sklears_compose/

quantum.rs

//! Quantum Computing Pipeline Components
//!
//! This module provides experimental quantum computing integration for machine learning pipelines.
//! It includes quantum-inspired algorithms and interfaces for quantum computing backends.

use scirs2_core::ndarray::{Array1, Array2};
use sklears_core::error::Result;
use sklears_core::traits::Transform;
use std::collections::{HashMap, VecDeque};
use std::fmt::Debug;
use std::time::{Duration, Instant};

/// Quantum-inspired machine learning transformer
///
/// This transformer uses quantum computing concepts for data transformation
/// Currently provides a classical simulation of quantum algorithms
#[derive(Debug, Clone)]
pub struct QuantumTransformer {
    /// Number of qubits to simulate
    pub n_qubits: usize,
    /// Quantum gate configuration
    pub gate_sequence: Vec<QuantumGate>,
    /// Classical simulation backend
    pub backend: QuantumBackend,
}

/// Quantum gate types for the transformer
#[derive(Debug, Clone)]
pub enum QuantumGate {
    /// Hadamard gate
    Hadamard(usize),
    /// Pauli-X gate  
    PauliX(usize),
    /// Pauli-Y gate
    PauliY(usize),
    /// Pauli-Z gate
    PauliZ(usize),
    /// Rotation gate around X axis
    RotationX(usize, f64),
    /// Rotation gate around Y axis
    RotationY(usize, f64),
    /// Rotation gate around Z axis
    RotationZ(usize, f64),
    /// CNOT gate
    CNOT(usize, usize),
}

/// Quantum computing backend configuration
#[derive(Debug, Clone)]
pub enum QuantumBackend {
    /// Classical simulation (default)
    Simulator,
    /// IBM Qiskit backend
    Qiskit,
    /// Google Cirq backend  
    Cirq,
    /// Rigetti Forest backend
    Forest,
    /// Azure Quantum backend
    Azure,
}

impl Default for QuantumTransformer {
    fn default() -> Self {
        Self {
            n_qubits: 4,
            gate_sequence: vec![
                QuantumGate::Hadamard(0),
                QuantumGate::CNOT(0, 1),
                QuantumGate::RotationY(1, std::f64::consts::PI / 4.0),
            ],
            backend: QuantumBackend::Simulator,
        }
    }
}

impl QuantumTransformer {
    /// Create a new quantum transformer
    #[must_use]
    pub fn new(n_qubits: usize) -> Self {
        Self {
            n_qubits,
            ..Default::default()
        }
    }

    /// Add a quantum gate to the sequence
    pub fn add_gate(&mut self, gate: QuantumGate) -> &mut Self {
        self.gate_sequence.push(gate);
        self
    }

    /// Set the quantum backend
    #[must_use]
    pub fn with_backend(mut self, backend: QuantumBackend) -> Self {
        self.backend = backend;
        self
    }

    /// Apply quantum transformation to input data
    fn apply_quantum_transform(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
        // Classical simulation of quantum transformation
        let mut transformed = data.clone();

        // Apply quantum-inspired transformations based on gate sequence
        for gate in &self.gate_sequence {
            match gate {
                QuantumGate::Hadamard(qubit) => {
                    // Apply Hadamard-like transformation
                    if *qubit < transformed.ncols() {
                        for mut row in transformed.rows_mut() {
                            let val = row[*qubit];
                            row[*qubit] = val / std::f64::consts::SQRT_2;
                        }
                    }
                }
                QuantumGate::RotationY(qubit, angle) => {
                    // Apply Y-rotation transformation
                    if *qubit < transformed.ncols() {
                        for mut row in transformed.rows_mut() {
                            let val = row[*qubit];
                            row[*qubit] = val * angle.cos();
                        }
                    }
                }
                QuantumGate::CNOT(control, target) => {
                    // Apply controlled transformation
                    if *control < transformed.ncols() && *target < transformed.ncols() {
                        for mut row in transformed.rows_mut() {
                            if row[*control] > 0.0 {
                                row[*target] = -row[*target];
                            }
                        }
                    }
                }
                _ => {
                    // Placeholder for other gates - skip to next gate
                }
            }
        }

        Ok(transformed)
    }
}

impl<T: Clone + Into<f64> + Debug> Transform<Array2<T>, Array2<f64>> for QuantumTransformer {
    fn transform(&self, input: &Array2<T>) -> Result<Array2<f64>> {
        // Convert input to f64
        let data = input.mapv(std::convert::Into::into);
        self.apply_quantum_transform(&data)
    }
}

/// Quantum-inspired ensemble for combining classical and quantum models
#[derive(Debug, Clone)]
pub struct QuantumEnsemble<T> {
    /// Classical models in the ensemble
    pub classical_models: Vec<T>,
    /// Quantum transformers
    pub quantum_transformers: Vec<QuantumTransformer>,
    /// Ensemble weights
    pub weights: Array1<f64>,
}

impl<T> QuantumEnsemble<T> {
    /// Create a new quantum ensemble
    #[must_use]
    pub fn new() -> Self {
        Self {
            classical_models: Vec::new(),
            quantum_transformers: Vec::new(),
            weights: Array1::zeros(0),
        }
    }

    /// Add a classical model to the ensemble
    pub fn add_classical_model(mut self, model: T) -> Self {
        self.classical_models.push(model);
        self
    }

    /// Add a quantum transformer to the ensemble  
    #[must_use]
    pub fn add_quantum_transformer(mut self, transformer: QuantumTransformer) -> Self {
        self.quantum_transformers.push(transformer);
        self
    }

    /// Set ensemble weights
    #[must_use]
    pub fn with_weights(mut self, weights: Array1<f64>) -> Self {
        self.weights = weights;
        self
    }
}

impl<T> Default for QuantumEnsemble<T> {
    fn default() -> Self {
        Self::new()
    }
}

/// Quantum pipeline builder for creating quantum-classical hybrid workflows
#[derive(Debug)]
pub struct QuantumPipelineBuilder {
    steps: Vec<QuantumPipelineStep>,
}

/// Steps in a quantum pipeline
#[derive(Debug)]
pub enum QuantumPipelineStep {
    /// Classical preprocessing step
    ClassicalPreprocess(String),
    /// Quantum transformation step
    QuantumTransform(QuantumTransformer),
    /// Classical model step
    ClassicalModel(String),
    /// Quantum measurement step
    QuantumMeasurement,
}

impl QuantumPipelineBuilder {
    /// Create a new quantum pipeline builder
    #[must_use]
    pub fn new() -> Self {
        Self { steps: Vec::new() }
    }

    /// Add a classical preprocessing step
    #[must_use]
    pub fn add_classical_preprocess(mut self, name: String) -> Self {
        self.steps
            .push(QuantumPipelineStep::ClassicalPreprocess(name));
        self
    }

    /// Add a quantum transformation step
    #[must_use]
    pub fn add_quantum_transform(mut self, transformer: QuantumTransformer) -> Self {
        self.steps
            .push(QuantumPipelineStep::QuantumTransform(transformer));
        self
    }

    /// Add a classical model step
    #[must_use]
    pub fn add_classical_model(mut self, name: String) -> Self {
        self.steps.push(QuantumPipelineStep::ClassicalModel(name));
        self
    }

    /// Add a quantum measurement step
    #[must_use]
    pub fn add_quantum_measurement(mut self) -> Self {
        self.steps.push(QuantumPipelineStep::QuantumMeasurement);
        self
    }

    /// Build the quantum pipeline
    #[must_use]
    pub fn build(self) -> QuantumPipeline {
        QuantumPipeline { steps: self.steps }
    }
}

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

/// Quantum-classical hybrid pipeline
#[derive(Debug)]
pub struct QuantumPipeline {
    steps: Vec<QuantumPipelineStep>,
}

impl QuantumPipeline {
    /// Execute the quantum pipeline
    pub fn execute(&self, input: &Array2<f64>) -> Result<Array2<f64>> {
        let mut data = input.clone();

        for step in &self.steps {
            match step {
                QuantumPipelineStep::QuantumTransform(transformer) => {
                    data = transformer.apply_quantum_transform(&data)?;
                }
                QuantumPipelineStep::QuantumMeasurement => {
                    // Apply measurement operator (collapse to classical state)
                    data = data.mapv(|x| if x.abs() > 0.5 { 1.0 } else { 0.0 });
                }
                _ => {
                    // Placeholder for other steps - skip to next step
                }
            }
        }

        Ok(data)
    }
}

/// Hybrid quantum-classical workflow system
#[derive(Debug, Clone)]
pub struct HybridQuantumClassicalWorkflow {
    /// Quantum circuit components
    pub quantum_circuits: Vec<QuantumCircuit>,
    /// Classical processing components  
    pub classical_processors: Vec<ClassicalProcessor>,
    /// Workflow orchestration
    pub orchestrator: WorkflowOrchestrator,
    /// Optimization strategy
    pub optimization_strategy: QuantumClassicalOptimization,
}

/// Quantum circuit representation
#[derive(Debug, Clone)]
pub struct QuantumCircuit {
    /// Circuit identifier
    pub id: String,
    /// Number of qubits
    pub n_qubits: usize,
    /// Circuit depth
    pub depth: usize,
    /// Gate sequence
    pub gates: Vec<QuantumGate>,
    /// Circuit parameters (for parameterized circuits)
    pub parameters: Vec<f64>,
}

/// Classical processor for hybrid workflows
#[derive(Debug, Clone)]
pub struct ClassicalProcessor {
    /// Processor identifier
    pub id: String,
    /// Processing function type
    pub processor_type: ClassicalProcessorType,
    /// Configuration parameters
    pub config: HashMap<String, f64>,
}

/// Types of classical processors
#[derive(Debug, Clone)]
pub enum ClassicalProcessorType {
    /// Optimization algorithm
    Optimizer(OptimizerType),
    /// Data preprocessor
    Preprocessor(PreprocessorType),
    /// Model evaluator
    Evaluator(EvaluatorType),
    /// Parameter encoder/decoder
    Encoder(EncoderType),
}

/// Optimizer types for quantum-classical optimization
#[derive(Debug, Clone)]
pub enum OptimizerType {
    /// Variational Quantum Eigensolver
    VQE,
    /// Quantum Approximate Optimization Algorithm
    QAOA,
    /// Classical optimization
    Classical(String),
    /// Hybrid optimization
    Hybrid,
}

/// Preprocessor types
#[derive(Debug, Clone)]
pub enum PreprocessorType {
    /// Feature encoding for quantum states
    QuantumFeatureEncoding,
    /// Amplitude encoding
    AmplitudeEncoding,
    /// Angle encoding
    AngleEncoding,
    /// Basis encoding
    BasisEncoding,
}

/// Evaluator types
#[derive(Debug, Clone)]
pub enum EvaluatorType {
    /// Cost function evaluation
    CostFunction,
    /// Expectation value calculation
    ExpectationValue,
    /// Fidelity measurement
    Fidelity,
    /// Entanglement measure
    Entanglement,
}

/// Encoder types
#[derive(Debug, Clone)]
pub enum EncoderType {
    /// Classical-to-quantum encoding
    Classical2Quantum,
    /// Quantum-to-classical decoding
    Quantum2Classical,
    /// Parameter encoding
    Parameter,
}

/// Workflow orchestration system
#[derive(Debug, Clone)]
pub struct WorkflowOrchestrator {
    /// Execution schedule
    pub schedule: WorkflowSchedule,
    /// Resource allocation
    pub resources: QuantumResourceManager,
    /// Synchronization points
    pub sync_points: Vec<SynchronizationPoint>,
}

/// Workflow scheduling strategies
#[derive(Debug, Clone)]
pub enum WorkflowSchedule {
    /// Sequential execution
    Sequential,
    /// Parallel quantum-classical execution
    Parallel,
    /// Adaptive scheduling based on resource availability
    Adaptive,
    /// Time-sliced execution
    TimeSliced { slice_duration: Duration },
}

/// Quantum resource manager
#[derive(Debug, Clone)]
pub struct QuantumResourceManager {
    /// Available quantum backends
    pub backends: Vec<QuantumBackend>,
    /// Resource allocation strategy
    pub allocation_strategy: ResourceAllocationStrategy,
    /// Current resource usage
    pub usage: ResourceUsage,
}

/// Resource allocation strategies
#[derive(Debug, Clone)]
pub enum ResourceAllocationStrategy {
    /// First available
    FirstAvailable,
    /// Load balancing
    LoadBalanced,
    /// Optimization-aware allocation
    OptimizationAware,
    /// Cost-based allocation
    CostBased,
}

/// Resource usage tracking
#[derive(Debug, Clone)]
pub struct ResourceUsage {
    /// Quantum processing unit usage
    pub qpu_usage: HashMap<String, f64>,
    /// Memory usage
    pub memory_usage: HashMap<String, u64>,
    /// Network bandwidth usage
    pub network_usage: HashMap<String, f64>,
}

/// Synchronization points for hybrid workflows
#[derive(Debug, Clone)]
pub struct SynchronizationPoint {
    /// Synchronization identifier
    pub id: String,
    /// Components to synchronize
    pub components: Vec<String>,
    /// Synchronization type
    pub sync_type: SynchronizationType,
    /// Timeout duration
    pub timeout: Option<Duration>,
}

/// Types of synchronization
#[derive(Debug, Clone)]
pub enum SynchronizationType {
    /// Wait for all components
    BarrierSync,
    /// Wait for any component
    AnySync,
    /// Conditional synchronization
    ConditionalSync(String),
    /// Data exchange synchronization
    DataExchange,
}

/// Quantum-classical optimization strategies
#[derive(Debug, Clone)]
pub enum QuantumClassicalOptimization {
    /// Variational approach
    Variational {
        classical_optimizer: String,
        quantum_ansatz: String,
        max_iterations: usize,
    },
    /// Adiabatic approach
    Adiabatic {
        evolution_time: f64,
        schedule_function: String,
    },
    /// Hybrid optimization
    Hybrid {
        quantum_steps: usize,
        classical_steps: usize,
        convergence_threshold: f64,
    },
    /// Machine learning guided
    MLGuided {
        model_type: String,
        training_iterations: usize,
    },
}

/// Quantum advantage analysis system
#[derive(Debug)]
pub struct QuantumAdvantageAnalyzer {
    /// Benchmarking suite
    pub benchmarks: Vec<QuantumBenchmark>,
    /// Classical baselines
    pub classical_baselines: Vec<ClassicalBaseline>,
    /// Advantage metrics
    pub metrics: AdvantageMetrics,
    /// Analysis results
    pub results: Option<AdvantageAnalysisResult>,
}

/// Quantum benchmark definition
#[derive(Debug, Clone)]
pub struct QuantumBenchmark {
    /// Benchmark name
    pub name: String,
    /// Problem size
    pub problem_size: usize,
    /// Quantum algorithm
    pub quantum_algorithm: QuantumAlgorithm,
    /// Expected complexity
    pub complexity: AlgorithmComplexity,
}

/// Quantum algorithm types
#[derive(Debug, Clone)]
pub enum QuantumAlgorithm {
    /// Shor's algorithm for factoring
    Shor,
    /// Grover's search algorithm
    Grover,
    /// Variational Quantum Eigensolver
    VQE,
    /// Quantum Approximate Optimization Algorithm
    QAOA,
    /// Quantum Machine Learning algorithm
    QML(String),
    /// Custom algorithm
    Custom(String),
}

/// Classical baseline for comparison
#[derive(Debug, Clone)]
pub struct ClassicalBaseline {
    /// Algorithm name
    pub name: String,
    /// Implementation details
    pub implementation: String,
    /// Performance characteristics
    pub performance: PerformanceProfile,
}

/// Performance profile
#[derive(Debug, Clone)]
pub struct PerformanceProfile {
    /// Time complexity
    pub time_complexity: String,
    /// Space complexity
    pub space_complexity: String,
    /// Actual runtime
    pub runtime: Duration,
    /// Memory usage
    pub memory_usage: u64,
}

/// Algorithm complexity analysis
#[derive(Debug, Clone)]
pub struct AlgorithmComplexity {
    /// Classical complexity
    pub classical: String,
    /// Quantum complexity
    pub quantum: String,
    /// Speedup factor
    pub speedup_factor: Option<f64>,
}

/// Advantage analysis metrics
#[derive(Debug, Clone)]
pub struct AdvantageMetrics {
    /// Speed advantage
    pub speed_advantage: f64,
    /// Memory advantage
    pub memory_advantage: f64,
    /// Energy efficiency
    pub energy_efficiency: f64,
    /// Accuracy improvement
    pub accuracy_improvement: f64,
    /// Noise resilience
    pub noise_resilience: f64,
}

/// Advantage analysis result
#[derive(Debug, Clone)]
pub struct AdvantageAnalysisResult {
    /// Overall advantage score
    pub advantage_score: f64,
    /// Detailed breakdown
    pub breakdown: HashMap<String, f64>,
    /// Recommendations
    pub recommendations: Vec<String>,
    /// Confidence interval
    pub confidence_interval: (f64, f64),
}

/// Quantum workflow scheduler
#[derive(Debug)]
pub struct QuantumWorkflowScheduler {
    /// Scheduling queue
    pub queue: VecDeque<ScheduledWorkflow>,
    /// Resource manager
    pub resource_manager: QuantumResourceManager,
    /// Scheduling strategy
    pub strategy: SchedulingStrategy,
    /// Performance monitor
    pub monitor: PerformanceMonitor,
}

/// Scheduled workflow item
#[derive(Debug, Clone)]
pub struct ScheduledWorkflow {
    /// Workflow identifier
    pub id: String,
    /// Workflow definition
    pub workflow: HybridQuantumClassicalWorkflow,
    /// Scheduling priority
    pub priority: SchedulingPriority,
    /// Resource requirements
    pub requirements: ResourceRequirements,
    /// Deadline (optional)
    pub deadline: Option<Instant>,
    /// Dependencies
    pub dependencies: Vec<String>,
}

/// Scheduling priority levels
#[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord)]
pub enum SchedulingPriority {
    /// Low priority
    Low,
    /// Normal priority
    Normal,
    /// High priority
    High,
    /// Critical priority
    Critical,
}

/// Resource requirements specification
#[derive(Debug, Clone)]
pub struct ResourceRequirements {
    /// Required qubits
    pub qubits: usize,
    /// Circuit depth limit
    pub max_depth: usize,
    /// Memory requirements
    pub memory: u64,
    /// Execution time estimate
    pub estimated_time: Duration,
    /// Quality requirements
    pub quality: QualityRequirements,
}

/// Quality requirements
#[derive(Debug, Clone)]
pub struct QualityRequirements {
    /// Maximum acceptable error rate
    pub max_error_rate: f64,
    /// Required fidelity
    pub min_fidelity: f64,
    /// Coherence time requirements
    pub min_coherence_time: Duration,
}

/// Scheduling strategies
#[derive(Debug, Clone)]
pub enum SchedulingStrategy {
    /// First-Come-First-Served
    FCFS,
    /// Shortest Job First
    SJF,
    /// Priority-based scheduling
    Priority,
    /// Deadline-aware scheduling
    DeadlineAware,
    /// Resource-aware scheduling
    ResourceAware,
    /// Machine learning guided scheduling
    MLGuided,
}

/// Performance monitoring system
#[derive(Debug)]
pub struct PerformanceMonitor {
    /// Execution metrics
    pub metrics: HashMap<String, PerformanceMetric>,
    /// Historical data
    pub history: VecDeque<PerformanceSnapshot>,
    /// Alerting thresholds
    pub thresholds: AlertingThresholds,
}

/// Performance metric
#[derive(Debug, Clone)]
pub struct PerformanceMetric {
    /// Metric name
    pub name: String,
    /// Current value
    pub value: f64,
    /// Unit of measurement
    pub unit: String,
    /// Trend direction
    pub trend: TrendDirection,
}

/// Trend direction
#[derive(Debug, Clone)]
pub enum TrendDirection {
    /// Improving
    Improving,
    /// Stable
    Stable,
    /// Degrading
    Degrading,
    /// Unknown
    Unknown,
}

/// Performance snapshot
#[derive(Debug, Clone)]
pub struct PerformanceSnapshot {
    /// Timestamp
    pub timestamp: Instant,
    /// Metrics at this time
    pub metrics: HashMap<String, f64>,
    /// System state
    pub system_state: SystemState,
}

/// System state information
#[derive(Debug, Clone)]
pub struct SystemState {
    /// Active workflows
    pub active_workflows: usize,
    /// Queue length
    pub queue_length: usize,
    /// Resource utilization
    pub resource_utilization: f64,
    /// Error rate
    pub error_rate: f64,
}

/// Alerting thresholds
#[derive(Debug, Clone)]
pub struct AlertingThresholds {
    /// High utilization threshold
    pub high_utilization: f64,
    /// High error rate threshold
    pub high_error_rate: f64,
    /// Long queue threshold
    pub long_queue: usize,
    /// Deadline miss threshold
    pub deadline_miss_rate: f64,
}

#[allow(non_snake_case)]
#[cfg(test)]
mod tests {
    use super::*;
    use scirs2_core::ndarray::Array2;

    #[test]
    fn test_quantum_transformer_creation() {
        let transformer = QuantumTransformer::new(4);
        assert_eq!(transformer.n_qubits, 4);
    }

    #[test]
    fn test_quantum_transformer_with_gates() {
        let mut transformer = QuantumTransformer::new(2);
        transformer.add_gate(QuantumGate::Hadamard(0));
        transformer.add_gate(QuantumGate::CNOT(0, 1));

        assert_eq!(transformer.gate_sequence.len(), 5); // 3 default + 2 added
    }

    #[test]
    fn test_quantum_transform() {
        let transformer = QuantumTransformer::default();
        let input = Array2::from_shape_vec((2, 2), vec![1.0, 2.0, 3.0, 4.0]).unwrap();

        let result = transformer.transform(&input);
        assert!(result.is_ok());

        let transformed = result.unwrap();
        assert_eq!(transformed.shape(), input.shape());
    }

    #[test]
    fn test_quantum_ensemble_creation() {
        let ensemble: QuantumEnsemble<String> = QuantumEnsemble::new()
            .add_classical_model("linear_regression".to_string())
            .add_quantum_transformer(QuantumTransformer::default());

        assert_eq!(ensemble.classical_models.len(), 1);
        assert_eq!(ensemble.quantum_transformers.len(), 1);
    }

    #[test]
    fn test_quantum_pipeline_builder() {
        let pipeline = QuantumPipelineBuilder::new()
            .add_classical_preprocess("normalize".to_string())
            .add_quantum_transform(QuantumTransformer::default())
            .add_quantum_measurement()
            .add_classical_model("svm".to_string())
            .build();

        assert_eq!(pipeline.steps.len(), 4);
    }

    #[test]
    fn test_quantum_pipeline_execution() {
        let pipeline = QuantumPipelineBuilder::new()
            .add_quantum_transform(QuantumTransformer::default())
            .add_quantum_measurement()
            .build();

        let input = Array2::from_shape_vec((2, 2), vec![0.1, 0.2, 0.8, 0.9]).unwrap();
        let result = pipeline.execute(&input);

        assert!(result.is_ok());
        let output = result.unwrap();
        assert_eq!(output.shape(), input.shape());
    }

    #[test]
    fn test_quantum_backend_types() {
        let transformer = QuantumTransformer::default().with_backend(QuantumBackend::Qiskit);

        matches!(transformer.backend, QuantumBackend::Qiskit);
    }

    #[test]
    fn test_hybrid_quantum_classical_workflow() {
        let quantum_circuit = QuantumCircuit {
            id: "test_circuit".to_string(),
            n_qubits: 4,
            depth: 10,
            gates: vec![QuantumGate::Hadamard(0), QuantumGate::CNOT(0, 1)],
            parameters: vec![0.5, 1.0],
        };

        let classical_processor = ClassicalProcessor {
            id: "optimizer".to_string(),
            processor_type: ClassicalProcessorType::Optimizer(OptimizerType::VQE),
            config: HashMap::new(),
        };

        let workflow = HybridQuantumClassicalWorkflow {
            quantum_circuits: vec![quantum_circuit],
            classical_processors: vec![classical_processor],
            orchestrator: WorkflowOrchestrator {
                schedule: WorkflowSchedule::Sequential,
                resources: QuantumResourceManager {
                    backends: vec![QuantumBackend::Simulator],
                    allocation_strategy: ResourceAllocationStrategy::FirstAvailable,
                    usage: ResourceUsage {
                        qpu_usage: HashMap::new(),
                        memory_usage: HashMap::new(),
                        network_usage: HashMap::new(),
                    },
                },
                sync_points: vec![],
            },
            optimization_strategy: QuantumClassicalOptimization::Variational {
                classical_optimizer: "ADAM".to_string(),
                quantum_ansatz: "Hardware Efficient".to_string(),
                max_iterations: 100,
            },
        };

        assert_eq!(workflow.quantum_circuits.len(), 1);
        assert_eq!(workflow.classical_processors.len(), 1);
    }

    #[test]
    fn test_quantum_advantage_analyzer() {
        let benchmark = QuantumBenchmark {
            name: "VQE H2 molecule".to_string(),
            problem_size: 4,
            quantum_algorithm: QuantumAlgorithm::VQE,
            complexity: AlgorithmComplexity {
                classical: "O(4^n)".to_string(),
                quantum: "O(n^3)".to_string(),
                speedup_factor: Some(16.0),
            },
        };

        let baseline = ClassicalBaseline {
            name: "Full Configuration Interaction".to_string(),
            implementation: "SciPy sparse eigenvalue solver".to_string(),
            performance: PerformanceProfile {
                time_complexity: "O(4^n)".to_string(),
                space_complexity: "O(4^n)".to_string(),
                runtime: Duration::from_secs(3600),
                memory_usage: 1_000_000_000,
            },
        };

        let analyzer = QuantumAdvantageAnalyzer {
            benchmarks: vec![benchmark],
            classical_baselines: vec![baseline],
            metrics: AdvantageMetrics {
                speed_advantage: 16.0,
                memory_advantage: 8.0,
                energy_efficiency: 2.0,
                accuracy_improvement: 1.1,
                noise_resilience: 0.8,
            },
            results: None,
        };

        assert_eq!(analyzer.benchmarks.len(), 1);
        assert_eq!(analyzer.classical_baselines.len(), 1);
        assert_eq!(analyzer.metrics.speed_advantage, 16.0);
    }

    #[test]
    fn test_quantum_workflow_scheduler() {
        let workflow = HybridQuantumClassicalWorkflow {
            quantum_circuits: vec![],
            classical_processors: vec![],
            orchestrator: WorkflowOrchestrator {
                schedule: WorkflowSchedule::Sequential,
                resources: QuantumResourceManager {
                    backends: vec![QuantumBackend::Simulator],
                    allocation_strategy: ResourceAllocationStrategy::FirstAvailable,
                    usage: ResourceUsage {
                        qpu_usage: HashMap::new(),
                        memory_usage: HashMap::new(),
                        network_usage: HashMap::new(),
                    },
                },
                sync_points: vec![],
            },
            optimization_strategy: QuantumClassicalOptimization::Hybrid {
                quantum_steps: 10,
                classical_steps: 5,
                convergence_threshold: 1e-6,
            },
        };

        let scheduled_workflow = ScheduledWorkflow {
            id: "test_workflow".to_string(),
            workflow,
            priority: SchedulingPriority::Normal,
            requirements: ResourceRequirements {
                qubits: 8,
                max_depth: 100,
                memory: 1_000_000,
                estimated_time: Duration::from_secs(300),
                quality: QualityRequirements {
                    max_error_rate: 0.01,
                    min_fidelity: 0.95,
                    min_coherence_time: Duration::from_micros(100),
                },
            },
            deadline: None,
            dependencies: vec![],
        };

        let scheduler = QuantumWorkflowScheduler {
            queue: VecDeque::from([scheduled_workflow]),
            resource_manager: QuantumResourceManager {
                backends: vec![QuantumBackend::Simulator, QuantumBackend::Qiskit],
                allocation_strategy: ResourceAllocationStrategy::LoadBalanced,
                usage: ResourceUsage {
                    qpu_usage: HashMap::new(),
                    memory_usage: HashMap::new(),
                    network_usage: HashMap::new(),
                },
            },
            strategy: SchedulingStrategy::Priority,
            monitor: PerformanceMonitor {
                metrics: HashMap::new(),
                history: VecDeque::new(),
                thresholds: AlertingThresholds {
                    high_utilization: 0.8,
                    high_error_rate: 0.05,
                    long_queue: 10,
                    deadline_miss_rate: 0.1,
                },
            },
        };

        assert_eq!(scheduler.queue.len(), 1);
        assert_eq!(scheduler.resource_manager.backends.len(), 2);
        assert!(matches!(scheduler.strategy, SchedulingStrategy::Priority));
    }

    #[test]
    fn test_scheduling_priority_ordering() {
        let mut priorities = vec![
            SchedulingPriority::Low,
            SchedulingPriority::Critical,
            SchedulingPriority::Normal,
            SchedulingPriority::High,
        ];

        priorities.sort();

        assert_eq!(priorities[0], SchedulingPriority::Low);
        assert_eq!(priorities[1], SchedulingPriority::Normal);
        assert_eq!(priorities[2], SchedulingPriority::High);
        assert_eq!(priorities[3], SchedulingPriority::Critical);
    }

    #[test]
    fn test_quantum_classical_optimization_strategies() {
        let variational = QuantumClassicalOptimization::Variational {
            classical_optimizer: "L-BFGS-B".to_string(),
            quantum_ansatz: "UCCSD".to_string(),
            max_iterations: 200,
        };

        let adiabatic = QuantumClassicalOptimization::Adiabatic {
            evolution_time: 1000.0,
            schedule_function: "linear".to_string(),
        };

        let hybrid = QuantumClassicalOptimization::Hybrid {
            quantum_steps: 50,
            classical_steps: 25,
            convergence_threshold: 1e-8,
        };

        match variational {
            QuantumClassicalOptimization::Variational { max_iterations, .. } => {
                assert_eq!(max_iterations, 200)
            }
            _ => panic!("Wrong optimization type"),
        }

        match adiabatic {
            QuantumClassicalOptimization::Adiabatic { evolution_time, .. } => {
                assert_eq!(evolution_time, 1000.0)
            }
            _ => panic!("Wrong optimization type"),
        }

        match hybrid {
            QuantumClassicalOptimization::Hybrid {
                quantum_steps,
                classical_steps,
                ..
            } => {
                assert_eq!(quantum_steps, 50);
                assert_eq!(classical_steps, 25);
            }
            _ => panic!("Wrong optimization type"),
        }
    }
}