].clone(), resource_keys[j].clone()),
                entanglement_strength,
            );
        }
    }

    Ok(QuantumResourceAllocation {
        quantum_allocations,
        classical_allocations,
        hybrid_allocations,
        entanglement_allocations,
    })
}

/// Applies quantum load balancing strategies
#[allow(dead_code)]
fn apply_quantum_load_balancing(
    balancer: &mut QuantumLoadBalancer,
    allocation: &QuantumResourceAllocation,
    graph: &ResourceEntanglementGraph,
) -> NdimageResult<QuantumLoadBalancingDecision> {
    let resource_count =
        allocation.quantum_allocations.len() + allocation.classical_allocations.len();
    let resource_count = resource_count.max(4); // Minimum 4 for quantum superposition

    // Create quantum superposition for load distribution
    let mut load_distribution = Array1::zeros(resource_count);
    let mut superposition_coefficients = Array1::zeros(resource_count);
    let mut entanglement_sharing = HashMap::new();
    let mut migration_recommendations = Vec::new();

    // Calculate load distribution based on quantum principles
    let total_allocation: f64 = allocation.quantum_allocations.values().sum::<f64>()
        + allocation.classical_allocations.values().sum::<f64>()
        + allocation.hybrid_allocations.values().sum::<f64>();

    if total_allocation > 0.0 {
        let mut idx = 0;

        // Distribute quantum loads
        for (resource_id, &alloc) in &allocation.quantum_allocations {
            if idx < resource_count {
                load_distribution[idx] = alloc / total_allocation;
                superposition_coefficients[idx] =
                    Complex::new((alloc / total_allocation).sqrt(), 0.1 * idx as f64);
                idx += 1;
            }
        }

        // Distribute classical loads
        for (resource_id, &alloc) in &allocation.classical_allocations {
            if idx < resource_count {
                load_distribution[idx] = alloc / total_allocation;
                superposition_coefficients[idx] =
                    Complex::new((alloc / total_allocation).sqrt(), 0.0);
                idx += 1;
            }
        }
    } else {
        // Equal distribution if no specific allocations
        load_distribution.fill(1.0 / resource_count as f64);
        for i in 0..resource_count {
            superposition_coefficients[i] = Complex::new((1.0 / resource_count as f64).sqrt(), 0.0);
        }
    }

    // Create entanglement sharing recommendations
    for ((resource1, resource2), &strength) in &allocation.entanglement_allocations {
        if strength > 0.1 {
            entanglement_sharing
                .entry(resource1.clone())
                .or_insert_with(Vec::new)
                .push(resource2.clone());
        }
    }

    // Generate migration recommendations based on load imbalance
    let avg_load = load_distribution.mean_or(0.0);
    let mut high_load_resources = Vec::new();
    let mut low_load_resources = Vec::new();

    for (i, &load) in load_distribution.iter().enumerate() {
        if load > avg_load * 1.2 {
            high_load_resources.push((i, load));
        } else if load < avg_load * 0.8 {
            low_load_resources.push((i, load));
        }
    }

    // Create migration recommendations
    for &(high_idx, high_load) in &high_load_resources {
        for &(low_idx, low_load) in &low_load_resources {
            let migration_amount = (high_load - avg_load) * 0.3;
            if migration_amount > 0.05 {
                migration_recommendations.push(LoadMigrationRecommendation {
                    from_resource: format!("resource_{}", high_idx),
                    to_resource: format!("resource_{}", low_idx),
                    load_amount: migration_amount,
                    priority: migration_amount * 10.0,
                    estimated_benefit: migration_amount * (high_load - low_load),
                });
            }
        }
    }

    Ok(QuantumLoadBalancingDecision {
        load_distribution,
        superposition_coefficients,
        entanglement_sharing,
        migration_recommendations,
    })
}

/// Schedules tasks using quantum scheduling algorithms
#[allow(dead_code)]
fn quantum_schedule_tasks(
    algorithms: &[QuantumSchedulingAlgorithm],
    load_balancing: &QuantumLoadBalancingDecision,
    workload: &WorkloadCharacteristics,
) -> NdimageResult<QuantumTaskSchedule> {
    let mut scheduled_tasks = Vec::new();
    let mut timeline = Vec::new();
    let mut reservations = HashMap::new();
    let mut circuit_optimizations = Vec::new();

    // Create scheduled tasks based on workload characteristics
    for (task_name, task_requirements) in &workload.task_types {
        let task_id = format!("task_{}", task_name);

        // Find optimal resource assignment based on load balancing
        let mut best_resource_idx = 0;
        let mut best_load = f64::INFINITY;

        for (i, &load) in load_balancing.load_distribution.iter().enumerate() {
            if load < best_load {
                best_load = load;
                best_resource_idx = i;
            }
        }

        let assigned_resources = vec![format!("resource_{}", best_resource_idx)];

        // Estimate duration based on task requirements and resource capabilities
        let base_duration = task_requirements.qubit_requirement as f64 * 0.1
            + task_requirements.gate_operations.len() as f64 * 0.05;
        let duration = base_duration / (1.0 + best_load);

        scheduled_tasks.push(ScheduledQuantumTask {
            task_id: task_id.clone(),
            assigned_resources: assigned_resources.clone(),
            start_time: 0.0, // Will be optimized by scheduling algorithm
            duration,
            priority: 1.0 / (task_requirements.coherence_requirement + 0.1),
            quantum_requirements: task_requirements.clone(),
        });

        // Create resource reservations
        for resource in &assigned_resources {
            reservations
                .entry(resource.clone())
                .or_insert_with(Vec::new)
                .push(ResourceReservation {
                    resource_id: resource.clone(),
                    start_time: 0.0,
                    duration,
                    task_id: task_id.clone(),
                });
        }

        // Create circuit optimization if quantum operations are involved
        if !task_requirements.gate_operations.is_empty() {
            circuit_optimizations.push(CircuitOptimization {
                original_circuit: format!("circuit_{}", task_name),
                optimized_circuit: format!("optimized_circuit_{}", task_name),
                optimization_technique: "quantum_annealing".to_string(),
                improvement_factor: 1.2 + task_requirements.classical_ratio * 0.3,
            });
        }
    }

    // Create timeline slots
    let total_duration = scheduled_tasks
        .iter()
        .map(|task| task.duration)
        .sum::<f64>();
    let slot_duration = total_duration / 10.0; // 10 time slots

    for i in 0..10 {
        let start_time = i as f64 * slot_duration;
        let mut active_tasks = Vec::new();
        let mut resource_utilization = HashMap::new();

        // Find tasks active in this time slot
        for task in &scheduled_tasks {
            if task.start_time <= start_time && task.start_time + task.duration > start_time {
                active_tasks.push(task.task_id.clone());

                // Update resource utilization
                for resource in &task.assigned_resources {
                    *resource_utilization.entry(resource.clone()).or_insert(0.0) +=
                        task.duration / slot_duration;
                }
            }
        }

        timeline.push(SchedulingTimeSlot {
            start_time,
            duration: slot_duration,
            active_tasks,
            resource_utilization,
        });
    }

    Ok(QuantumTaskSchedule {
        scheduled_tasks,
        timeline,
        reservations,
        circuit_optimizations,
    })
}

/// Updates quantum entanglement graph based on task schedule
#[allow(dead_code)]
fn update_quantum_entanglement_graph(
    graph: &mut ResourceEntanglementGraph,
    schedule: &QuantumTaskSchedule,
    config: &AdvancedConfig,
) -> NdimageResult<()> {
    // Update entanglement strengths based on task interactions
    for task in &schedule.scheduled_tasks {
        if task.assigned_resources.len() >= 2 {
            // Create entanglement between assigned resources
            for i in 0..task.assigned_resources.len() {
                for j in (i + 1)..task.assigned_resources.len() {
                    let resource1 = &task.assigned_resources[i];
                    let resource2 = &task.assigned_resources[j];

                    let entanglement_key = if resource1 < resource2 {
                        (resource1.clone(), resource2.clone())
                    } else {
                        (resource2.clone(), resource1.clone())
                    };

                    let current_strength = graph
                        .entanglement_strengths
                        .get(&entanglement_key)
                        .unwrap_or(&0.0);

                    let new_strength = (*current_strength
                        + task.quantum_requirements.coherence_requirement
                            * config.quantum_coherence_threshold)
                        .min(1.0);

                    graph
                        .entanglement_strengths
                        .insert(entanglement_key, new_strength);
                }
            }
        }
    }

    // Update decoherence tracking
    for (entanglement_pair, strength) in &graph.entanglement_strengths {
        let decoherence_rate = (1.0 - strength) * 0.1;
        graph
            .decoherence_tracking
            .insert(entanglement_pair.clone(), decoherence_rate);
    }

    Ok(())
}

/// Performs quantum performance monitoring
#[allow(dead_code)]
fn quantum_performance_monitoring(
    monitor: &mut QuantumPerformanceMonitor,
    schedule: &QuantumTaskSchedule,
    state: &AdvancedState,
) -> NdimageResult<QuantumMonitoringFeedback> {
    // Calculate performance metrics
    let total_tasks = schedule.scheduled_tasks.len();
    let avg_duration = if total_tasks > 0 {
        schedule
            .scheduled_tasks
            .iter()
            .map(|task| task.duration)
            .sum::<f64>()
            / total_tasks as f64
    } else {
        0.0
    };

    // Calculate quantum speedup based on circuit optimizations
    let quantum_speedup = schedule
        .circuit_optimizations
        .iter()
        .map(|opt| opt.improvement_factor)
        .sum::<f64>()
        / schedule.circuit_optimizations.len().max(1) as f64;

    // Update performance metrics
    monitor.metrics.quantum_speedup = quantum_speedup;
    monitor.metrics.quantum_advantage_ratio = quantum_speedup / avg_duration.max(0.1);
    monitor.metrics.resource_efficiency = 1.0 - avg_duration / 10.0; // Normalized efficiency

    // Detect potential issues
    let mut detected_issues = Vec::new();
    let mut optimization_recommendations = Vec::new();

    if quantum_speedup < 1.1 {
        detected_issues.push("Low quantum speedup detected".to_string());
        optimization_recommendations.push("Consider optimizing quantum circuits".to_string());
    }

    if avg_duration > 5.0 {
        detected_issues.push("High task duration detected".to_string());
        optimization_recommendations.push("Consider parallelizing tasks".to_string());
    }

    let performance_improvement = if monitor.metrics.quantum_speedup > 1.0 {
        monitor.metrics.quantum_speedup
    } else {
        1.0
    };

    Ok(QuantumMonitoringFeedback {
        performance_improvement,
        detected_issues,
        optimization_recommendations,
    })
}

/// Applies quantum optimization feedback for continuous improvement
#[allow(dead_code)]
fn apply_quantum_optimization_feedback(
    scheduler: &mut QuantumAwareResourceScheduler,
    feedback: &QuantumMonitoringFeedback,
    config: &AdvancedConfig,
) -> NdimageResult<()> {
    // Apply performance improvements based on feedback
    if feedback.performance_improvement > 1.2 {
        // Good performance, maintain current settings
        scheduler.performance_monitor.metrics.quantum_speedup = feedback.performance_improvement;
    } else if feedback.performance_improvement < 1.1 {
        // Poor performance, adjust parameters
        for algorithm in &mut scheduler.scheduling_algorithms {
            match algorithm {
                QuantumSchedulingAlgorithm::QuantumAnnealing {
                    ref mut annealing_schedule,
                    ..
                } => {
                    annealing_schedule.cooling_rate *= 0.95; // Slower cooling for better optimization
                    annealing_schedule.steps = (annealing_schedule.steps as f64 * 1.1) as usize;
                }
                QuantumSchedulingAlgorithm::QAOA { ref mut layers, .. } => {
                    *layers = (*layers + 1).min(10); // Add layers but cap at 10
                }
            }
        }
    }

    // Apply optimization recommendations
    for recommendation in &feedback.optimization_recommendations {
        if recommendation.contains("circuits") {
            // Enhance circuit optimization
            scheduler.performance_monitor.metrics.coherence_efficiency *= 1.05;
        } else if recommendation.contains("parallelizing") {
            // Improve load balancing
            for strategy in &mut scheduler.quantum_load_balancer.strategies {
                if let QuantumLoadBalancingStrategy::QuantumSuperposition {
                    ref mut superposition_weights,
                    ..
                } = strategy
                {
                    // Normalize and enhance superposition weights
                    let sum: Complex<f64> = superposition_weights.sum();
                    if sum.norm() > 0.0 {
                        *superposition_weights = superposition_weights.mapv(|x| x / sum.norm());
                    }
                }
            }
        }
    }

    Ok(())
}

/// Calculates quantum coherence preservation level
#[allow(dead_code)]
fn calculate_coherence_preservation(graph: &ResourceEntanglementGraph) -> NdimageResult<f64> {
    if graph.entanglement_strengths.is_empty() {
        return Ok(0.85); // Default coherence level
    }

    // Calculate average entanglement strength
    let total_strength: f64 = graph.entanglement_strengths.values().sum();
    let avg_strength = total_strength / graph.entanglement_strengths.len() as f64;

    // Calculate decoherence penalty
    let total_decoherence: f64 = graph.decoherence_tracking.values().sum();
    let avg_decoherence = if !graph.decoherence_tracking.is_empty() {
        total_decoherence / graph.decoherence_tracking.len() as f64
    } else {
        0.0
    };

    // Coherence preservation is high strength minus decoherence effects
    let coherence_preservation = (avg_strength - avg_decoherence * 0.5).max(0.1).min(1.0);

    Ok(coherence_preservation)
}

/// Workload characteristics for quantum scheduling
#[derive(Debug, Clone)]
pub struct WorkloadCharacteristics {
    /// Task types and their quantum requirements
    pub task_types: HashMap<String, QuantumTaskRequirements>,
    /// Workload intensity over time
    pub intensity_pattern: Vec<f64>,
    /// Data dependencies
    pub dependencies: Vec<(String, String)>,
    /// Performance requirements
    pub performance_requirements: PerformanceRequirements,
}

/// Quantum task requirements
#[derive(Debug, Clone)]
pub struct QuantumTaskRequirements {
    /// Required qubits
    pub qubit_requirement: usize,
    /// Coherence time requirement
    pub coherence_requirement: f64,
    /// Gate operations needed
    pub gate_operations: Vec<String>,
    /// Classical computation ratio
    pub classical_ratio: f64,
}

/// Performance requirements
#[derive(Debug, Clone)]
pub struct PerformanceRequirements {
    /// Maximum acceptable latency
    pub max_latency: f64,
    /// Minimum throughput requirement
    pub min_throughput: f64,
    /// Accuracy requirements
    pub accuracy_requirement: f64,
    /// Energy constraints
    pub energy_budget: f64,
}

/// Resource scheduling decision
#[derive(Debug, Clone)]
pub struct ResourceSchedulingDecision {
    /// Optimal resource allocation
    pub resource_allocation: QuantumResourceAllocation,
    /// Load balancing decisions
    pub load_balancing: QuantumLoadBalancingDecision,
    /// Task scheduling plan
    pub task_schedule: QuantumTaskSchedule,
    /// Expected performance metrics
    pub performancemetrics: QuantumPerformanceMetrics,
    /// Quantum coherence preservation level
    pub quantum_coherence_preservation: f64,
    /// Estimated performance improvement
    pub estimated_performance_improvement: f64,
}

/// Quantum resource allocation
#[derive(Debug, Clone)]
pub struct QuantumResourceAllocation {
    /// Quantum unit allocations
    pub quantum_allocations: HashMap<String, f64>,
    /// Classical unit allocations
    pub classical_allocations: HashMap<String, f64>,
    /// Hybrid unit allocations
    pub hybrid_allocations: HashMap<String, f64>,
    /// Entanglement resource allocations
    pub entanglement_allocations: HashMap<(String, String), f64>,
}

/// Quantum load balancing decision
#[derive(Debug, Clone)]
pub struct QuantumLoadBalancingDecision {
    /// Load distribution across resources
    pub load_distribution: Array1<f64>,
    /// Quantum superposition coefficients
    pub superposition_coefficients: Array1<Complex<f64>>,
    /// Entanglement-based sharing decisions
    pub entanglement_sharing: HashMap<String, Vec<String>>,
    /// Load migration recommendations
    pub migration_recommendations: Vec<LoadMigrationRecommendation>,
}

/// Load migration recommendation
#[derive(Debug, Clone)]
pub struct LoadMigrationRecommendation {
    /// Source resource
    pub from_resource: String,
    /// Target resource
    pub to_resource: String,
    /// Load amount to migrate
    pub load_amount: f64,
    /// Migration priority
    pub priority: f64,
    /// Estimated benefit
    pub estimated_benefit: f64,
}

/// Quantum task schedule
#[derive(Debug, Clone)]
pub struct QuantumTaskSchedule {
    /// Scheduled tasks
    pub scheduled_tasks: Vec<ScheduledQuantumTask>,
    /// Scheduling timeline
    pub timeline: Vec<SchedulingTimeSlot>,
    /// Resource reservations
    pub reservations: HashMap<String, Vec<ResourceReservation>>,
    /// Quantum circuit optimizations
    pub circuit_optimizations: Vec<CircuitOptimization>,
}

/// Scheduled quantum task
#[derive(Debug, Clone)]
pub struct ScheduledQuantumTask {
    /// Task identifier
    pub task_id: String,
    /// Assigned resources
    pub assigned_resources: Vec<String>,
    /// Start time
    pub start_time: f64,
    /// Estimated duration
    pub duration: f64,
    /// Priority level
    pub priority: f64,
    /// Quantum requirements
    pub quantum_requirements: QuantumTaskRequirements,
}

/// Scheduling time slot
#[derive(Debug, Clone)]
pub struct SchedulingTimeSlot {
    /// Time slot start
    pub start_time: f64,
    /// Time slot duration
    pub duration: f64,
    /// Active tasks in slot
    pub active_tasks: Vec<String>,
    /// Resource utilization
    pub resource_utilization: HashMap<String, f64>,
}

/// Resource reservation
#[derive(Debug, Clone)]
pub struct ResourceReservation {
    /// Reserved resource
    pub resource_id: String,
    /// Reservation start time
    pub start_time: f64,
    /// Reservation duration
    pub duration: f64,
    /// Reserving task
    pub task_id: String,
}

/// Circuit optimization
#[derive(Debug, Clone)]
pub struct CircuitOptimization {
    /// Original circuit description
    pub original_circuit: String,
    /// Optimized circuit description
    pub optimized_circuit: String,
    /// Optimization technique used
    pub optimization_technique: String,
    /// Performance improvement
    pub improvement_factor: f64,
}

/// Quantum monitoring feedback
#[derive(Debug, Clone)]
pub struct QuantumMonitoringFeedback {
    /// Performance improvement ratio
    pub performance_improvement: f64,
    /// Issues detected
    pub detected_issues: Vec<String>,
    /// Optimization recommendations
    pub optimization_recommendations: Vec<String>,
}