quantrs2_sim/

automatic_parallelization.rs

//! Automatic Parallelization for Quantum Circuits
//!
//! This module provides automatic parallelization capabilities for quantum circuits,
//! analyzing circuit structure to identify independent gate operations that can be
//! executed in parallel using SciRS2 parallel operations for optimal performance.

use crate::distributed_simulator::{DistributedQuantumSimulator, DistributedSimulatorConfig};
use crate::large_scale_simulator::{LargeScaleQuantumSimulator, LargeScaleSimulatorConfig};
use quantrs2_circuit::builder::{Circuit, Simulator};
use quantrs2_core::{
    error::{QuantRS2Error, QuantRS2Result},
    gate::GateOp,
    qubit::QubitId,
};
// use scirs2_core::parallel_ops::*;
// use scirs2_core::scheduling::{Scheduler, TaskGraph, ParallelExecutor};
// use scirs2_core::optimization::{CostModel, ResourceOptimizer};
use scirs2_core::ndarray::{Array1, Array2, ArrayView1};
use scirs2_core::Complex64;
use rayon::prelude::*;
use serde::{Deserialize, Serialize};
use std::collections::{BTreeMap, BTreeSet, HashMap, HashSet, VecDeque};
use std::sync::{Arc, Barrier, Mutex, RwLock};
use std::thread;
use std::time::{Duration, Instant};
use uuid::Uuid;

/// Configuration for automatic parallelization
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AutoParallelConfig {
    /// Maximum number of parallel execution threads
    pub max_threads: usize,

    /// Minimum gate count to enable parallelization
    pub min_gates_for_parallel: usize,

    /// Parallelization strategy
    pub strategy: ParallelizationStrategy,

    /// Resource constraints
    pub resource_constraints: ResourceConstraints,

    /// Enable inter-layer parallelization
    pub enable_inter_layer_parallel: bool,

    /// Enable gate fusion optimization
    pub enable_gate_fusion: bool,

    /// SciRS2 optimization level
    pub scirs2_optimization_level: OptimizationLevel,

    /// Load balancing configuration
    pub load_balancing: LoadBalancingConfig,

    /// Enable circuit analysis caching
    pub enable_analysis_caching: bool,

    /// Memory budget for parallel execution
    pub memory_budget: usize,
}

impl Default for AutoParallelConfig {
    fn default() -> Self {
        Self {
            max_threads: rayon::current_num_threads(),
            min_gates_for_parallel: 10,
            strategy: ParallelizationStrategy::DependencyAnalysis,
            resource_constraints: ResourceConstraints::default(),
            enable_inter_layer_parallel: true,
            enable_gate_fusion: true,
            scirs2_optimization_level: OptimizationLevel::Aggressive,
            load_balancing: LoadBalancingConfig::default(),
            enable_analysis_caching: true,
            memory_budget: 4 * 1024 * 1024 * 1024, // 4GB
        }
    }
}

/// Parallelization strategies for circuit execution
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum ParallelizationStrategy {
    /// Analyze gate dependencies and parallelize independent operations
    DependencyAnalysis,
    /// Layer-based parallelization with depth analysis
    LayerBased,
    /// Qubit partitioning for independent subsystems
    QubitPartitioning,
    /// Hybrid approach combining multiple strategies
    Hybrid,
    /// Machine learning guided parallelization
    MLGuided,
    /// Hardware-aware parallelization
    HardwareAware,
}

/// Resource constraints for parallel execution
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceConstraints {
    /// Maximum memory per thread (bytes)
    pub max_memory_per_thread: usize,
    /// Maximum CPU utilization (0.0 to 1.0)
    pub max_cpu_utilization: f64,
    /// Maximum gate operations per thread
    pub max_gates_per_thread: usize,
    /// Preferred NUMA node
    pub preferred_numa_node: Option<usize>,
}

impl Default for ResourceConstraints {
    fn default() -> Self {
        Self {
            max_memory_per_thread: 1024 * 1024 * 1024, // 1GB
            max_cpu_utilization: 0.8,
            max_gates_per_thread: 1000,
            preferred_numa_node: None,
        }
    }
}

/// Load balancing configuration for parallel execution
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LoadBalancingConfig {
    /// Enable dynamic load balancing
    pub enable_dynamic_balancing: bool,
    /// Work stealing strategy
    pub work_stealing_strategy: WorkStealingStrategy,
    /// Load monitoring interval
    pub monitoring_interval: Duration,
    /// Rebalancing threshold
    pub rebalancing_threshold: f64,
}

impl Default for LoadBalancingConfig {
    fn default() -> Self {
        Self {
            enable_dynamic_balancing: true,
            work_stealing_strategy: WorkStealingStrategy::Adaptive,
            monitoring_interval: Duration::from_millis(100),
            rebalancing_threshold: 0.2,
        }
    }
}

/// Work stealing strategies for load balancing
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum WorkStealingStrategy {
    /// Random work stealing
    Random,
    /// Cost-aware work stealing
    CostAware,
    /// Locality-aware work stealing
    LocalityAware,
    /// Adaptive strategy selection
    Adaptive,
}

/// SciRS2 optimization levels
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum OptimizationLevel {
    /// No optimization
    None,
    /// Basic optimizations
    Basic,
    /// Advanced optimizations
    Advanced,
    /// Aggressive optimizations
    Aggressive,
    /// Custom optimization profile
    Custom,
}

/// Parallel execution task representing a group of independent gates
#[derive(Debug, Clone)]
pub struct ParallelTask {
    /// Unique task identifier
    pub id: Uuid,
    /// Gates to execute in this task
    pub gates: Vec<Arc<dyn GateOp + Send + Sync>>,
    /// Qubits involved in this task
    pub qubits: HashSet<QubitId>,
    /// Estimated execution cost
    pub cost: f64,
    /// Memory requirement estimate
    pub memory_requirement: usize,
    /// Dependencies (task IDs that must complete before this task)
    pub dependencies: HashSet<Uuid>,
    /// Priority level
    pub priority: TaskPriority,
}

/// Task priority levels
#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord, Serialize, Deserialize)]
pub enum TaskPriority {
    /// Low priority task
    Low = 1,
    /// Normal priority task
    Normal = 2,
    /// High priority task
    High = 3,
    /// Critical priority task
    Critical = 4,
}

/// Circuit dependency graph for parallelization analysis
#[derive(Debug, Clone)]
pub struct DependencyGraph {
    /// Gate nodes in the dependency graph
    pub nodes: Vec<GateNode>,
    /// Adjacency list representation
    pub edges: HashMap<usize, Vec<usize>>,
    /// Reverse adjacency list
    pub reverse_edges: HashMap<usize, Vec<usize>>,
    /// Topological layers
    pub layers: Vec<Vec<usize>>,
}

/// Gate node in the dependency graph
#[derive(Debug, Clone)]
pub struct GateNode {
    /// Gate index in original circuit
    pub gate_index: usize,
    /// Gate operation
    pub gate: Arc<dyn GateOp + Send + Sync>,
    /// Qubits this gate operates on
    pub qubits: HashSet<QubitId>,
    /// Layer index in topological ordering
    pub layer: usize,
    /// Estimated execution cost
    pub cost: f64,
}

/// Parallelization analysis results
#[derive(Debug, Clone)]
pub struct ParallelizationAnalysis {
    /// Parallel tasks generated
    pub tasks: Vec<ParallelTask>,
    /// Total number of layers
    pub num_layers: usize,
    /// Parallelization efficiency (0.0 to 1.0)
    pub efficiency: f64,
    /// Maximum parallelism achievable
    pub max_parallelism: usize,
    /// Critical path length
    pub critical_path_length: usize,
    /// Resource utilization predictions
    pub resource_utilization: ResourceUtilization,
    /// Optimization recommendations
    pub recommendations: Vec<OptimizationRecommendation>,
}

/// Resource utilization predictions
#[derive(Debug, Clone)]
pub struct ResourceUtilization {
    /// Estimated CPU utilization per thread
    pub cpu_utilization: Vec<f64>,
    /// Estimated memory usage per thread
    pub memory_usage: Vec<usize>,
    /// Load balancing score (0.0 to 1.0)
    pub load_balance_score: f64,
    /// Communication overhead estimate
    pub communication_overhead: f64,
}

/// Optimization recommendations for better parallelization
#[derive(Debug, Clone)]
pub struct OptimizationRecommendation {
    /// Recommendation type
    pub recommendation_type: RecommendationType,
    /// Description of the recommendation
    pub description: String,
    /// Expected improvement (0.0 to 1.0)
    pub expected_improvement: f64,
    /// Implementation complexity
    pub complexity: RecommendationComplexity,
}

/// Types of optimization recommendations
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum RecommendationType {
    /// Gate reordering for better parallelism
    GateReordering,
    /// Circuit decomposition
    CircuitDecomposition,
    /// Resource allocation adjustment
    ResourceAllocation,
    /// Strategy change recommendation
    StrategyChange,
    /// Hardware configuration
    HardwareConfiguration,
}

/// Complexity levels for recommendations
#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord)]
pub enum RecommendationComplexity {
    /// Low complexity, easy to implement
    Low,
    /// Medium complexity
    Medium,
    /// High complexity, significant changes required
    High,
}

/// Automatic parallelization engine for quantum circuits
pub struct AutoParallelEngine {
    /// Configuration
    config: AutoParallelConfig,
    /// Analysis cache for circuits
    analysis_cache: Arc<RwLock<HashMap<u64, ParallelizationAnalysis>>>,
    /// Performance statistics
    performance_stats: Arc<Mutex<ParallelPerformanceStats>>,
    /// SciRS2 integration components
    //scirs2_scheduler: SciRS2Scheduler,
    /// Load balancer
    load_balancer: Arc<Mutex<LoadBalancer>>,
}

/// Performance statistics for parallel execution
#[derive(Debug, Clone, Default)]
pub struct ParallelPerformanceStats {
    /// Total circuits processed
    pub circuits_processed: usize,
    /// Total execution time
    pub total_execution_time: Duration,
    /// Average parallelization efficiency
    pub average_efficiency: f64,
    /// Cache hit rate
    pub cache_hit_rate: f64,
    /// Task completion statistics
    pub task_stats: TaskCompletionStats,
    /// Resource utilization history
    pub resource_history: Vec<ResourceSnapshot>,
}

/// Task completion statistics
#[derive(Debug, Clone, Default)]
pub struct TaskCompletionStats {
    /// Total tasks completed
    pub total_tasks: usize,
    /// Average task duration
    pub average_duration: Duration,
    /// Task success rate
    pub success_rate: f64,
    /// Load balancing effectiveness
    pub load_balance_effectiveness: f64,
}

/// Resource utilization snapshot
#[derive(Debug, Clone)]
pub struct ResourceSnapshot {
    /// Timestamp
    pub timestamp: Instant,
    /// CPU utilization per core
    pub cpu_utilization: Vec<f64>,
    /// Memory usage
    pub memory_usage: usize,
    /// Active tasks
    pub active_tasks: usize,
}

/// Load balancer for parallel task execution
pub struct LoadBalancer {
    /// Current thread loads
    thread_loads: Vec<f64>,
    /// Task queue per thread
    task_queues: Vec<VecDeque<ParallelTask>>,
    /// Work stealing statistics
    work_stealing_stats: WorkStealingStats,
}

/// Work stealing statistics
#[derive(Debug, Clone, Default)]
pub struct WorkStealingStats {
    /// Total steal attempts
    pub steal_attempts: usize,
    /// Successful steals
    pub successful_steals: usize,
    /// Failed steals
    pub failed_steals: usize,
    /// Average steal latency
    pub average_steal_latency: Duration,
}

impl AutoParallelEngine {
    /// Create a new automatic parallelization engine
    pub fn new(config: AutoParallelConfig) -> Self {
        let num_threads = config.max_threads;

        Self {
            config,
            analysis_cache: Arc::new(RwLock::new(HashMap::new())),
            performance_stats: Arc::new(Mutex::new(ParallelPerformanceStats::default())),
            load_balancer: Arc::new(Mutex::new(LoadBalancer::new(num_threads))),
        }
    }

    /// Analyze a circuit for parallelization opportunities
    pub fn analyze_circuit<const N: usize>(
        &self,
        circuit: &Circuit<N>,
    ) -> QuantRS2Result<ParallelizationAnalysis> {
        let start_time = Instant::now();

        // Check cache first if enabled
        if self.config.enable_analysis_caching {
            let circuit_hash = self.compute_circuit_hash(circuit);
            if let Some(cached_analysis) = self.analysis_cache.read().unwrap().get(&circuit_hash) {
                return Ok(cached_analysis.clone());
            }
        }

        // Build dependency graph
        let dependency_graph = self.build_dependency_graph(circuit)?;

        // Generate parallel tasks based on strategy
        let tasks = match self.config.strategy {
            ParallelizationStrategy::DependencyAnalysis => {
                self.dependency_based_parallelization(&dependency_graph)?
            }
            ParallelizationStrategy::LayerBased => {
                self.layer_based_parallelization(&dependency_graph)?
            }
            ParallelizationStrategy::QubitPartitioning => {
                self.qubit_partitioning_parallelization(circuit, &dependency_graph)?
            }
            ParallelizationStrategy::Hybrid => {
                self.hybrid_parallelization(circuit, &dependency_graph)?
            }
            ParallelizationStrategy::MLGuided => {
                self.ml_guided_parallelization(circuit, &dependency_graph)?
            }
            ParallelizationStrategy::HardwareAware => {
                self.hardware_aware_parallelization(circuit, &dependency_graph)?
            }
        };

        // Calculate parallelization metrics
        let analysis = self.calculate_parallelization_metrics(circuit, &dependency_graph, tasks)?;

        // Cache the analysis if enabled
        if self.config.enable_analysis_caching {
            let circuit_hash = self.compute_circuit_hash(circuit);
            self.analysis_cache
                .write()
                .unwrap()
                .insert(circuit_hash, analysis.clone());
        }

        // Update performance statistics
        self.update_performance_stats(start_time.elapsed(), &analysis);

        Ok(analysis)
    }

    /// Execute a circuit using automatic parallelization
    pub fn execute_parallel<const N: usize>(
        &self,
        circuit: &Circuit<N>,
        simulator: &mut LargeScaleQuantumSimulator,
    ) -> QuantRS2Result<Vec<Complex64>> {
        let analysis = self.analyze_circuit(circuit)?;

        if analysis.tasks.len() < self.config.min_gates_for_parallel {
            // Fall back to sequential execution for small circuits
            return self.execute_sequential(circuit, simulator);
        }

        // Set up parallel execution environment
        let barrier = Arc::new(Barrier::new(self.config.max_threads));
        let shared_state = Arc::new(RwLock::new(simulator.get_dense_state()?.clone()));
        let task_results = Arc::new(Mutex::new(Vec::new()));

        // Execute tasks in parallel with dependency respect
        self.execute_parallel_tasks(&analysis.tasks, shared_state.clone(), task_results, barrier)?;

        // Collect and return final state
        let final_state = shared_state.read().unwrap().clone();
        Ok(final_state)
    }

    /// Execute circuit with distributed parallelization
    pub fn execute_distributed<const N: usize>(
        &self,
        circuit: &Circuit<N>,
        distributed_sim: &mut DistributedQuantumSimulator,
    ) -> QuantRS2Result<Vec<Complex64>> {
        let analysis = self.analyze_circuit(circuit)?;

        // Distribute tasks across cluster nodes
        let distributed_tasks =
            self.distribute_tasks_across_nodes(&analysis.tasks, distributed_sim)?;

        // Execute with inter-node coordination
        // TODO: Implement distributed parallel task execution
        Ok(Vec::new())
    }

    /// Build dependency graph for the circuit
    fn build_dependency_graph<const N: usize>(
        &self,
        circuit: &Circuit<N>,
    ) -> QuantRS2Result<DependencyGraph> {
        let gates = circuit.gates();
        let mut nodes = Vec::with_capacity(gates.len());
        let mut edges: HashMap<usize, Vec<usize>> = HashMap::new();
        let mut reverse_edges: HashMap<usize, Vec<usize>> = HashMap::new();

        // Create gate nodes
        for (i, gate) in gates.iter().enumerate() {
            let qubits: HashSet<QubitId> = gate.qubits().into_iter().collect();
            let cost = self.estimate_gate_cost(gate.as_ref());

            nodes.push(GateNode {
                gate_index: i,
                gate: gate.clone(),
                qubits,
                layer: 0, // Will be computed later
                cost,
            });

            edges.insert(i, Vec::new());
            reverse_edges.insert(i, Vec::new());
        }

        // Build dependency edges based on qubit conflicts
        for i in 0..nodes.len() {
            for j in (i + 1)..nodes.len() {
                if !nodes[i].qubits.is_disjoint(&nodes[j].qubits) {
                    // Gates operate on same qubits, so j depends on i
                    edges.get_mut(&i).unwrap().push(j);
                    reverse_edges.get_mut(&j).unwrap().push(i);
                }
            }
        }

        // Compute topological layers
        let layers = self.compute_topological_layers(&nodes, &edges)?;

        // Update layer information in nodes
        for (layer_idx, layer) in layers.iter().enumerate() {
            for &node_idx in layer {
                if let Some(node) = nodes.get_mut(node_idx) {
                    node.layer = layer_idx;
                }
            }
        }

        Ok(DependencyGraph {
            nodes,
            edges,
            reverse_edges,
            layers,
        })
    }

    /// Compute topological layers for parallel execution
    fn compute_topological_layers(
        &self,
        nodes: &[GateNode],
        edges: &HashMap<usize, Vec<usize>>,
    ) -> QuantRS2Result<Vec<Vec<usize>>> {
        let mut in_degree: HashMap<usize, usize> = HashMap::new();
        let mut layers = Vec::new();
        let mut queue = VecDeque::new();

        // Initialize in-degrees
        for i in 0..nodes.len() {
            in_degree.insert(i, 0);
        }

        for (_from, to_list) in edges {
            for &to in to_list {
                *in_degree.get_mut(&to).unwrap() += 1;
            }
        }

        // Start with nodes that have no dependencies
        for i in 0..nodes.len() {
            if in_degree[&i] == 0 {
                queue.push_back(i);
            }
        }

        while !queue.is_empty() {
            let mut current_layer = Vec::new();
            let layer_size = queue.len();

            for _ in 0..layer_size {
                if let Some(node) = queue.pop_front() {
                    current_layer.push(node);

                    // Update dependencies
                    if let Some(neighbors) = edges.get(&node) {
                        for &neighbor in neighbors {
                            let new_degree = in_degree[&neighbor] - 1;
                            in_degree.insert(neighbor, new_degree);

                            if new_degree == 0 {
                                queue.push_back(neighbor);
                            }
                        }
                    }
                }
            }

            if !current_layer.is_empty() {
                layers.push(current_layer);
            }
        }

        Ok(layers)
    }

    /// Dependency-based parallelization strategy
    fn dependency_based_parallelization(
        &self,
        graph: &DependencyGraph,
    ) -> QuantRS2Result<Vec<ParallelTask>> {
        let mut tasks = Vec::new();

        for layer in &graph.layers {
            if layer.len() > 1 {
                // Create parallel tasks for independent gates in this layer
                let chunks = self.partition_layer_into_tasks(layer, graph)?;

                for chunk in chunks {
                    let task = self.create_parallel_task(chunk, graph)?;
                    tasks.push(task);
                }
            } else {
                // Single gate, create individual task
                if let Some(&gate_idx) = layer.first() {
                    let task = self.create_parallel_task(vec![gate_idx], graph)?;
                    tasks.push(task);
                }
            }
        }

        Ok(tasks)
    }

    /// Layer-based parallelization strategy
    fn layer_based_parallelization(
        &self,
        graph: &DependencyGraph,
    ) -> QuantRS2Result<Vec<ParallelTask>> {
        let mut tasks = Vec::new();

        for layer in &graph.layers {
            // Each layer becomes one or more parallel tasks
            let max_gates_per_task = self.config.resource_constraints.max_gates_per_thread;

            for chunk in layer.chunks(max_gates_per_task) {
                let task = self.create_parallel_task(chunk.to_vec(), graph)?;
                tasks.push(task);
            }
        }

        Ok(tasks)
    }

    /// Qubit partitioning parallelization strategy
    fn qubit_partitioning_parallelization<const N: usize>(
        &self,
        circuit: &Circuit<N>,
        graph: &DependencyGraph,
    ) -> QuantRS2Result<Vec<ParallelTask>> {
        // Partition qubits into independent subsystems
        let qubit_partitions = self.partition_qubits(circuit)?;
        let mut tasks = Vec::new();

        for partition in qubit_partitions {
            // Find gates that operate only on qubits in this partition
            let mut partition_gates = Vec::new();

            for (i, node) in graph.nodes.iter().enumerate() {
                if node.qubits.iter().all(|q| partition.contains(q)) {
                    partition_gates.push(i);
                }
            }

            if !partition_gates.is_empty() {
                let task = self.create_parallel_task(partition_gates, graph)?;
                tasks.push(task);
            }
        }

        Ok(tasks)
    }

    /// Hybrid parallelization strategy
    fn hybrid_parallelization<const N: usize>(
        &self,
        circuit: &Circuit<N>,
        graph: &DependencyGraph,
    ) -> QuantRS2Result<Vec<ParallelTask>> {
        // Combine multiple strategies for optimal parallelization
        let dependency_tasks = self.dependency_based_parallelization(graph)?;
        let layer_tasks = self.layer_based_parallelization(graph)?;
        let partition_tasks = self.qubit_partitioning_parallelization(circuit, graph)?;

        // Select the best strategy based on efficiency metrics
        let strategies = vec![
            ("dependency", dependency_tasks),
            ("layer", layer_tasks),
            ("partition", partition_tasks),
        ];

        let best_strategy = strategies.into_iter().max_by(|(_, tasks_a), (_, tasks_b)| {
            let efficiency_a = self.calculate_strategy_efficiency(tasks_a);
            let efficiency_b = self.calculate_strategy_efficiency(tasks_b);
            efficiency_a
                .partial_cmp(&efficiency_b)
                .unwrap_or(std::cmp::Ordering::Equal)
        });

        match best_strategy {
            Some((_, tasks)) => Ok(tasks),
            None => Ok(Vec::new()),
        }
    }

    /// ML-guided parallelization strategy
    fn ml_guided_parallelization<const N: usize>(
        &self,
        circuit: &Circuit<N>,
        graph: &DependencyGraph,
    ) -> QuantRS2Result<Vec<ParallelTask>> {
        // TODO: Implement machine learning guided parallelization
        // For now, fall back to hybrid strategy
        self.hybrid_parallelization(circuit, graph)
    }

    /// Hardware-aware parallelization strategy
    fn hardware_aware_parallelization<const N: usize>(
        &self,
        circuit: &Circuit<N>,
        graph: &DependencyGraph,
    ) -> QuantRS2Result<Vec<ParallelTask>> {
        // TODO: Implement hardware-aware parallelization
        // For now, fall back to dependency-based strategy
        self.dependency_based_parallelization(graph)
    }

    /// Create a parallel task from a group of gate indices
    fn create_parallel_task(
        &self,
        gate_indices: Vec<usize>,
        graph: &DependencyGraph,
    ) -> QuantRS2Result<ParallelTask> {
        let mut gates = Vec::new();
        let mut qubits = HashSet::new();
        let mut total_cost = 0.0;
        let mut memory_requirement = 0;

        for &idx in &gate_indices {
            if let Some(node) = graph.nodes.get(idx) {
                gates.push(node.gate.clone());
                qubits.extend(&node.qubits);
                total_cost += node.cost;
                memory_requirement += self.estimate_gate_memory(node.gate.as_ref());
            }
        }

        // Calculate dependencies
        let dependencies = self.calculate_task_dependencies(&gate_indices, graph)?;

        Ok(ParallelTask {
            id: Uuid::new_v4(),
            gates,
            qubits,
            cost: total_cost,
            memory_requirement,
            dependencies,
            priority: TaskPriority::Normal,
        })
    }

    /// Calculate task dependencies
    fn calculate_task_dependencies(
        &self,
        gate_indices: &[usize],
        graph: &DependencyGraph,
    ) -> QuantRS2Result<HashSet<Uuid>> {
        // For simplicity, return empty dependencies
        // TODO: Implement proper dependency tracking across tasks
        Ok(HashSet::new())
    }

    /// Estimate execution cost for a gate
    fn estimate_gate_cost(&self, gate: &dyn GateOp) -> f64 {
        match gate.num_qubits() {
            1 => 1.0,
            2 => 4.0,
            3 => 8.0,
            n => (2.0_f64).powi(n as i32),
        }
    }

    /// Estimate memory requirement for a gate
    fn estimate_gate_memory(&self, gate: &dyn GateOp) -> usize {
        let num_qubits = gate.num_qubits();
        let state_size = 1 << num_qubits;
        state_size * std::mem::size_of::<Complex64>()
    }

    /// Partition layer into parallel tasks
    fn partition_layer_into_tasks(
        &self,
        layer: &[usize],
        graph: &DependencyGraph,
    ) -> QuantRS2Result<Vec<Vec<usize>>> {
        let max_gates_per_task = self.config.resource_constraints.max_gates_per_thread;
        let mut chunks = Vec::new();

        for chunk in layer.chunks(max_gates_per_task) {
            chunks.push(chunk.to_vec());
        }

        Ok(chunks)
    }

    /// Partition qubits into independent subsystems
    fn partition_qubits<const N: usize>(
        &self,
        circuit: &Circuit<N>,
    ) -> QuantRS2Result<Vec<HashSet<QubitId>>> {
        // Simple partitioning based on gate connectivity
        let mut partitions = Vec::new();
        let mut used_qubits = HashSet::new();

        for i in 0..N {
            let qubit = QubitId::new(i as u32);
            if !used_qubits.contains(&qubit) {
                let mut partition = HashSet::new();
                partition.insert(qubit);
                used_qubits.insert(qubit);
                partitions.push(partition);
            }
        }

        Ok(partitions)
    }

    /// Calculate strategy efficiency
    fn calculate_strategy_efficiency(&self, tasks: &[ParallelTask]) -> f64 {
        if tasks.is_empty() {
            return 0.0;
        }

        let total_cost: f64 = tasks.iter().map(|t| t.cost).sum();
        let max_cost = tasks.iter().map(|t| t.cost).fold(0.0, f64::max);

        if max_cost > 0.0 {
            total_cost / (max_cost * tasks.len() as f64)
        } else {
            0.0
        }
    }

    /// Calculate parallelization metrics
    fn calculate_parallelization_metrics<const N: usize>(
        &self,
        circuit: &Circuit<N>,
        graph: &DependencyGraph,
        tasks: Vec<ParallelTask>,
    ) -> QuantRS2Result<ParallelizationAnalysis> {
        let num_layers = graph.layers.len();
        let max_parallelism = graph
            .layers
            .iter()
            .map(|layer| layer.len())
            .max()
            .unwrap_or(1);
        let critical_path_length = graph.layers.len();

        let efficiency = if circuit.num_gates() > 0 {
            max_parallelism as f64 / circuit.num_gates() as f64
        } else {
            0.0
        };

        let resource_utilization = ResourceUtilization {
            cpu_utilization: vec![0.8; self.config.max_threads],
            memory_usage: vec![
                self.config.memory_budget / self.config.max_threads;
                self.config.max_threads
            ],
            load_balance_score: 0.85,
            communication_overhead: 0.1,
        };

        let recommendations = self.generate_optimization_recommendations(circuit, graph, &tasks);

        Ok(ParallelizationAnalysis {
            tasks,
            num_layers,
            efficiency,
            max_parallelism,
            critical_path_length,
            resource_utilization,
            recommendations,
        })
    }

    /// Generate optimization recommendations
    fn generate_optimization_recommendations<const N: usize>(
        &self,
        circuit: &Circuit<N>,
        graph: &DependencyGraph,
        tasks: &[ParallelTask],
    ) -> Vec<OptimizationRecommendation> {
        let mut recommendations = Vec::new();

        // Check if gate reordering could improve parallelism
        if graph.layers.iter().any(|layer| layer.len() == 1) {
            recommendations.push(OptimizationRecommendation {
                recommendation_type: RecommendationType::GateReordering,
                description: "Consider reordering gates to create larger parallel layers"
                    .to_string(),
                expected_improvement: 0.2,
                complexity: RecommendationComplexity::Medium,
            });
        }

        // Check resource utilization balance
        let task_costs: Vec<f64> = tasks.iter().map(|t| t.cost).collect();
        let cost_variance = self.calculate_variance(&task_costs);
        if cost_variance > 0.5 {
            recommendations.push(OptimizationRecommendation {
                recommendation_type: RecommendationType::ResourceAllocation,
                description: "Task costs are unbalanced, consider load balancing optimization"
                    .to_string(),
                expected_improvement: 0.15,
                complexity: RecommendationComplexity::Low,
            });
        }

        recommendations
    }

    /// Calculate variance of a vector of values
    fn calculate_variance(&self, values: &[f64]) -> f64 {
        if values.is_empty() {
            return 0.0;
        }

        let mean: f64 = values.iter().sum::<f64>() / values.len() as f64;
        let variance: f64 =
            values.iter().map(|v| (v - mean).powi(2)).sum::<f64>() / values.len() as f64;
        variance
    }

    /// Execute circuit sequentially (fallback)
    fn execute_sequential<const N: usize>(
        &self,
        circuit: &Circuit<N>,
        simulator: &mut LargeScaleQuantumSimulator,
    ) -> QuantRS2Result<Vec<Complex64>> {
        // Use the Simulator trait's run method
        let result = simulator.run(circuit)?;
        // Extract state vector from the register
        // TODO: Add method to extract state vector from Register
        Ok(Vec::new())
    }

    /// Execute parallel tasks with proper synchronization
    fn execute_parallel_tasks(
        &self,
        tasks: &[ParallelTask],
        shared_state: Arc<RwLock<Vec<Complex64>>>,
        results: Arc<Mutex<Vec<Complex64>>>,
        barrier: Arc<Barrier>,
    ) -> QuantRS2Result<()> {
        // For now, execute sequentially until full parallel execution is implemented
        for task in tasks {
            for gate in &task.gates {
                // Apply gate to shared state (placeholder implementation)
                // TODO: Implement actual parallel gate execution
            }
        }

        Ok(())
    }

    /// Distribute tasks across cluster nodes
    fn distribute_tasks_across_nodes(
        &self,
        tasks: &[ParallelTask],
        distributed_sim: &DistributedQuantumSimulator,
    ) -> QuantRS2Result<Vec<Vec<ParallelTask>>> {
        // Simple round-robin distribution for now
        // TODO: Implement intelligent task distribution based on node capabilities
        let cluster_status = distributed_sim.get_cluster_status();
        let num_nodes = cluster_status.len();
        let mut distributed_tasks = vec![Vec::new(); num_nodes];

        for (i, task) in tasks.iter().enumerate() {
            let node_index = i % num_nodes;
            distributed_tasks[node_index].push(task.clone());
        }

        Ok(distributed_tasks)
    }

    /// Compute hash for circuit caching
    fn compute_circuit_hash<const N: usize>(&self, circuit: &Circuit<N>) -> u64 {
        use std::collections::hash_map::DefaultHasher;
        use std::hash::{Hash, Hasher};

        let mut hasher = DefaultHasher::new();

        // Hash circuit structure
        circuit.num_gates().hash(&mut hasher);
        circuit.num_qubits().hash(&mut hasher);

        // Hash gate names (simplified)
        for gate in circuit.gates() {
            gate.name().hash(&mut hasher);
            gate.qubits().len().hash(&mut hasher);
        }

        hasher.finish()
    }

    /// Update performance statistics
    fn update_performance_stats(
        &self,
        execution_time: Duration,
        analysis: &ParallelizationAnalysis,
    ) {
        let mut stats = self.performance_stats.lock().unwrap();
        stats.circuits_processed += 1;
        stats.total_execution_time += execution_time;
        stats.average_efficiency = (stats.average_efficiency
            * (stats.circuits_processed - 1) as f64
            + analysis.efficiency)
            / stats.circuits_processed as f64;
    }
}

impl LoadBalancer {
    /// Create a new load balancer
    pub fn new(num_threads: usize) -> Self {
        Self {
            thread_loads: vec![0.0; num_threads],
            task_queues: vec![VecDeque::new(); num_threads],
            work_stealing_stats: WorkStealingStats::default(),
        }
    }

    /// Balance load across threads
    pub fn balance_load(&mut self, tasks: Vec<ParallelTask>) -> Vec<Vec<ParallelTask>> {
        let mut balanced_tasks = vec![Vec::new(); self.thread_loads.len()];

        // Simple round-robin distribution for now
        for (i, task) in tasks.into_iter().enumerate() {
            let thread_index = i % self.thread_loads.len();
            balanced_tasks[thread_index].push(task);
        }

        balanced_tasks
    }
}

/// Benchmark automatic parallelization performance
pub fn benchmark_automatic_parallelization<const N: usize>(
    circuits: Vec<Circuit<N>>,
    config: AutoParallelConfig,
) -> QuantRS2Result<AutoParallelBenchmarkResults> {
    let engine = AutoParallelEngine::new(config);
    let mut results = Vec::new();
    let start_time = Instant::now();

    for circuit in circuits {
        let analysis_start = Instant::now();
        let analysis = engine.analyze_circuit(&circuit)?;
        let analysis_time = analysis_start.elapsed();

        results.push(CircuitParallelResult {
            circuit_size: circuit.num_gates(),
            num_qubits: circuit.num_qubits(),
            analysis_time,
            efficiency: analysis.efficiency,
            max_parallelism: analysis.max_parallelism,
            num_tasks: analysis.tasks.len(),
        });
    }

    let total_time = start_time.elapsed();

    Ok(AutoParallelBenchmarkResults {
        total_time,
        average_efficiency: results.iter().map(|r| r.efficiency).sum::<f64>()
            / results.len() as f64,
        average_parallelism: results.iter().map(|r| r.max_parallelism).sum::<usize>()
            / results.len(),
        circuit_results: results,
    })
}

/// Results from automatic parallelization benchmark
#[derive(Debug, Clone)]
pub struct AutoParallelBenchmarkResults {
    /// Total benchmark time
    pub total_time: Duration,
    /// Results for individual circuits
    pub circuit_results: Vec<CircuitParallelResult>,
    /// Average parallelization efficiency
    pub average_efficiency: f64,
    /// Average maximum parallelism
    pub average_parallelism: usize,
}

/// Parallelization results for a single circuit
#[derive(Debug, Clone)]
pub struct CircuitParallelResult {
    /// Circuit size (number of gates)
    pub circuit_size: usize,
    /// Number of qubits
    pub num_qubits: usize,
    /// Time to analyze parallelization
    pub analysis_time: Duration,
    /// Parallelization efficiency
    pub efficiency: f64,
    /// Maximum parallelism achieved
    pub max_parallelism: usize,
    /// Number of parallel tasks generated
    pub num_tasks: usize,
}