quantrs2_core/

quantum_hardware_abstraction.rs

//! Quantum Hardware Abstraction Layer
//!
//! Universal hardware-agnostic interface for quantum devices with
//! adaptive middleware and cross-platform quantum operation optimization.

use crate::error::QuantRS2Error;
use crate::gate::GateOp;
use crate::qubit::QubitId;
use scirs2_core::ndarray::Array2;
use scirs2_core::Complex64;
use std::collections::HashMap;
use std::sync::{Arc, Mutex, RwLock};
use std::time::{Duration, Instant, SystemTime};
// use uuid::Uuid;

/// Simple UUID replacement for ultrathink mode
pub type Uuid = u64;

fn generate_uuid() -> Uuid {
    use std::collections::hash_map::DefaultHasher;
    use std::hash::{Hash, Hasher};
    use std::time::SystemTime;

    let mut hasher = DefaultHasher::new();
    SystemTime::now().hash(&mut hasher);
    hasher.finish()
}

/// Universal quantum hardware abstraction interface
#[derive(Debug)]
pub struct QuantumHardwareAbstraction {
    pub abstraction_id: Uuid,
    pub hardware_backends: Vec<Arc<dyn QuantumHardwareBackend>>,
    pub middleware: AdaptiveMiddleware,
    pub resource_manager: HardwareResourceManager,
    pub calibration_engine: CalibrationEngine,
    pub error_mitigation: ErrorMitigationLayer,
}

/// Trait for quantum hardware backends
pub trait QuantumHardwareBackend: Send + Sync + std::fmt::Debug {
    fn backend_name(&self) -> &str;
    fn hardware_type(&self) -> HardwareType;
    fn capabilities(&self) -> HardwareCapabilities;
    fn status(&self) -> HardwareStatus;

    // Core operations
    fn initialize(&self) -> Result<(), QuantRS2Error>;
    fn calibrate(&self) -> Result<CalibrationResult, QuantRS2Error>;
    fn execute_gate(
        &self,
        gate: &dyn GateOp,
        context: &ExecutionContext,
    ) -> Result<ExecutionResult, QuantRS2Error>;
    fn execute_circuit(
        &self,
        circuit: &[Box<dyn GateOp>],
        context: &ExecutionContext,
    ) -> Result<CircuitResult, QuantRS2Error>;
    fn measure_qubits(
        &self,
        qubits: &[QubitId],
        basis: MeasurementBasis,
    ) -> Result<MeasurementResult, QuantRS2Error>;

    // Advanced features
    fn get_noise_model(&self) -> NoiseModel;
    fn estimate_fidelity(&self, operation: &dyn GateOp) -> f64;
    fn estimate_execution_time(&self, operation: &dyn GateOp) -> Duration;
    fn get_connectivity_graph(&self) -> ConnectivityGraph;
}

#[derive(Debug, Clone)]
pub enum HardwareType {
    Superconducting,
    TrappedIon,
    Photonic,
    NeutralAtom,
    Silicon,
    QuantumAnnealer,
    Simulator,
    Hybrid,
}

#[derive(Debug, Clone)]
pub struct HardwareCapabilities {
    pub qubit_count: usize,
    pub max_circuit_depth: usize,
    pub native_gates: Vec<String>,
    pub coherence_time: Duration,
    pub gate_fidelity: f64,
    pub measurement_fidelity: f64,
    pub supports_midcircuit_measurement: bool,
    pub supports_conditional_operations: bool,
    pub supports_reset: bool,
    pub max_shots: u64,
}

#[derive(Debug, Clone)]
pub enum HardwareStatus {
    Online,
    Offline,
    Calibrating,
    Busy,
    Error(String),
    Maintenance,
}

impl QuantumHardwareAbstraction {
    /// Create new quantum hardware abstraction
    pub fn new() -> Self {
        Self {
            abstraction_id: generate_uuid(),
            hardware_backends: Vec::new(),
            middleware: AdaptiveMiddleware::new(),
            resource_manager: HardwareResourceManager::new(),
            calibration_engine: CalibrationEngine::new(),
            error_mitigation: ErrorMitigationLayer::new(),
        }
    }

    /// Register a hardware backend
    pub fn register_backend(&mut self, backend: Arc<dyn QuantumHardwareBackend>) {
        self.hardware_backends.push(backend.clone());
        self.resource_manager.register_hardware(backend);
    }

    /// Execute operation with optimal backend selection
    pub async fn execute_operation(
        &self,
        operation: &dyn GateOp,
        requirements: &ExecutionRequirements,
    ) -> Result<QuantumOperationResult, QuantRS2Error> {
        // Select optimal backend
        let backend = self.select_optimal_backend(operation, requirements).await?;

        // Apply middleware transformations
        let optimized_operation = self
            .middleware
            .optimize_operation(operation, &backend)
            .await?;

        // Create execution context
        let context = self
            .create_execution_context(requirements, &backend)
            .await?;

        // Apply error mitigation preprocessing
        let mitigated_operation = self
            .error_mitigation
            .preprocess_operation(optimized_operation.as_ref(), &backend)?;

        // Execute on hardware
        let raw_result = backend.execute_gate(mitigated_operation.as_ref(), &context)?;

        // Apply error mitigation postprocessing
        let final_result = self
            .error_mitigation
            .postprocess_result(&raw_result, &backend)?;

        Ok(QuantumOperationResult {
            result: final_result.clone(),
            backend_used: backend.backend_name().to_string(),
            execution_time: context.execution_time,
            fidelity_estimate: final_result.fidelity,
            error_mitigation_applied: true,
        })
    }

    /// Execute quantum circuit with adaptive optimization
    pub async fn execute_circuit(
        &self,
        circuit: &[Box<dyn GateOp>],
        requirements: &ExecutionRequirements,
    ) -> Result<QuantumCircuitResult, QuantRS2Error> {
        // Analyze circuit for optimal backend selection
        let circuit_analysis = self.analyze_circuit(circuit).await?;
        let backend = self
            .select_optimal_backend_for_circuit(&circuit_analysis, requirements)
            .await?;

        // Apply circuit-level optimizations
        let optimized_circuit = self.middleware.optimize_circuit(circuit, &backend).await?;

        // Partition circuit if necessary for distributed execution
        let partitions = self
            .partition_circuit_if_needed(&optimized_circuit, &backend)
            .await?;
        let partition_count = partitions.len();

        let mut results = Vec::new();
        let start_time = Instant::now();

        for partition in partitions {
            let context = self
                .create_execution_context(requirements, &backend)
                .await?;
            let partition_result = backend.execute_circuit(&partition, &context)?;
            results.push(partition_result);
        }

        // Merge distributed results
        let final_result = self.merge_circuit_results(results)?;

        Ok(QuantumCircuitResult {
            circuit_result: final_result,
            backend_used: backend.backend_name().to_string(),
            total_execution_time: start_time.elapsed(),
            optimizations_applied: self.middleware.get_applied_optimizations(),
            partitions_used: partition_count,
        })
    }

    /// Select optimal backend for operation
    async fn select_optimal_backend(
        &self,
        operation: &dyn GateOp,
        requirements: &ExecutionRequirements,
    ) -> Result<Arc<dyn QuantumHardwareBackend>, QuantRS2Error> {
        let mut best_backend = None;
        let mut best_score = f64::NEG_INFINITY;

        for backend in &self.hardware_backends {
            if matches!(backend.status(), HardwareStatus::Online) {
                let score = self
                    .calculate_backend_score(backend, operation, requirements)
                    .await;
                if score > best_score {
                    best_score = score;
                    best_backend = Some(backend.clone());
                }
            }
        }

        best_backend.ok_or_else(|| {
            QuantRS2Error::NoHardwareAvailable("No suitable backends available".to_string())
        })
    }

    /// Calculate backend suitability score
    async fn calculate_backend_score(
        &self,
        backend: &Arc<dyn QuantumHardwareBackend>,
        operation: &dyn GateOp,
        requirements: &ExecutionRequirements,
    ) -> f64 {
        let mut score = 0.0;
        let capabilities = backend.capabilities();

        // Fidelity score
        let estimated_fidelity = backend.estimate_fidelity(operation);
        score += estimated_fidelity * requirements.fidelity_weight;

        // Speed score
        let estimated_time = backend.estimate_execution_time(operation);
        let speed_score = 1.0 / (1.0 + estimated_time.as_secs_f64());
        score += speed_score * requirements.speed_weight;

        // Native gate support
        let native_support = if capabilities
            .native_gates
            .contains(&operation.name().to_string())
        {
            1.0
        } else {
            0.5
        };
        score += native_support * requirements.native_gate_weight;

        // Resource availability
        let resource_score = self.resource_manager.get_availability_score(backend).await;
        score += resource_score * requirements.resource_weight;

        score
    }

    /// Analyze circuit characteristics
    async fn analyze_circuit(
        &self,
        circuit: &[Box<dyn GateOp>],
    ) -> Result<CircuitAnalysis, QuantRS2Error> {
        let mut analysis = CircuitAnalysis {
            total_gates: circuit.len(),
            gate_types: HashMap::new(),
            circuit_depth: 0,
            parallelism_factor: 0.0,
            entanglement_structure: Vec::new(),
            critical_path_length: Duration::ZERO,
            resource_requirements: ResourceRequirements::default(),
        };

        // Analyze gate distribution
        for gate in circuit {
            *analysis
                .gate_types
                .entry(gate.name().to_string())
                .or_insert(0) += 1;
        }

        // Calculate circuit depth
        analysis.circuit_depth = self.calculate_circuit_depth(circuit);

        // Analyze parallelism
        analysis.parallelism_factor = self.calculate_parallelism_factor(circuit);

        // Analyze entanglement structure
        analysis.entanglement_structure = self.analyze_entanglement_structure(circuit);

        Ok(analysis)
    }

    /// Calculate circuit depth
    fn calculate_circuit_depth(&self, circuit: &[Box<dyn GateOp>]) -> usize {
        let mut qubit_depths: HashMap<QubitId, usize> = HashMap::new();
        let mut max_depth = 0;

        for gate in circuit {
            let mut current_depth = 0;
            for qubit in gate.qubits() {
                current_depth = current_depth.max(*qubit_depths.get(&qubit).unwrap_or(&0));
            }
            current_depth += 1;

            for qubit in gate.qubits() {
                qubit_depths.insert(qubit, current_depth);
            }

            max_depth = max_depth.max(current_depth);
        }

        max_depth
    }

    /// Calculate parallelism factor
    fn calculate_parallelism_factor(&self, circuit: &[Box<dyn GateOp>]) -> f64 {
        if circuit.is_empty() {
            return 0.0;
        }

        let total_gates = circuit.len();
        let circuit_depth = self.calculate_circuit_depth(circuit);

        if circuit_depth == 0 {
            return 0.0;
        }

        total_gates as f64 / circuit_depth as f64
    }

    /// Analyze entanglement structure
    fn analyze_entanglement_structure(
        &self,
        circuit: &[Box<dyn GateOp>],
    ) -> Vec<EntanglementConnection> {
        let mut connections = Vec::new();

        for (i, gate) in circuit.iter().enumerate() {
            if gate.qubits().len() >= 2 {
                for j in 0..gate.qubits().len() {
                    for k in j + 1..gate.qubits().len() {
                        connections.push(EntanglementConnection {
                            qubit1: gate.qubits()[j],
                            qubit2: gate.qubits()[k],
                            gate_index: i,
                            strength: 1.0, // Simplified
                        });
                    }
                }
            }
        }

        connections
    }

    /// Create execution context
    async fn create_execution_context(
        &self,
        requirements: &ExecutionRequirements,
        backend: &Arc<dyn QuantumHardwareBackend>,
    ) -> Result<ExecutionContext, QuantRS2Error> {
        Ok(ExecutionContext {
            execution_id: generate_uuid(),
            backend_name: backend.backend_name().to_string(),
            shots: requirements.shots,
            optimization_level: requirements.optimization_level.clone(),
            error_mitigation_enabled: requirements.enable_error_mitigation,
            execution_time: Instant::now(),
            calibration_data: self
                .calibration_engine
                .get_latest_calibration(backend)
                .await?,
        })
    }

    /// Partition circuit for distributed execution
    async fn partition_circuit_if_needed(
        &self,
        circuit: &[Box<dyn GateOp>],
        backend: &Arc<dyn QuantumHardwareBackend>,
    ) -> Result<Vec<Vec<Box<dyn GateOp>>>, QuantRS2Error> {
        let capabilities = backend.capabilities();

        if circuit.len() <= capabilities.max_circuit_depth {
            // No partitioning needed
            Ok(vec![circuit.to_vec()])
        } else {
            // Partition circuit
            self.partition_circuit(circuit, capabilities.max_circuit_depth)
        }
    }

    /// Partition circuit into smaller chunks
    fn partition_circuit(
        &self,
        circuit: &[Box<dyn GateOp>],
        max_depth: usize,
    ) -> Result<Vec<Vec<Box<dyn GateOp>>>, QuantRS2Error> {
        let mut partitions = Vec::new();
        let mut current_partition = Vec::new();
        let mut current_depth = 0;

        for gate in circuit {
            if current_depth >= max_depth {
                partitions.push(current_partition);
                current_partition = Vec::new();
                current_depth = 0;
            }

            current_partition.push(gate.clone());
            current_depth += 1;
        }

        if !current_partition.is_empty() {
            partitions.push(current_partition);
        }

        Ok(partitions)
    }

    /// Merge results from distributed execution
    fn merge_circuit_results(
        &self,
        results: Vec<CircuitResult>,
    ) -> Result<CircuitResult, QuantRS2Error> {
        if results.is_empty() {
            return Err(QuantRS2Error::InvalidOperation(
                "No results to merge".to_string(),
            ));
        }

        if let [single_result] = results.as_slice() {
            return Ok(single_result.clone());
        }

        // Merge multiple results
        let mut merged_measurements = HashMap::new();
        let mut total_fidelity = 1.0;
        let mut total_shots = 0;

        for result in results {
            for (outcome, count) in result.measurements {
                *merged_measurements.entry(outcome).or_insert(0) += count;
            }
            total_fidelity *= result.fidelity;
            total_shots += result.shots;
        }

        Ok(CircuitResult {
            measurements: merged_measurements,
            fidelity: total_fidelity,
            shots: total_shots,
            execution_metadata: ExecutionMetadata::default(),
        })
    }

    /// Select optimal backend for circuit
    async fn select_optimal_backend_for_circuit(
        &self,
        analysis: &CircuitAnalysis,
        requirements: &ExecutionRequirements,
    ) -> Result<Arc<dyn QuantumHardwareBackend>, QuantRS2Error> {
        let mut best_backend = None;
        let mut best_score = f64::NEG_INFINITY;

        for backend in &self.hardware_backends {
            if matches!(backend.status(), HardwareStatus::Online) {
                let score = self
                    .calculate_circuit_backend_score(backend, analysis, requirements)
                    .await;
                if score > best_score {
                    best_score = score;
                    best_backend = Some(backend.clone());
                }
            }
        }

        best_backend.ok_or_else(|| {
            QuantRS2Error::NoHardwareAvailable("No suitable backends available".to_string())
        })
    }

    /// Calculate backend score for circuit
    async fn calculate_circuit_backend_score(
        &self,
        backend: &Arc<dyn QuantumHardwareBackend>,
        analysis: &CircuitAnalysis,
        requirements: &ExecutionRequirements,
    ) -> f64 {
        let mut score = 0.0;
        let capabilities = backend.capabilities();

        // Check if backend can handle circuit size
        if analysis.circuit_depth > capabilities.max_circuit_depth {
            score -= 1000.0; // Heavy penalty for insufficient capacity
        }

        // Native gate support score
        let mut native_gate_ratio = 0.0;
        for (gate_type, count) in &analysis.gate_types {
            if capabilities.native_gates.contains(gate_type) {
                native_gate_ratio += *count as f64;
            }
        }
        native_gate_ratio /= analysis.total_gates as f64;
        score += native_gate_ratio * requirements.native_gate_weight;

        // Fidelity score for circuit
        let estimated_circuit_fidelity =
            capabilities.gate_fidelity.powi(analysis.total_gates as i32);
        score += estimated_circuit_fidelity * requirements.fidelity_weight;

        // Parallelism utilization
        score += analysis.parallelism_factor * requirements.parallelism_weight;

        score
    }
}

/// Adaptive middleware for hardware optimization
#[derive(Debug)]
pub struct AdaptiveMiddleware {
    pub optimization_strategies: Vec<Box<dyn OptimizationStrategy>>,
    pub transformation_cache: Arc<RwLock<HashMap<String, TransformationResult>>>,
    pub learning_engine: AdaptiveLearningEngine,
}

pub trait OptimizationStrategy: Send + Sync + std::fmt::Debug {
    fn strategy_name(&self) -> &str;
    fn applicable_to(&self, backend_type: &HardwareType) -> bool;
    fn optimize_operation(
        &self,
        operation: &dyn GateOp,
        backend: &dyn QuantumHardwareBackend,
    ) -> Result<Box<dyn GateOp>, QuantRS2Error>;
    fn optimize_circuit(
        &self,
        circuit: &[Box<dyn GateOp>],
        backend: &dyn QuantumHardwareBackend,
    ) -> Result<Vec<Box<dyn GateOp>>, QuantRS2Error>;
}

impl AdaptiveMiddleware {
    pub fn new() -> Self {
        Self {
            optimization_strategies: Vec::new(),
            transformation_cache: Arc::new(RwLock::new(HashMap::new())),
            learning_engine: AdaptiveLearningEngine::new(),
        }
    }

    pub async fn optimize_operation(
        &self,
        operation: &dyn GateOp,
        backend: &Arc<dyn QuantumHardwareBackend>,
    ) -> Result<Box<dyn GateOp>, QuantRS2Error> {
        // Check cache first
        let cache_key = format!("{}_{}", operation.name(), backend.backend_name());
        {
            let cache = self
                .transformation_cache
                .read()
                .map_err(|e| QuantRS2Error::RuntimeError(format!("Lock poisoned: {e}")))?;
            if let Some(cached) = cache.get(&cache_key) {
                return Ok(cached.optimized_operation.clone());
            }
        }

        // Apply applicable optimization strategies
        let mut optimized_operation = operation.clone_gate();
        for strategy in &self.optimization_strategies {
            if strategy.applicable_to(&backend.hardware_type()) {
                optimized_operation =
                    strategy.optimize_operation(&*optimized_operation, &**backend)?;
            }
        }

        // Cache result
        if let Ok(mut cache) = self.transformation_cache.write() {
            cache.insert(
                cache_key,
                TransformationResult {
                    optimized_operation: optimized_operation.clone(),
                    transformation_time: Instant::now(),
                    performance_gain: 1.0, // Simplified
                },
            );
        }

        Ok(optimized_operation)
    }

    pub async fn optimize_circuit(
        &self,
        circuit: &[Box<dyn GateOp>],
        backend: &Arc<dyn QuantumHardwareBackend>,
    ) -> Result<Vec<Box<dyn GateOp>>, QuantRS2Error> {
        let mut optimized_circuit = circuit.to_vec();

        for strategy in &self.optimization_strategies {
            if strategy.applicable_to(&backend.hardware_type()) {
                optimized_circuit = strategy.optimize_circuit(&optimized_circuit, &**backend)?;
            }
        }

        Ok(optimized_circuit)
    }

    pub fn get_applied_optimizations(&self) -> Vec<String> {
        self.optimization_strategies
            .iter()
            .map(|s| s.strategy_name().to_string())
            .collect()
    }
}

/// Hardware resource manager
#[derive(Debug)]
pub struct HardwareResourceManager {
    pub registered_hardware: Vec<Arc<dyn QuantumHardwareBackend>>,
    pub resource_usage: Arc<RwLock<HashMap<String, ResourceUsage>>>,
    pub scheduling_queue: Arc<Mutex<Vec<ScheduledJob>>>,
}

impl HardwareResourceManager {
    pub fn new() -> Self {
        Self {
            registered_hardware: Vec::new(),
            resource_usage: Arc::new(RwLock::new(HashMap::new())),
            scheduling_queue: Arc::new(Mutex::new(Vec::new())),
        }
    }

    pub fn register_hardware(&mut self, backend: Arc<dyn QuantumHardwareBackend>) {
        self.registered_hardware.push(backend);
    }

    pub async fn get_availability_score(&self, backend: &Arc<dyn QuantumHardwareBackend>) -> f64 {
        let Ok(usage) = self.resource_usage.read() else {
            return 0.5; // Default score if lock is poisoned
        };
        if let Some(resource_usage) = usage.get(backend.backend_name()) {
            1.0 - resource_usage.utilization_ratio
        } else {
            1.0 // No usage data means fully available
        }
    }
}

/// Calibration engine for hardware
#[derive(Debug)]
pub struct CalibrationEngine {
    pub calibration_data: Arc<RwLock<HashMap<String, CalibrationData>>>,
    pub calibration_schedule: Arc<Mutex<Vec<CalibrationTask>>>,
}

impl CalibrationEngine {
    pub fn new() -> Self {
        Self {
            calibration_data: Arc::new(RwLock::new(HashMap::new())),
            calibration_schedule: Arc::new(Mutex::new(Vec::new())),
        }
    }

    pub async fn get_latest_calibration(
        &self,
        backend: &Arc<dyn QuantumHardwareBackend>,
    ) -> Result<CalibrationData, QuantRS2Error> {
        let calibration_data = self
            .calibration_data
            .read()
            .map_err(|e| QuantRS2Error::RuntimeError(format!("Lock poisoned: {e}")))?;
        calibration_data
            .get(backend.backend_name())
            .cloned()
            .ok_or_else(|| {
                QuantRS2Error::CalibrationNotFound(format!(
                    "No calibration data for {}",
                    backend.backend_name()
                ))
            })
    }
}

/// Error mitigation layer
#[derive(Debug)]
pub struct ErrorMitigationLayer {
    pub mitigation_protocols: Vec<Box<dyn ErrorMitigationProtocol>>,
    pub noise_characterization: NoiseCharacterization,
}

pub trait ErrorMitigationProtocol: Send + Sync + std::fmt::Debug {
    fn protocol_name(&self) -> &str;
    fn applicable_to(&self, noise_model: &NoiseModel) -> bool;
    fn preprocess_operation(
        &self,
        operation: &dyn GateOp,
    ) -> Result<Box<dyn GateOp>, QuantRS2Error>;
    fn postprocess_result(
        &self,
        result: &ExecutionResult,
    ) -> Result<ExecutionResult, QuantRS2Error>;
}

impl ErrorMitigationLayer {
    pub fn new() -> Self {
        Self {
            mitigation_protocols: Vec::new(),
            noise_characterization: NoiseCharacterization::new(),
        }
    }

    pub fn preprocess_operation(
        &self,
        operation: &dyn GateOp,
        backend: &Arc<dyn QuantumHardwareBackend>,
    ) -> Result<Box<dyn GateOp>, QuantRS2Error> {
        let noise_model = backend.get_noise_model();
        let mut processed_operation = operation.clone_gate();

        for protocol in &self.mitigation_protocols {
            if protocol.applicable_to(&noise_model) {
                processed_operation = protocol.preprocess_operation(&*processed_operation)?;
            }
        }

        Ok(processed_operation)
    }

    pub fn postprocess_result(
        &self,
        result: &ExecutionResult,
        backend: &Arc<dyn QuantumHardwareBackend>,
    ) -> Result<ExecutionResult, QuantRS2Error> {
        let noise_model = backend.get_noise_model();
        let mut processed_result = result.clone();

        for protocol in &self.mitigation_protocols {
            if protocol.applicable_to(&noise_model) {
                processed_result = protocol.postprocess_result(&processed_result)?;
            }
        }

        Ok(processed_result)
    }
}

/// Data structures for hardware abstraction
#[derive(Debug, Clone)]
pub struct ExecutionRequirements {
    pub shots: u64,
    pub optimization_level: OptimizationLevel,
    pub enable_error_mitigation: bool,
    pub fidelity_weight: f64,
    pub speed_weight: f64,
    pub native_gate_weight: f64,
    pub resource_weight: f64,
    pub parallelism_weight: f64,
}

#[derive(Debug, Clone)]
pub enum OptimizationLevel {
    None,
    Basic,
    Aggressive,
    Adaptive,
}

#[derive(Debug, Clone)]
pub struct ExecutionContext {
    pub execution_id: Uuid,
    pub backend_name: String,
    pub shots: u64,
    pub optimization_level: OptimizationLevel,
    pub error_mitigation_enabled: bool,
    pub execution_time: Instant,
    pub calibration_data: CalibrationData,
}

#[derive(Debug, Clone)]
pub struct ExecutionResult {
    pub measurement_outcomes: HashMap<String, u64>,
    pub fidelity: f64,
    pub execution_time: Duration,
    pub metadata: ExecutionMetadata,
}

#[derive(Debug, Clone)]
pub struct CircuitResult {
    pub measurements: HashMap<String, u64>,
    pub fidelity: f64,
    pub shots: u64,
    pub execution_metadata: ExecutionMetadata,
}

#[derive(Debug, Clone, Default)]
pub struct ExecutionMetadata {
    pub gate_count: usize,
    pub circuit_depth: usize,
    pub errors_detected: u64,
    pub calibration_drift: f64,
}

#[derive(Debug, Clone)]
pub struct CalibrationData {
    pub timestamp: SystemTime,
    pub gate_fidelities: HashMap<String, f64>,
    pub coherence_times: HashMap<QubitId, Duration>,
    pub cross_talk_matrix: Array2<f64>,
    pub temperature: f64,
}

#[derive(Debug, Clone)]
pub struct CalibrationResult {
    pub success: bool,
    pub data: CalibrationData,
    pub drift_detected: bool,
    pub recalibration_needed: bool,
}

#[derive(Debug, Clone)]
pub struct NoiseModel {
    pub gate_noise: HashMap<String, f64>,
    pub measurement_noise: f64,
    pub decoherence_rates: HashMap<QubitId, f64>,
    pub crosstalk_strengths: Array2<f64>,
}

#[derive(Debug, Clone)]
pub struct ConnectivityGraph {
    pub adjacency_matrix: Array2<bool>,
    pub edge_weights: HashMap<(QubitId, QubitId), f64>,
}

#[derive(Debug, Clone)]
pub enum MeasurementBasis {
    Computational,
    Hadamard,
    Custom(Array2<Complex64>),
}

#[derive(Debug, Clone)]
pub struct MeasurementResult {
    pub outcomes: HashMap<String, u64>,
    pub total_shots: u64,
    pub measurement_fidelity: f64,
}

// Additional supporting structures
#[derive(Debug, Clone)]
pub struct CircuitAnalysis {
    pub total_gates: usize,
    pub gate_types: HashMap<String, usize>,
    pub circuit_depth: usize,
    pub parallelism_factor: f64,
    pub entanglement_structure: Vec<EntanglementConnection>,
    pub critical_path_length: Duration,
    pub resource_requirements: ResourceRequirements,
}

#[derive(Debug, Clone)]
pub struct EntanglementConnection {
    pub qubit1: QubitId,
    pub qubit2: QubitId,
    pub gate_index: usize,
    pub strength: f64,
}

#[derive(Debug, Clone, Default)]
pub struct ResourceRequirements {
    pub qubits_needed: usize,
    pub memory_mb: usize,
    pub estimated_runtime: Duration,
}

#[derive(Debug, Clone)]
pub struct TransformationResult {
    pub optimized_operation: Box<dyn GateOp>,
    pub transformation_time: Instant,
    pub performance_gain: f64,
}

#[derive(Debug, Clone)]
pub struct ResourceUsage {
    pub utilization_ratio: f64,
    pub queue_length: usize,
    pub estimated_wait_time: Duration,
}

#[derive(Debug, Clone)]
pub struct ScheduledJob {
    pub job_id: Uuid,
    pub priority: JobPriority,
    pub estimated_duration: Duration,
    pub resource_requirements: ResourceRequirements,
}

#[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord)]
pub enum JobPriority {
    Low = 0,
    Medium = 1,
    High = 2,
    Critical = 3,
}

#[derive(Debug, Clone)]
pub struct CalibrationTask {
    pub task_id: Uuid,
    pub backend_name: String,
    pub scheduled_time: SystemTime,
    pub task_type: CalibrationType,
}

#[derive(Debug, Clone)]
pub enum CalibrationType {
    Full,
    Partial,
    Drift,
    Emergency,
}

#[derive(Debug)]
pub struct AdaptiveLearningEngine {
    pub performance_history: Arc<RwLock<Vec<PerformanceRecord>>>,
    pub optimization_suggestions: Arc<RwLock<Vec<OptimizationSuggestion>>>,
}

impl AdaptiveLearningEngine {
    pub fn new() -> Self {
        Self {
            performance_history: Arc::new(RwLock::new(Vec::new())),
            optimization_suggestions: Arc::new(RwLock::new(Vec::new())),
        }
    }
}

#[derive(Debug, Clone)]
pub struct PerformanceRecord {
    pub timestamp: SystemTime,
    pub backend_name: String,
    pub operation_type: String,
    pub fidelity: f64,
    pub execution_time: Duration,
    pub optimization_used: String,
}

#[derive(Debug, Clone)]
pub struct OptimizationSuggestion {
    pub suggestion_id: Uuid,
    pub backend_name: String,
    pub suggested_optimization: String,
    pub expected_improvement: f64,
    pub confidence: f64,
}

#[derive(Debug)]
pub struct NoiseCharacterization {
    pub characterized_devices: HashMap<String, NoiseModel>,
    pub characterization_history: Vec<NoiseCharacterizationRecord>,
}

impl NoiseCharacterization {
    pub fn new() -> Self {
        Self {
            characterized_devices: HashMap::new(),
            characterization_history: Vec::new(),
        }
    }
}

#[derive(Debug, Clone)]
pub struct NoiseCharacterizationRecord {
    pub timestamp: SystemTime,
    pub device_name: String,
    pub noise_model: NoiseModel,
    pub characterization_fidelity: f64,
}

// Result structures
#[derive(Debug, Clone)]
pub struct QuantumOperationResult {
    pub result: ExecutionResult,
    pub backend_used: String,
    pub execution_time: Instant,
    pub fidelity_estimate: f64,
    pub error_mitigation_applied: bool,
}

#[derive(Debug, Clone)]
pub struct QuantumCircuitResult {
    pub circuit_result: CircuitResult,
    pub backend_used: String,
    pub total_execution_time: Duration,
    pub optimizations_applied: Vec<String>,
    pub partitions_used: usize,
}

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

    #[test]
    fn test_hardware_abstraction_creation() {
        let abstraction = QuantumHardwareAbstraction::new();
        assert_eq!(abstraction.hardware_backends.len(), 0);
    }

    #[test]
    fn test_execution_requirements_default() {
        let requirements = ExecutionRequirements {
            shots: 1000,
            optimization_level: OptimizationLevel::Basic,
            enable_error_mitigation: true,
            fidelity_weight: 0.4,
            speed_weight: 0.3,
            native_gate_weight: 0.2,
            resource_weight: 0.1,
            parallelism_weight: 0.0,
        };

        assert_eq!(requirements.shots, 1000);
        assert!(requirements.enable_error_mitigation);
    }

    #[test]
    fn test_adaptive_middleware_creation() {
        let middleware = AdaptiveMiddleware::new();
        assert_eq!(middleware.optimization_strategies.len(), 0);
    }

    #[test]
    fn test_calibration_engine_creation() {
        let engine = CalibrationEngine::new();
        assert_eq!(
            engine
                .calibration_data
                .read()
                .expect("Failed to read calibration data")
                .len(),
            0
        );
    }

    #[test]
    fn test_error_mitigation_layer_creation() {
        let layer = ErrorMitigationLayer::new();
        assert_eq!(layer.mitigation_protocols.len(), 0);
    }
}