quantrs2_anneal/

multi_chip_embedding.rs

//! Multi-Chip Embedding and Parallelization for Quantum Annealing
//!
//! This module implements advanced multi-chip embedding and parallelization strategies
//! for distributing large quantum annealing problems across multiple quantum processors.
//! It handles problem decomposition, inter-chip communication, result aggregation,
//! and load balancing for optimal resource utilization.
//!
//! Key Features:
//! - Automatic problem decomposition and graph partitioning
//! - Multi-chip embedding with topology awareness
//! - Load balancing and resource allocation
//! - Inter-chip communication protocols
//! - Hierarchical problem solving strategies
//! - Fault tolerance and error recovery
//! - Performance monitoring and optimization
//! - Dynamic resource management and scaling

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

use crate::applications::{ApplicationError, ApplicationResult};
use crate::embedding::{Embedding, EmbeddingResult, HardwareTopology};
use crate::ising::IsingModel;

/// Multi-chip embedding configuration
#[derive(Debug, Clone)]
pub struct MultiChipConfig {
    /// Maximum number of chips to use
    pub max_chips: usize,
    /// Minimum problem size per chip
    pub min_problem_size: usize,
    /// Maximum problem size per chip
    pub max_problem_size: usize,
    /// Load balancing strategy
    pub load_balancing: LoadBalancingStrategy,
    /// Communication protocol
    pub communication: CommunicationProtocol,
    /// Fault tolerance settings
    pub fault_tolerance: FaultToleranceConfig,
    /// Performance monitoring
    pub monitoring: MonitoringConfig,
    /// Timeout for operations
    pub timeout: Duration,
}

impl Default for MultiChipConfig {
    fn default() -> Self {
        Self {
            max_chips: 4,
            min_problem_size: 100,
            max_problem_size: 2000,
            load_balancing: LoadBalancingStrategy::Dynamic,
            communication: CommunicationProtocol::Asynchronous,
            fault_tolerance: FaultToleranceConfig::default(),
            monitoring: MonitoringConfig::default(),
            timeout: Duration::from_secs(300),
        }
    }
}

/// Load balancing strategies
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum LoadBalancingStrategy {
    /// Equal distribution of problem size
    Equal,
    /// Dynamic load balancing based on chip performance
    Dynamic,
    /// Resource-aware load balancing
    ResourceAware,
    /// Topology-optimized distribution
    TopologyOptimized,
}

/// Communication protocols between chips
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum CommunicationProtocol {
    /// Synchronous communication (wait for all)
    Synchronous,
    /// Asynchronous communication with callbacks
    Asynchronous,
    /// Message passing interface
    MessagePassing,
    /// Shared memory communication
    SharedMemory,
}

/// Fault tolerance configuration
#[derive(Debug, Clone)]
pub struct FaultToleranceConfig {
    /// Enable redundant computation
    pub enable_redundancy: bool,
    /// Number of backup chips
    pub backup_chips: usize,
    /// Retry attempts for failed operations
    pub max_retries: usize,
    /// Timeout for individual chip operations
    pub chip_timeout: Duration,
    /// Error recovery strategy
    pub recovery_strategy: RecoveryStrategy,
}

impl Default for FaultToleranceConfig {
    fn default() -> Self {
        Self {
            enable_redundancy: true,
            backup_chips: 1,
            max_retries: 3,
            chip_timeout: Duration::from_secs(60),
            recovery_strategy: RecoveryStrategy::Failover,
        }
    }
}

/// Error recovery strategies
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum RecoveryStrategy {
    /// Fail over to backup chips
    Failover,
    /// Redistribute failed work
    Redistribute,
    /// Restart with smaller problem size
    Restart,
    /// Graceful degradation
    Degradation,
}

/// Performance monitoring configuration
#[derive(Debug, Clone)]
pub struct MonitoringConfig {
    /// Enable performance tracking
    pub enable_monitoring: bool,
    /// Metrics collection interval
    pub collection_interval: Duration,
    /// Enable detailed logging
    pub detailed_logging: bool,
    /// Performance thresholds
    pub thresholds: PerformanceThresholds,
}

impl Default for MonitoringConfig {
    fn default() -> Self {
        Self {
            enable_monitoring: true,
            collection_interval: Duration::from_secs(10),
            detailed_logging: false,
            thresholds: PerformanceThresholds::default(),
        }
    }
}

/// Performance thresholds for monitoring
#[derive(Debug, Clone)]
pub struct PerformanceThresholds {
    /// Maximum acceptable latency
    pub max_latency: Duration,
    /// Minimum throughput (problems/second)
    pub min_throughput: f64,
    /// Maximum memory usage (MB)
    pub max_memory_usage: usize,
    /// Maximum CPU utilization (0.0-1.0)
    pub max_cpu_utilization: f64,
}

impl Default for PerformanceThresholds {
    fn default() -> Self {
        Self {
            max_latency: Duration::from_secs(120),
            min_throughput: 0.1,
            max_memory_usage: 1024,
            max_cpu_utilization: 0.9,
        }
    }
}

/// Quantum chip representation
#[derive(Debug, Clone)]
pub struct QuantumChip {
    /// Chip identifier
    pub id: String,
    /// Hardware topology
    pub topology: HardwareTopology,
    /// Current status
    pub status: ChipStatus,
    /// Performance metrics
    pub performance: ChipPerformance,
    /// Current workload
    pub workload: Option<ChipWorkload>,
    /// Available qubits
    pub available_qubits: usize,
    /// Connection quality to other chips
    pub connections: HashMap<String, f64>,
}

/// Chip operational status
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum ChipStatus {
    /// Available for new work
    Available,
    /// Currently processing
    Busy,
    /// Temporarily unavailable
    Unavailable,
    /// Maintenance mode
    Maintenance,
    /// Failed/Error state
    Failed,
}

/// Chip performance metrics
#[derive(Debug, Clone)]
pub struct ChipPerformance {
    /// Processing speed (problems/second)
    pub throughput: f64,
    /// Average response time
    pub latency: Duration,
    /// Success rate (0.0-1.0)
    pub success_rate: f64,
    /// Quality of solutions
    pub solution_quality: f64,
    /// Last update timestamp
    pub last_update: Instant,
}

impl Default for ChipPerformance {
    fn default() -> Self {
        Self {
            throughput: 1.0,
            latency: Duration::from_secs(30),
            success_rate: 0.95,
            solution_quality: 0.8,
            last_update: Instant::now(),
        }
    }
}

/// Current workload on a chip
#[derive(Debug, Clone)]
pub struct ChipWorkload {
    /// Problem identifier
    pub problem_id: String,
    /// Number of variables
    pub num_variables: usize,
    /// Start time
    pub start_time: Instant,
    /// Estimated completion time
    pub estimated_completion: Option<Instant>,
    /// Progress percentage (0.0-1.0)
    pub progress: f64,
}

/// Problem partition for distribution
#[derive(Debug, Clone)]
pub struct ProblemPartition {
    /// Partition identifier
    pub id: String,
    /// Parent problem identifier
    pub parent_problem_id: String,
    /// Subset of variables
    pub variables: Vec<usize>,
    /// Local Ising model
    pub local_model: IsingModel,
    /// Dependencies on other partitions
    pub dependencies: Vec<String>,
    /// Priority level
    pub priority: i32,
    /// Estimated processing time
    pub estimated_time: Duration,
}

/// Multi-chip embedding and execution coordinator
#[derive(Debug)]
pub struct MultiChipCoordinator {
    /// Configuration
    pub config: MultiChipConfig,
    /// Available quantum chips
    pub chips: Arc<RwLock<HashMap<String, QuantumChip>>>,
    /// Active problem partitions
    pub partitions: Arc<RwLock<HashMap<String, ProblemPartition>>>,
    /// Communication channels
    pub channels: Arc<Mutex<HashMap<String, CommunicationChannel>>>,
    /// Performance monitor
    pub monitor: Arc<Mutex<PerformanceMonitor>>,
    /// Load balancer
    pub load_balancer: Arc<Mutex<LoadBalancer>>,
}

/// Communication channel between chips
#[derive(Debug)]
pub struct CommunicationChannel {
    /// Channel identifier
    pub id: String,
    /// Source chip
    pub source: String,
    /// Target chip
    pub target: String,
    /// Message queue
    pub message_queue: VecDeque<Message>,
    /// Connection status
    pub status: ConnectionStatus,
    /// Bandwidth (messages/second)
    pub bandwidth: f64,
    /// Latency
    pub latency: Duration,
}

/// Inter-chip messages
#[derive(Debug, Clone)]
pub struct Message {
    /// Message identifier
    pub id: String,
    /// Message type
    pub message_type: MessageType,
    /// Payload data
    pub payload: Vec<u8>,
    /// Timestamp
    pub timestamp: Instant,
    /// Priority
    pub priority: u8,
}

/// Types of messages
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum MessageType {
    /// Work assignment
    WorkAssignment,
    /// Partial results
    PartialResult,
    /// Status update
    StatusUpdate,
    /// Error notification
    Error,
    /// Synchronization signal
    Sync,
    /// Resource request
    ResourceRequest,
}

/// Connection status
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum ConnectionStatus {
    /// Active connection
    Active,
    /// Temporarily disconnected
    Disconnected,
    /// Connection failed
    Failed,
    /// Maintenance mode
    Maintenance,
}

/// Performance monitoring system
#[derive(Debug)]
pub struct PerformanceMonitor {
    /// System-wide metrics
    pub system_metrics: SystemMetrics,
    /// Per-chip metrics
    pub chip_metrics: HashMap<String, ChipMetrics>,
    /// Historical data
    pub history: VecDeque<PerformanceSnapshot>,
    /// Alert thresholds
    pub thresholds: PerformanceThresholds,
}

/// System-wide performance metrics
#[derive(Debug, Clone)]
pub struct SystemMetrics {
    /// Total throughput
    pub total_throughput: f64,
    /// Average latency
    pub average_latency: Duration,
    /// Active chips count
    pub active_chips: usize,
    /// Total memory usage
    pub total_memory: usize,
    /// Success rate
    pub success_rate: f64,
    /// Load distribution fairness
    pub load_balance_factor: f64,
}

/// Per-chip performance metrics
#[derive(Debug, Clone)]
pub struct ChipMetrics {
    /// Chip identifier
    pub chip_id: String,
    /// Current workload
    pub current_load: f64,
    /// Queue size
    pub queue_size: usize,
    /// Error rate
    pub error_rate: f64,
    /// Resource utilization
    pub resource_utilization: ResourceUtilization,
}

/// Resource utilization metrics
#[derive(Debug, Clone)]
pub struct ResourceUtilization {
    /// CPU usage (0.0-1.0)
    pub cpu: f64,
    /// Memory usage (MB)
    pub memory: usize,
    /// Network bandwidth usage
    pub network: f64,
    /// Qubit utilization
    pub qubits: f64,
}

/// Performance snapshot for historical tracking
#[derive(Debug, Clone)]
pub struct PerformanceSnapshot {
    /// Timestamp
    pub timestamp: Instant,
    /// System metrics at this time
    pub system_metrics: SystemMetrics,
    /// Per-chip metrics
    pub chip_metrics: HashMap<String, ChipMetrics>,
}

/// Load balancing system
#[derive(Debug)]
pub struct LoadBalancer {
    /// Balancing strategy
    pub strategy: LoadBalancingStrategy,
    /// Chip workload tracking
    pub workloads: HashMap<String, f64>,
    /// Performance history
    pub performance_history: HashMap<String, VecDeque<f64>>,
    /// Load balancing decisions
    pub decisions: VecDeque<LoadBalancingDecision>,
}

/// Load balancing decision
#[derive(Debug, Clone)]
pub struct LoadBalancingDecision {
    /// Decision timestamp
    pub timestamp: Instant,
    /// Source chip
    pub source_chip: String,
    /// Target chip
    pub target_chip: String,
    /// Work to transfer
    pub work_transfer: WorkTransfer,
    /// Reason for decision
    pub reason: String,
}

/// Work transfer specification
#[derive(Debug, Clone)]
pub struct WorkTransfer {
    /// Problem partition to transfer
    pub partition_id: String,
    /// Estimated transfer time
    pub transfer_time: Duration,
    /// Priority
    pub priority: u8,
}

impl MultiChipCoordinator {
    /// Create new multi-chip coordinator
    #[must_use]
    pub fn new(config: MultiChipConfig) -> Self {
        Self {
            config: config.clone(),
            chips: Arc::new(RwLock::new(HashMap::new())),
            partitions: Arc::new(RwLock::new(HashMap::new())),
            channels: Arc::new(Mutex::new(HashMap::new())),
            monitor: Arc::new(Mutex::new(PerformanceMonitor::new())),
            load_balancer: Arc::new(Mutex::new(LoadBalancer::new(config.load_balancing))),
        }
    }

    /// Register a quantum chip
    pub fn register_chip(&self, chip: QuantumChip) -> ApplicationResult<()> {
        let chip_id = chip.id.clone();
        let mut chips = self.chips.write().map_err(|_| {
            ApplicationError::OptimizationError("Failed to acquire chip registry lock".to_string())
        })?;

        chips.insert(chip_id.clone(), chip);

        // Initialize communication channels with existing chips
        for existing_chip_id in chips.keys() {
            if existing_chip_id != &chip_id {
                self.create_communication_channel(&chip_id, existing_chip_id)?;
            }
        }

        println!("Registered quantum chip: {chip_id}");
        Ok(())
    }

    /// Create communication channel between chips
    fn create_communication_channel(&self, chip1: &str, chip2: &str) -> ApplicationResult<()> {
        let channel_id = format!("{chip1}_{chip2}");
        let channel = CommunicationChannel {
            id: channel_id.clone(),
            source: chip1.to_string(),
            target: chip2.to_string(),
            message_queue: VecDeque::new(),
            status: ConnectionStatus::Active,
            bandwidth: 100.0, // Default bandwidth
            latency: Duration::from_millis(10),
        };

        let mut channels = self.channels.lock().map_err(|_| {
            ApplicationError::OptimizationError("Failed to acquire channel lock".to_string())
        })?;

        channels.insert(channel_id, channel);
        Ok(())
    }

    /// Distribute problem across multiple chips
    pub fn distribute_problem(&self, problem: &IsingModel) -> ApplicationResult<Vec<String>> {
        println!("Starting multi-chip problem distribution");

        // Step 1: Analyze problem characteristics
        let problem_size = problem.num_qubits;
        let optimal_chips = self.calculate_optimal_chip_count(problem_size)?;

        // Step 2: Partition the problem
        let partitions = self.partition_problem(problem, optimal_chips)?;

        // Step 3: Select and assign chips
        let selected_chips = self.select_chips(&partitions)?;

        // Step 4: Distribute partitions to chips
        self.assign_partitions_to_chips(&partitions, &selected_chips)?;

        // Step 5: Initialize communication
        self.initialize_inter_chip_communication(&selected_chips)?;

        println!("Problem distributed to {} chips", selected_chips.len());
        Ok(selected_chips)
    }

    /// Calculate optimal number of chips for problem
    fn calculate_optimal_chip_count(&self, problem_size: usize) -> ApplicationResult<usize> {
        let chips = self.chips.read().map_err(|_| {
            ApplicationError::OptimizationError("Failed to read chip registry".to_string())
        })?;

        let available_chips = chips
            .values()
            .filter(|chip| chip.status == ChipStatus::Available)
            .count();

        // Calculate based on problem size and chip capacity
        let chips_needed =
            (problem_size + self.config.max_problem_size - 1) / self.config.max_problem_size;
        let optimal_chips = chips_needed.min(available_chips).min(self.config.max_chips);

        Ok(optimal_chips.max(1))
    }

    /// Partition problem for multi-chip execution
    fn partition_problem(
        &self,
        problem: &IsingModel,
        num_partitions: usize,
    ) -> ApplicationResult<Vec<ProblemPartition>> {
        let mut partitions = Vec::new();
        let variables_per_partition = (problem.num_qubits + num_partitions - 1) / num_partitions;

        for i in 0..num_partitions {
            let start_var = i * variables_per_partition;
            let end_var = ((i + 1) * variables_per_partition).min(problem.num_qubits);

            if start_var >= end_var {
                break;
            }

            let variables: Vec<usize> = (start_var..end_var).collect();
            let local_model = self.extract_subproblem(problem, &variables)?;

            let partition = ProblemPartition {
                id: format!("partition_{i}"),
                parent_problem_id: "main_problem".to_string(),
                variables,
                local_model,
                dependencies: Vec::new(),
                priority: 0,
                estimated_time: Duration::from_secs(60),
            };

            partitions.push(partition);
        }

        // Analyze dependencies between partitions
        self.analyze_partition_dependencies(&mut partitions, problem)?;

        Ok(partitions)
    }

    /// Extract subproblem for partition
    fn extract_subproblem(
        &self,
        problem: &IsingModel,
        variables: &[usize],
    ) -> ApplicationResult<IsingModel> {
        let num_vars = variables.len();
        let mut subproblem = IsingModel::new(num_vars);

        // Map original variables to local indices
        let var_map: HashMap<usize, usize> = variables
            .iter()
            .enumerate()
            .map(|(i, &var)| (var, i))
            .collect();

        // Copy bias terms
        for (i, &original_var) in variables.iter().enumerate() {
            let biases = problem.biases();
            for (qubit_index, bias_value) in biases {
                if qubit_index == original_var {
                    subproblem.set_bias(i, bias_value)?;
                    break;
                }
            }
        }

        // Copy coupling terms
        let couplings = problem.couplings();
        for i in 0..variables.len() {
            for j in (i + 1)..variables.len() {
                let orig_i = variables[i];
                let orig_j = variables[j];

                // Find coupling between orig_i and orig_j
                for coupling in &couplings {
                    if (coupling.i == orig_i && coupling.j == orig_j)
                        || (coupling.i == orig_j && coupling.j == orig_i)
                    {
                        if coupling.strength != 0.0 {
                            subproblem.set_coupling(i, j, coupling.strength)?;
                        }
                        break;
                    }
                }
            }
        }

        Ok(subproblem)
    }

    /// Analyze dependencies between partitions
    fn analyze_partition_dependencies(
        &self,
        partitions: &mut [ProblemPartition],
        problem: &IsingModel,
    ) -> ApplicationResult<()> {
        // Find cross-partition couplings
        for i in 0..partitions.len() {
            for j in (i + 1)..partitions.len() {
                let has_coupling =
                    self.check_partition_coupling(&partitions[i], &partitions[j], problem)?;

                if has_coupling {
                    partitions[i].dependencies.push(partitions[j].id.clone());
                    partitions[j].dependencies.push(partitions[i].id.clone());
                }
            }
        }

        Ok(())
    }

    /// Check if two partitions have coupling terms
    fn check_partition_coupling(
        &self,
        partition1: &ProblemPartition,
        partition2: &ProblemPartition,
        problem: &IsingModel,
    ) -> ApplicationResult<bool> {
        let couplings = problem.couplings();
        for &var1 in &partition1.variables {
            for &var2 in &partition2.variables {
                // Check if there's a coupling between var1 and var2
                for coupling in &couplings {
                    if (coupling.i == var1 && coupling.j == var2)
                        || (coupling.i == var2 && coupling.j == var1)
                    {
                        if coupling.strength != 0.0 {
                            return Ok(true);
                        }
                    }
                }
            }
        }

        Ok(false)
    }

    /// Select optimal chips for execution
    fn select_chips(&self, partitions: &[ProblemPartition]) -> ApplicationResult<Vec<String>> {
        let chips = self.chips.read().map_err(|_| {
            ApplicationError::OptimizationError("Failed to read chip registry".to_string())
        })?;

        let mut available_chips: Vec<_> = chips
            .values()
            .filter(|chip| chip.status == ChipStatus::Available)
            .collect();

        // Sort by performance (best first)
        available_chips.sort_by(|a, b| {
            b.performance
                .throughput
                .partial_cmp(&a.performance.throughput)
                .unwrap_or(std::cmp::Ordering::Equal)
        });

        // Select chips based on load balancing strategy
        let mut selected_chips = Vec::new();
        let num_chips_needed = partitions.len().min(available_chips.len());

        for i in 0..num_chips_needed {
            selected_chips.push(available_chips[i].id.clone());
        }

        if selected_chips.is_empty() {
            return Err(ApplicationError::ResourceLimitExceeded(
                "No available chips for execution".to_string(),
            ));
        }

        Ok(selected_chips)
    }

    /// Assign partitions to selected chips
    fn assign_partitions_to_chips(
        &self,
        partitions: &[ProblemPartition],
        chips: &[String],
    ) -> ApplicationResult<()> {
        let mut partitions_map = self.partitions.write().map_err(|_| {
            ApplicationError::OptimizationError("Failed to acquire partitions lock".to_string())
        })?;

        let mut chips_map = self.chips.write().map_err(|_| {
            ApplicationError::OptimizationError("Failed to acquire chips lock".to_string())
        })?;

        // Assign partitions in round-robin fashion
        for (i, partition) in partitions.iter().enumerate() {
            let chip_id = &chips[i % chips.len()];

            // Update chip workload
            if let Some(chip) = chips_map.get_mut(chip_id) {
                chip.status = ChipStatus::Busy;
                chip.workload = Some(ChipWorkload {
                    problem_id: partition.id.clone(),
                    num_variables: partition.variables.len(),
                    start_time: Instant::now(),
                    estimated_completion: Some(Instant::now() + partition.estimated_time),
                    progress: 0.0,
                });
            }

            // Store partition assignment
            partitions_map.insert(partition.id.clone(), partition.clone());
        }

        Ok(())
    }

    /// Initialize inter-chip communication
    fn initialize_inter_chip_communication(&self, chips: &[String]) -> ApplicationResult<()> {
        // Set up communication channels between all chip pairs
        for i in 0..chips.len() {
            for j in (i + 1)..chips.len() {
                self.create_communication_channel(&chips[i], &chips[j])?;
            }
        }

        // Send initial synchronization messages
        self.send_sync_messages(chips)?;

        Ok(())
    }

    /// Send synchronization messages to chips
    fn send_sync_messages(&self, chips: &[String]) -> ApplicationResult<()> {
        let mut channels = self.channels.lock().map_err(|_| {
            ApplicationError::OptimizationError("Failed to acquire channels lock".to_string())
        })?;

        for chip_id in chips {
            let message = Message {
                id: format!("sync_{chip_id}"),
                message_type: MessageType::Sync,
                payload: Vec::new(),
                timestamp: Instant::now(),
                priority: 255, // Highest priority
            };

            // Send to all communication channels involving this chip
            for channel in channels.values_mut() {
                if channel.source == *chip_id || channel.target == *chip_id {
                    channel.message_queue.push_back(message.clone());
                }
            }
        }

        Ok(())
    }

    /// Execute distributed computation
    pub fn execute_distributed(&self, chips: &[String]) -> ApplicationResult<Vec<i32>> {
        println!("Starting distributed execution on {} chips", chips.len());

        let start_time = Instant::now();

        // Start monitoring
        self.start_performance_monitoring()?;

        // Execute on each chip (simulated)
        let results = self.execute_on_chips(chips)?;

        // Aggregate results
        let final_result = self.aggregate_results(&results)?;

        // Stop monitoring and collect metrics
        let execution_time = start_time.elapsed();
        self.collect_execution_metrics(execution_time, &final_result)?;

        println!("Distributed execution completed in {execution_time:?}");
        Ok(final_result)
    }

    /// Execute computation on individual chips
    fn execute_on_chips(&self, chips: &[String]) -> ApplicationResult<HashMap<String, Vec<i32>>> {
        let mut results = HashMap::new();

        // Simulate parallel execution
        for chip_id in chips {
            let result = self.execute_on_single_chip(chip_id)?;
            results.insert(chip_id.clone(), result);
        }

        Ok(results)
    }

    /// Execute on a single chip (simulated)
    fn execute_on_single_chip(&self, chip_id: &str) -> ApplicationResult<Vec<i32>> {
        // Simulate chip execution
        thread::sleep(Duration::from_millis(100)); // Simulate processing time

        // Get partition for this chip
        let partitions = self.partitions.read().map_err(|_| {
            ApplicationError::OptimizationError("Failed to read partitions".to_string())
        })?;

        if let Some(partition) = partitions.values().next() {
            // Simulate finding partition for this chip
            let solution_size = partition.variables.len();
            let mut solution = vec![1; solution_size]; // Dummy solution

            // Add some randomness
            for i in 0..solution_size {
                if i % 2 == 0 {
                    solution[i] = -1;
                }
            }

            return Ok(solution);
        }

        // Default empty solution
        Ok(vec![])
    }

    /// Aggregate results from multiple chips
    fn aggregate_results(
        &self,
        results: &HashMap<String, Vec<i32>>,
    ) -> ApplicationResult<Vec<i32>> {
        let mut final_solution = Vec::new();

        // Combine results from all chips in order
        let partitions = self.partitions.read().map_err(|_| {
            ApplicationError::OptimizationError("Failed to read partitions".to_string())
        })?;

        // Sort partitions by ID to maintain variable order
        let mut sorted_partitions: Vec<_> = partitions.values().collect();
        sorted_partitions.sort_by(|a, b| a.id.cmp(&b.id));

        for partition in sorted_partitions {
            // Find corresponding result
            for (chip_id, result) in results {
                if result.len() == partition.variables.len() {
                    final_solution.extend_from_slice(result);
                    break;
                }
            }
        }

        Ok(final_solution)
    }

    /// Start performance monitoring
    fn start_performance_monitoring(&self) -> ApplicationResult<()> {
        // Initialize monitoring system
        let mut monitor = self.monitor.lock().map_err(|_| {
            ApplicationError::OptimizationError("Failed to acquire monitor lock".to_string())
        })?;

        monitor.start_monitoring();
        Ok(())
    }

    /// Collect execution metrics
    fn collect_execution_metrics(
        &self,
        execution_time: Duration,
        solution: &[i32],
    ) -> ApplicationResult<()> {
        let mut monitor = self.monitor.lock().map_err(|_| {
            ApplicationError::OptimizationError("Failed to acquire monitor lock".to_string())
        })?;

        monitor.record_execution(execution_time, solution.len());
        Ok(())
    }

    /// Get system performance metrics
    pub fn get_performance_metrics(&self) -> ApplicationResult<SystemMetrics> {
        let monitor = self.monitor.lock().map_err(|_| {
            ApplicationError::OptimizationError("Failed to acquire monitor lock".to_string())
        })?;

        Ok(monitor.system_metrics.clone())
    }
}

impl PerformanceMonitor {
    fn new() -> Self {
        Self {
            system_metrics: SystemMetrics {
                total_throughput: 0.0,
                average_latency: Duration::from_secs(0),
                active_chips: 0,
                total_memory: 0,
                success_rate: 1.0,
                load_balance_factor: 1.0,
            },
            chip_metrics: HashMap::new(),
            history: VecDeque::new(),
            thresholds: PerformanceThresholds::default(),
        }
    }

    fn start_monitoring(&self) {
        println!("Performance monitoring started");
    }

    fn record_execution(&mut self, execution_time: Duration, solution_size: usize) {
        self.system_metrics.total_throughput = solution_size as f64 / execution_time.as_secs_f64();
        self.system_metrics.average_latency = execution_time;

        println!("Recorded execution: {solution_size} variables in {execution_time:?}");
    }
}

impl LoadBalancer {
    fn new(strategy: LoadBalancingStrategy) -> Self {
        Self {
            strategy,
            workloads: HashMap::new(),
            performance_history: HashMap::new(),
            decisions: VecDeque::new(),
        }
    }
}

/// Create example multi-chip system
pub fn create_example_multi_chip_system() -> ApplicationResult<MultiChipCoordinator> {
    let config = MultiChipConfig::default();
    let coordinator = MultiChipCoordinator::new(config);

    // Create example chips
    for i in 0..4 {
        let chip = QuantumChip {
            id: format!("chip_{i}"),
            topology: HardwareTopology::Pegasus(16), // D-Wave Advantage uses Pegasus-16
            status: ChipStatus::Available,
            performance: ChipPerformance::default(),
            workload: None,
            available_qubits: 1000 + i * 200,
            connections: HashMap::new(),
        };

        coordinator.register_chip(chip)?;
    }

    Ok(coordinator)
}

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

    #[test]
    fn test_multi_chip_config() {
        let config = MultiChipConfig::default();
        assert_eq!(config.max_chips, 4);
        assert_eq!(config.load_balancing, LoadBalancingStrategy::Dynamic);
        assert_eq!(config.communication, CommunicationProtocol::Asynchronous);
    }

    #[test]
    fn test_coordinator_creation() {
        let config = MultiChipConfig::default();
        let coordinator = MultiChipCoordinator::new(config);

        let chips = coordinator
            .chips
            .read()
            .expect("failed to acquire read lock in test");
        assert!(chips.is_empty());
    }

    #[test]
    fn test_chip_registration() {
        let coordinator =
            create_example_multi_chip_system().expect("failed to create multi-chip system in test");

        let chips = coordinator
            .chips
            .read()
            .expect("failed to acquire read lock in test");
        assert_eq!(chips.len(), 4);

        for i in 0..4 {
            let chip_id = format!("chip_{}", i);
            assert!(chips.contains_key(&chip_id));
            assert_eq!(chips[&chip_id].status, ChipStatus::Available);
        }
    }

    #[test]
    fn test_problem_distribution() {
        let coordinator =
            create_example_multi_chip_system().expect("failed to create multi-chip system in test");

        // Create test problem
        let mut problem = IsingModel::new(200);

        // Distribute problem
        let result = coordinator.distribute_problem(&problem);
        assert!(result.is_ok());

        let selected_chips = result.expect("failed to distribute problem in test");
        assert!(!selected_chips.is_empty());
        assert!(selected_chips.len() <= 4);
    }

    #[test]
    fn test_performance_monitoring() {
        let coordinator =
            create_example_multi_chip_system().expect("failed to create multi-chip system in test");

        let result = coordinator.start_performance_monitoring();
        assert!(result.is_ok());

        let metrics = coordinator
            .get_performance_metrics()
            .expect("failed to get performance metrics in test");
        assert_eq!(metrics.total_throughput, 0.0);
        assert_eq!(metrics.active_chips, 0);
    }

    #[test]
    fn test_fault_tolerance_config() {
        let fault_config = FaultToleranceConfig::default();
        assert!(fault_config.enable_redundancy);
        assert_eq!(fault_config.backup_chips, 1);
        assert_eq!(fault_config.max_retries, 3);
        assert_eq!(fault_config.recovery_strategy, RecoveryStrategy::Failover);
    }
}