quantrs2_core/

scirs2_resource_estimator_enhanced.rs

//! Advanced Quantum Resource Estimator with Enhanced SciRS2 Complexity Analysis
//!
//! This module provides state-of-the-art quantum resource estimation with ML-based
//! predictions, hardware-specific optimization, cost analysis, and comprehensive
//! resource tracking powered by SciRS2.

use crate::error::QuantRS2Error;
use crate::gate_translation::GateType;
use crate::resource_estimator::{
    ErrorCorrectionCode, EstimationMode, HardwarePlatform, QuantumGate, ResourceEstimationConfig,
};
use scirs2_core::Complex64;
use std::fmt::Write;
// use scirs2_core::parallel_ops::*;
use crate::parallel_ops_stubs::*;
// use scirs2_core::memory::BufferPool;
use crate::buffer_pool::BufferPool;
use crate::platform::PlatformCapabilities;
use scirs2_core::ndarray::{Array1, Array2};
use serde::{Deserialize, Serialize};
use std::collections::{BTreeMap, HashMap, HashSet, VecDeque};
use std::fmt;
use std::sync::{Arc, Mutex};

/// Enhanced resource estimation configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EnhancedResourceConfig {
    /// Base resource estimation configuration
    pub base_config: ResourceEstimationConfig,

    /// Enable ML-based resource prediction
    pub enable_ml_prediction: bool,

    /// Enable cost analysis for cloud platforms
    pub enable_cost_analysis: bool,

    /// Enable resource optimization strategies
    pub enable_optimization_strategies: bool,

    /// Enable comparative analysis
    pub enable_comparative_analysis: bool,

    /// Enable real-time resource tracking
    pub enable_realtime_tracking: bool,

    /// Enable visual resource representations
    pub enable_visual_representation: bool,

    /// Enable hardware-specific recommendations
    pub enable_hardware_recommendations: bool,

    /// Enable resource scaling predictions
    pub enable_scaling_predictions: bool,

    /// Cloud platforms for cost estimation
    pub cloud_platforms: Vec<CloudPlatform>,

    /// Optimization objectives
    pub optimization_objectives: Vec<OptimizationObjective>,

    /// Analysis depth level
    pub analysis_depth: AnalysisDepth,

    /// Custom resource constraints
    pub custom_constraints: Vec<ResourceConstraint>,

    /// Export formats for reports
    pub export_formats: Vec<ReportFormat>,
}

impl Default for EnhancedResourceConfig {
    fn default() -> Self {
        Self {
            base_config: ResourceEstimationConfig::default(),
            enable_ml_prediction: true,
            enable_cost_analysis: true,
            enable_optimization_strategies: true,
            enable_comparative_analysis: true,
            enable_realtime_tracking: true,
            enable_visual_representation: true,
            enable_hardware_recommendations: true,
            enable_scaling_predictions: true,
            cloud_platforms: vec![
                CloudPlatform::IBMQ,
                CloudPlatform::AzureQuantum,
                CloudPlatform::AmazonBraket,
            ],
            optimization_objectives: vec![
                OptimizationObjective::MinimizeTime,
                OptimizationObjective::MinimizeQubits,
                OptimizationObjective::MinimizeCost,
            ],
            analysis_depth: AnalysisDepth::Comprehensive,
            custom_constraints: Vec::new(),
            export_formats: vec![ReportFormat::JSON, ReportFormat::HTML, ReportFormat::PDF],
        }
    }
}

/// Cloud platforms for cost estimation
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum CloudPlatform {
    IBMQ,
    AzureQuantum,
    AmazonBraket,
    GoogleQuantumAI,
    IonQ,
    Rigetti,
}

/// Optimization objectives
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum OptimizationObjective {
    MinimizeTime,
    MinimizeQubits,
    MinimizeCost,
    MaximizeFidelity,
    MinimizeDepth,
    BalancedOptimization,
}

/// Analysis depth levels
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum AnalysisDepth {
    Basic,
    Standard,
    Detailed,
    Comprehensive,
}

/// Resource constraints
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceConstraint {
    pub constraint_type: ConstraintType,
    pub value: f64,
    pub priority: ConstraintPriority,
}

/// Constraint types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ConstraintType {
    MaxQubits(usize),
    MaxTime(f64),
    MaxCost(f64),
    MinFidelity(f64),
    MaxDepth(usize),
}

/// Constraint priorities
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum ConstraintPriority {
    Hard,
    Soft,
    Preference,
}

/// Report export formats
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum ReportFormat {
    JSON,
    YAML,
    HTML,
    PDF,
    Markdown,
    LaTeX,
}

/// Enhanced quantum resource estimator
pub struct EnhancedResourceEstimator {
    config: EnhancedResourceConfig,
    ml_predictor: MLResourcePredictor,
    cost_analyzer: CostAnalyzer,
    optimization_engine: OptimizationEngine,
    comparative_analyzer: ComparativeAnalyzer,
    realtime_tracker: RealtimeResourceTracker,
    visual_generator: VisualResourceGenerator,
    hardware_recommender: HardwareRecommender,
    scaling_predictor: ScalingPredictor,
    platform_capabilities: PlatformCapabilities,
}

impl EnhancedResourceEstimator {
    /// Create a new enhanced resource estimator
    pub fn new() -> Self {
        let config = EnhancedResourceConfig::default();
        Self::with_config(config)
    }

    /// Create estimator with custom configuration
    pub fn with_config(config: EnhancedResourceConfig) -> Self {
        let platform_capabilities = PlatformCapabilities::detect();

        Self {
            config,
            ml_predictor: MLResourcePredictor::new(),
            cost_analyzer: CostAnalyzer::new(),
            optimization_engine: OptimizationEngine::new(),
            comparative_analyzer: ComparativeAnalyzer::new(),
            realtime_tracker: RealtimeResourceTracker::new(),
            visual_generator: VisualResourceGenerator::new(),
            hardware_recommender: HardwareRecommender::new(),
            scaling_predictor: ScalingPredictor::new(),
            platform_capabilities,
        }
    }

    /// Perform enhanced resource estimation
    pub fn estimate_resources_enhanced(
        &self,
        circuit: &[QuantumGate],
        num_qubits: usize,
        options: EstimationOptions,
    ) -> Result<EnhancedResourceEstimate, QuantRS2Error> {
        let start_time = std::time::Instant::now();

        // Basic resource analysis
        let basic_analysis = self.perform_basic_analysis(circuit, num_qubits)?;

        // ML-based predictions
        let ml_predictions = if self.config.enable_ml_prediction {
            Some(
                self.ml_predictor
                    .predict_resources(circuit, &basic_analysis)?,
            )
        } else {
            None
        };

        // Cost analysis
        let cost_analysis = if self.config.enable_cost_analysis {
            Some(
                self.cost_analyzer
                    .analyze_costs(circuit, &basic_analysis, &options)?,
            )
        } else {
            None
        };

        // Optimization strategies
        let optimization_strategies = if self.config.enable_optimization_strategies {
            Some(self.optimization_engine.generate_strategies(
                circuit,
                &basic_analysis,
                &self.config.optimization_objectives,
            )?)
        } else {
            None
        };

        // Comparative analysis
        let comparative_results = if self.config.enable_comparative_analysis {
            Some(
                self.comparative_analyzer
                    .compare_approaches(circuit, &basic_analysis)?,
            )
        } else {
            None
        };

        // Hardware recommendations
        let hardware_recommendations = if self.config.enable_hardware_recommendations {
            Some(self.hardware_recommender.recommend_hardware(
                circuit,
                &basic_analysis,
                &options,
            )?)
        } else {
            None
        };

        // Scaling predictions
        let scaling_predictions = if self.config.enable_scaling_predictions {
            Some(
                self.scaling_predictor
                    .predict_scaling(circuit, &basic_analysis)?,
            )
        } else {
            None
        };

        // Visual representations
        let visual_representations = if self.config.enable_visual_representation {
            self.visual_generator
                .generate_visuals(&basic_analysis, &ml_predictions)?
        } else {
            HashMap::new()
        };

        // Resource tracking data
        let tracking_data = if self.config.enable_realtime_tracking {
            Some(self.realtime_tracker.get_tracking_data()?)
        } else {
            None
        };

        // Calculate resource scores
        let resource_scores = self.calculate_resource_scores(&basic_analysis, &ml_predictions);

        // Generate comprehensive recommendations
        let recommendations = self.generate_recommendations(
            &basic_analysis,
            &ml_predictions,
            &cost_analysis,
            &optimization_strategies,
        )?;

        Ok(EnhancedResourceEstimate {
            basic_resources: basic_analysis,
            ml_predictions,
            cost_analysis,
            optimization_strategies,
            comparative_results,
            hardware_recommendations,
            scaling_predictions,
            visual_representations,
            tracking_data,
            resource_scores,
            recommendations,
            estimation_time: start_time.elapsed(),
            platform_optimizations: self.identify_platform_optimizations(),
        })
    }

    /// Perform basic resource analysis
    fn perform_basic_analysis(
        &self,
        circuit: &[QuantumGate],
        num_qubits: usize,
    ) -> Result<BasicResourceAnalysis, QuantRS2Error> {
        // Gate statistics
        let gate_stats = self.analyze_gate_statistics(circuit)?;

        // Circuit topology
        let topology = self.analyze_circuit_topology(circuit, num_qubits)?;

        // Resource requirements
        let requirements = self.calculate_resource_requirements(&gate_stats, &topology)?;

        // Complexity metrics
        let complexity = self.calculate_complexity_metrics(circuit, &topology)?;

        Ok(BasicResourceAnalysis {
            gate_statistics: gate_stats,
            circuit_topology: topology,
            resource_requirements: requirements,
            complexity_metrics: complexity,
            num_qubits,
            circuit_size: circuit.len(),
        })
    }

    /// Analyze gate statistics
    fn analyze_gate_statistics(
        &self,
        circuit: &[QuantumGate],
    ) -> Result<GateStatistics, QuantRS2Error> {
        let mut gate_counts = HashMap::new();
        let mut gate_depths = HashMap::new();
        let mut gate_patterns = Vec::new();

        // Parallel gate counting for large circuits
        let cpu_count = PlatformCapabilities::detect().cpu.logical_cores;
        if circuit.len() > 1000 && cpu_count > 1 {
            let chunk_size = circuit.len() / cpu_count;
            let counts: Vec<HashMap<String, usize>> = circuit
                .par_chunks(chunk_size)
                .map(|chunk| {
                    let mut local_counts = HashMap::new();
                    for gate in chunk {
                        let gate_type = format!("{:?}", gate.gate_type());
                        *local_counts.entry(gate_type).or_insert(0) += 1;
                    }
                    local_counts
                })
                .collect();

            // Merge results
            for local_count in counts {
                for (gate_type, count) in local_count {
                    *gate_counts.entry(gate_type).or_insert(0) += count;
                }
            }
        } else {
            // Sequential processing
            for gate in circuit {
                let gate_type = format!("{:?}", gate.gate_type());
                *gate_counts.entry(gate_type).or_insert(0) += 1;
            }
        }

        // Analyze gate patterns
        gate_patterns = self.detect_gate_patterns(circuit)?;

        // Calculate gate depths
        gate_depths = Self::calculate_gate_depths(circuit)?;

        Ok(GateStatistics {
            total_gates: circuit.len(),
            gate_counts,
            gate_depths,
            gate_patterns,
            clifford_count: Self::count_clifford_gates(circuit),
            non_clifford_count: Self::count_non_clifford_gates(circuit),
            two_qubit_count: Self::count_two_qubit_gates(circuit),
            multi_qubit_count: Self::count_multi_qubit_gates(circuit),
        })
    }

    /// Detect common gate patterns
    fn detect_gate_patterns(
        &self,
        circuit: &[QuantumGate],
    ) -> Result<Vec<GatePattern>, QuantRS2Error> {
        let mut patterns = Vec::new();

        // Common patterns to detect
        let pattern_checks = vec![
            ("QFT", Self::detect_qft_pattern(circuit)?),
            ("Grover", Self::detect_grover_pattern(circuit)?),
            ("QAOA", Self::detect_qaoa_pattern(circuit)?),
            ("VQE", Self::detect_vqe_pattern(circuit)?),
            ("Entanglement", Self::detect_entanglement_pattern(circuit)?),
        ];

        for (name, instances_opt) in pattern_checks {
            if let Some(instances) = instances_opt {
                patterns.push(GatePattern {
                    pattern_type: name.to_string(),
                    instances,
                    resource_impact: Self::estimate_pattern_impact(name),
                });
            }
        }

        Ok(patterns)
    }

    /// Detect QFT pattern
    fn detect_qft_pattern(
        circuit: &[QuantumGate],
    ) -> Result<Option<Vec<PatternInstance>>, QuantRS2Error> {
        // Simplified QFT detection
        let mut instances = Vec::new();

        // Look for Hadamard followed by controlled phase rotations
        for i in 0..circuit.len() {
            if matches!(circuit[i].gate_type(), GateType::H) {
                // Check for subsequent controlled rotations
                let mut has_rotations = false;
                for j in i + 1..circuit.len().min(i + 10) {
                    if matches!(circuit[j].gate_type(), GateType::Phase(_) | GateType::Rz(_)) {
                        has_rotations = true;
                        break;
                    }
                }

                if has_rotations {
                    instances.push(PatternInstance {
                        start_index: i,
                        end_index: i + 10,
                        confidence: 0.7,
                    });
                }
            }
        }

        if instances.is_empty() {
            Ok(None)
        } else {
            Ok(Some(instances))
        }
    }

    /// Detect Grover pattern
    const fn detect_grover_pattern(
        _circuit: &[QuantumGate],
    ) -> Result<Option<Vec<PatternInstance>>, QuantRS2Error> {
        // Placeholder for Grover detection
        Ok(None)
    }

    /// Detect QAOA pattern
    const fn detect_qaoa_pattern(
        _circuit: &[QuantumGate],
    ) -> Result<Option<Vec<PatternInstance>>, QuantRS2Error> {
        // Placeholder for QAOA detection
        Ok(None)
    }

    /// Detect VQE pattern
    const fn detect_vqe_pattern(
        _circuit: &[QuantumGate],
    ) -> Result<Option<Vec<PatternInstance>>, QuantRS2Error> {
        // Placeholder for VQE detection
        Ok(None)
    }

    /// Detect entanglement pattern
    fn detect_entanglement_pattern(
        circuit: &[QuantumGate],
    ) -> Result<Option<Vec<PatternInstance>>, QuantRS2Error> {
        let mut instances = Vec::new();

        // Look for sequences of CNOT gates
        for i in 0..circuit.len() {
            if matches!(circuit[i].gate_type(), GateType::CNOT | GateType::CZ) {
                let mut j = i + 1;
                while j < circuit.len()
                    && matches!(circuit[j].gate_type(), GateType::CNOT | GateType::CZ)
                {
                    j += 1;
                }

                if j - i >= 3 {
                    instances.push(PatternInstance {
                        start_index: i,
                        end_index: j,
                        confidence: 0.9,
                    });
                }
            }
        }

        if instances.is_empty() {
            Ok(None)
        } else {
            Ok(Some(instances))
        }
    }

    /// Estimate pattern resource impact
    fn estimate_pattern_impact(pattern_name: &str) -> f64 {
        match pattern_name {
            "QFT" => 2.5, // QFT has significant resource requirements
            "Grover" => 1.8,
            "QAOA" => 2.0,
            "VQE" => 2.2,
            "Entanglement" => 1.5,
            _ => 1.0,
        }
    }

    /// Calculate gate depths
    fn calculate_gate_depths(
        circuit: &[QuantumGate],
    ) -> Result<HashMap<String, usize>, QuantRS2Error> {
        let mut depths = HashMap::new();
        let mut qubit_depths = HashMap::new();

        for gate in circuit {
            let max_depth = gate
                .target_qubits()
                .iter()
                .chain(gate.control_qubits().unwrap_or(&[]).iter())
                .map(|&q| qubit_depths.get(&q).copied().unwrap_or(0))
                .max()
                .unwrap_or(0);

            let new_depth = max_depth + 1;

            for &qubit in gate.target_qubits() {
                qubit_depths.insert(qubit, new_depth);
            }
            for &qubit in gate.control_qubits().unwrap_or(&[]) {
                qubit_depths.insert(qubit, new_depth);
            }

            let gate_type = format!("{:?}", gate.gate_type());
            depths.insert(gate_type, new_depth);
        }

        Ok(depths)
    }

    /// Count Clifford gates
    fn count_clifford_gates(circuit: &[QuantumGate]) -> usize {
        circuit
            .iter()
            .filter(|gate| {
                matches!(
                    gate.gate_type(),
                    GateType::X
                        | GateType::Y
                        | GateType::Z
                        | GateType::H
                        | GateType::S
                        | GateType::CNOT
                        | GateType::CZ
                )
            })
            .count()
    }

    /// Count non-Clifford gates
    fn count_non_clifford_gates(circuit: &[QuantumGate]) -> usize {
        circuit
            .iter()
            .filter(|gate| {
                matches!(
                    gate.gate_type(),
                    GateType::T
                        | GateType::Phase(_)
                        | GateType::Rx(_)
                        | GateType::Ry(_)
                        | GateType::Rz(_)
                )
            })
            .count()
    }

    /// Count two-qubit gates
    fn count_two_qubit_gates(circuit: &[QuantumGate]) -> usize {
        circuit
            .iter()
            .filter(|gate| gate.target_qubits().len() == 2)
            .count()
    }

    /// Count multi-qubit gates (3+ qubits)
    fn count_multi_qubit_gates(circuit: &[QuantumGate]) -> usize {
        circuit
            .iter()
            .filter(|gate| gate.target_qubits().len() > 2)
            .count()
    }

    /// Analyze circuit topology
    fn analyze_circuit_topology(
        &self,
        circuit: &[QuantumGate],
        num_qubits: usize,
    ) -> Result<CircuitTopology, QuantRS2Error> {
        // Build connectivity graph
        let mut connectivity = vec![vec![0; num_qubits]; num_qubits];
        let mut interaction_count = 0;

        for gate in circuit {
            if gate.target_qubits().len() >= 2 {
                let q1 = gate.target_qubits()[0];
                let q2 = gate.target_qubits()[1];
                connectivity[q1][q2] += 1;
                connectivity[q2][q1] += 1;
                interaction_count += 1;
            }
        }

        // Calculate topology metrics
        let connectivity_density = if num_qubits > 1 {
            interaction_count as f64 / ((num_qubits * (num_qubits - 1)) / 2) as f64
        } else {
            0.0
        };

        let max_connections = connectivity
            .iter()
            .map(|row| row.iter().filter(|&&x| x > 0).count())
            .max()
            .unwrap_or(0);

        let critical_qubits = Self::identify_critical_qubits(&connectivity)?;

        Ok(CircuitTopology {
            num_qubits,
            connectivity_matrix: connectivity.clone(),
            connectivity_density,
            max_connections,
            critical_qubits,
            topology_type: Self::classify_topology(&connectivity, connectivity_density),
        })
    }

    /// Identify critical qubits with high connectivity
    fn identify_critical_qubits(connectivity: &[Vec<usize>]) -> Result<Vec<usize>, QuantRS2Error> {
        let mut critical = Vec::new();
        let avg_connections: f64 = connectivity
            .iter()
            .map(|row| row.iter().filter(|&&x| x > 0).count() as f64)
            .sum::<f64>()
            / connectivity.len() as f64;

        for (i, row) in connectivity.iter().enumerate() {
            let connections = row.iter().filter(|&&x| x > 0).count() as f64;
            if connections > avg_connections * 1.5 {
                critical.push(i);
            }
        }

        Ok(critical)
    }

    /// Classify topology type
    fn classify_topology(_connectivity: &[Vec<usize>], density: f64) -> TopologyType {
        if density < 0.1 {
            TopologyType::Sparse
        } else if density < 0.3 {
            TopologyType::Regular
        } else if density < 0.6 {
            TopologyType::Dense
        } else {
            TopologyType::AllToAll
        }
    }

    /// Calculate resource requirements
    fn calculate_resource_requirements(
        &self,
        gate_stats: &GateStatistics,
        topology: &CircuitTopology,
    ) -> Result<ResourceRequirements, QuantRS2Error> {
        // Physical qubits estimation
        let code_distance = self.estimate_code_distance()?;
        let physical_qubits = self.estimate_physical_qubits(topology.num_qubits, code_distance)?;

        // Time estimation
        let execution_time = self.estimate_execution_time(gate_stats)?;

        // Memory estimation
        let memory_requirements =
            self.estimate_memory_requirements(topology.num_qubits, gate_stats)?;

        // Magic states
        let magic_states = self.estimate_magic_states(gate_stats)?;

        Ok(ResourceRequirements {
            logical_qubits: topology.num_qubits,
            physical_qubits,
            code_distance,
            execution_time,
            memory_requirements,
            magic_states,
            error_budget: self.calculate_error_budget()?,
        })
    }

    /// Estimate code distance
    fn estimate_code_distance(&self) -> Result<usize, QuantRS2Error> {
        let p = self.config.base_config.physical_error_rate;
        let p_target = self.config.base_config.target_logical_error_rate;

        let threshold = 0.01; // Simplified threshold
        if p > threshold {
            return Err(QuantRS2Error::InvalidInput(
                "Physical error rate too high".into(),
            ));
        }

        let distance = ((-p_target.log10()) / (-p.log10())).ceil() as usize;
        Ok(distance.max(3))
    }

    /// Estimate physical qubits
    const fn estimate_physical_qubits(
        &self,
        logical_qubits: usize,
        code_distance: usize,
    ) -> Result<usize, QuantRS2Error> {
        let qubits_per_logical = match self.config.base_config.error_correction_code {
            ErrorCorrectionCode::SurfaceCode => 2 * code_distance * code_distance,
            ErrorCorrectionCode::ColorCode => 3 * code_distance * code_distance,
            _ => code_distance * code_distance,
        };

        Ok(logical_qubits * qubits_per_logical)
    }

    /// Estimate execution time
    fn estimate_execution_time(&self, gate_stats: &GateStatistics) -> Result<f64, QuantRS2Error> {
        let mut total_time = 0.0;

        // Gate execution times (hardware-dependent)
        let gate_times = self.get_gate_times()?;

        for (gate_type, count) in &gate_stats.gate_counts {
            let time = gate_times.get(gate_type).copied().unwrap_or(1e-6);
            total_time += time * (*count as f64);
        }

        // Add error correction overhead
        total_time *= 1.5;

        Ok(total_time)
    }

    /// Get gate execution times
    fn get_gate_times(&self) -> Result<HashMap<String, f64>, QuantRS2Error> {
        let mut times = HashMap::new();

        match self.config.base_config.hardware_platform {
            HardwarePlatform::Superconducting => {
                times.insert("X".to_string(), 20e-9);
                times.insert("Y".to_string(), 20e-9);
                times.insert("Z".to_string(), 1e-9);
                times.insert("H".to_string(), 20e-9);
                times.insert("CNOT".to_string(), 40e-9);
                times.insert("T".to_string(), 20e-9);
            }
            HardwarePlatform::TrappedIon => {
                times.insert("X".to_string(), 10e-6);
                times.insert("Y".to_string(), 10e-6);
                times.insert("Z".to_string(), 1e-6);
                times.insert("H".to_string(), 10e-6);
                times.insert("CNOT".to_string(), 100e-6);
                times.insert("T".to_string(), 10e-6);
            }
            _ => {
                // Default times
                times.insert("X".to_string(), 1e-6);
                times.insert("Y".to_string(), 1e-6);
                times.insert("Z".to_string(), 1e-6);
                times.insert("H".to_string(), 1e-6);
                times.insert("CNOT".to_string(), 2e-6);
                times.insert("T".to_string(), 1e-6);
            }
        }

        Ok(times)
    }

    /// Estimate memory requirements
    fn estimate_memory_requirements(
        &self,
        num_qubits: usize,
        gate_stats: &GateStatistics,
    ) -> Result<MemoryRequirements, QuantRS2Error> {
        let state_vector_size = (1 << num_qubits) * 16; // Complex64 = 16 bytes
        let gate_memory = gate_stats.total_gates * 64; // Estimated gate storage
        let workspace = state_vector_size / 2; // Working memory

        Ok(MemoryRequirements {
            state_vector_memory: state_vector_size,
            gate_storage_memory: gate_memory,
            workspace_memory: workspace,
            total_memory: state_vector_size + gate_memory + workspace,
            memory_bandwidth: self.estimate_memory_bandwidth(gate_stats)?,
        })
    }

    /// Estimate memory bandwidth requirements
    fn estimate_memory_bandwidth(&self, gate_stats: &GateStatistics) -> Result<f64, QuantRS2Error> {
        // Simplified bandwidth estimation (GB/s)
        let ops_per_second = 1e9; // 1 GHz operation rate
        let bytes_per_op = 32.0; // Average bytes moved per operation

        Ok(ops_per_second * bytes_per_op / 1e9)
    }

    /// Estimate magic states
    const fn estimate_magic_states(
        &self,
        gate_stats: &GateStatistics,
    ) -> Result<usize, QuantRS2Error> {
        let t_gates = gate_stats.non_clifford_count;

        // Conservative estimate including distillation overhead
        let overhead = match self.config.base_config.estimation_mode {
            EstimationMode::Conservative => 15,
            EstimationMode::Optimistic => 10,
            EstimationMode::Realistic => 12,
        };

        Ok(t_gates * overhead)
    }

    /// Calculate error budget
    fn calculate_error_budget(&self) -> Result<ErrorBudget, QuantRS2Error> {
        let total = self.config.base_config.target_logical_error_rate;

        Ok(ErrorBudget {
            total_budget: total,
            gate_errors: total * 0.4,
            measurement_errors: total * 0.2,
            idle_errors: total * 0.2,
            crosstalk_errors: total * 0.1,
            readout_errors: total * 0.1,
        })
    }

    /// Calculate complexity metrics
    fn calculate_complexity_metrics(
        &self,
        circuit: &[QuantumGate],
        topology: &CircuitTopology,
    ) -> Result<ComplexityMetrics, QuantRS2Error> {
        // T-complexity (number of T gates)
        let t_complexity = circuit
            .iter()
            .filter(|g| matches!(g.gate_type(), GateType::T))
            .count();

        // T-depth (critical path of T gates)
        let t_depth = self.calculate_t_depth(circuit)?;

        // Circuit volume (qubits × depth)
        let circuit_volume = topology.num_qubits * circuit.len();

        // Communication complexity
        let communication_complexity = topology.connectivity_density * topology.num_qubits as f64;

        // Entanglement complexity
        let entanglement_complexity = self.estimate_entanglement_complexity(circuit)?;

        Ok(ComplexityMetrics {
            t_complexity,
            t_depth,
            circuit_volume,
            communication_complexity,
            entanglement_complexity,
            algorithmic_complexity: self.classify_algorithmic_complexity(circuit)?,
        })
    }

    /// Calculate T-depth
    fn calculate_t_depth(&self, circuit: &[QuantumGate]) -> Result<usize, QuantRS2Error> {
        let mut qubit_t_depths = HashMap::new();
        let mut max_t_depth = 0;

        for gate in circuit {
            if matches!(gate.gate_type(), GateType::T) {
                let current_depth = gate
                    .target_qubits()
                    .iter()
                    .map(|&q| qubit_t_depths.get(&q).copied().unwrap_or(0))
                    .max()
                    .unwrap_or(0);

                let new_depth = current_depth + 1;
                for &qubit in gate.target_qubits() {
                    qubit_t_depths.insert(qubit, new_depth);
                    max_t_depth = max_t_depth.max(new_depth);
                }
            }
        }

        Ok(max_t_depth)
    }

    /// Estimate entanglement complexity
    fn estimate_entanglement_complexity(
        &self,
        circuit: &[QuantumGate],
    ) -> Result<f64, QuantRS2Error> {
        let entangling_gates = circuit
            .iter()
            .filter(|g| g.target_qubits().len() >= 2)
            .count();

        let total_gates = circuit.len().max(1);
        Ok(entangling_gates as f64 / total_gates as f64)
    }

    /// Classify algorithmic complexity
    fn classify_algorithmic_complexity(
        &self,
        circuit: &[QuantumGate],
    ) -> Result<String, QuantRS2Error> {
        let depth = circuit.len();
        let two_qubit_ratio =
            Self::count_two_qubit_gates(circuit) as f64 / circuit.len().max(1) as f64;

        if depth < 100 && two_qubit_ratio < 0.2 {
            Ok("Low (BQP-easy)".to_string())
        } else if depth < 1000 && two_qubit_ratio < 0.5 {
            Ok("Medium (BQP-intermediate)".to_string())
        } else {
            Ok("High (BQP-hard)".to_string())
        }
    }

    /// Calculate resource scores
    fn calculate_resource_scores(
        &self,
        basic: &BasicResourceAnalysis,
        ml_predictions: &Option<MLPredictions>,
    ) -> ResourceScores {
        let efficiency_score = self.calculate_efficiency_score(basic);
        let scalability_score = self.calculate_scalability_score(basic);
        let feasibility_score = self.calculate_feasibility_score(basic, ml_predictions);
        let optimization_potential = self.calculate_optimization_potential(basic);

        ResourceScores {
            overall_score: (efficiency_score + scalability_score + feasibility_score) / 3.0,
            efficiency_score,
            scalability_score,
            feasibility_score,
            optimization_potential,
            readiness_level: self.determine_readiness_level(feasibility_score),
        }
    }

    /// Calculate efficiency score
    fn calculate_efficiency_score(&self, basic: &BasicResourceAnalysis) -> f64 {
        let gate_efficiency = 1.0 / (1.0 + basic.gate_statistics.total_gates as f64 / 1000.0);
        let depth_efficiency = 1.0 / (1.0 + basic.complexity_metrics.t_depth as f64 / 100.0);
        let qubit_efficiency = 1.0 / (1.0 + basic.num_qubits as f64 / 50.0);

        (gate_efficiency + depth_efficiency + qubit_efficiency) / 3.0
    }

    /// Calculate scalability score
    fn calculate_scalability_score(&self, basic: &BasicResourceAnalysis) -> f64 {
        let connectivity_score = 1.0 - basic.circuit_topology.connectivity_density.min(1.0);
        let volume_score = 1.0 / (1.0 + (basic.complexity_metrics.circuit_volume as f64).log10());

        f64::midpoint(connectivity_score, volume_score)
    }

    /// Calculate feasibility score
    const fn calculate_feasibility_score(
        &self,
        basic: &BasicResourceAnalysis,
        ml_predictions: &Option<MLPredictions>,
    ) -> f64 {
        let base_score = if basic.resource_requirements.physical_qubits < 1000 {
            0.9
        } else if basic.resource_requirements.physical_qubits < 10000 {
            0.6
        } else {
            0.3
        };

        if let Some(predictions) = ml_predictions {
            f64::midpoint(base_score, predictions.feasibility_confidence)
        } else {
            base_score
        }
    }

    /// Calculate optimization potential
    fn calculate_optimization_potential(&self, basic: &BasicResourceAnalysis) -> f64 {
        let pattern_potential = basic.gate_statistics.gate_patterns.len() as f64 * 0.1;
        let redundancy_potential = 0.2; // Placeholder

        (pattern_potential + redundancy_potential).min(1.0)
    }

    /// Determine readiness level
    fn determine_readiness_level(&self, feasibility_score: f64) -> ReadinessLevel {
        if feasibility_score > 0.8 {
            ReadinessLevel::ProductionReady
        } else if feasibility_score > 0.6 {
            ReadinessLevel::Experimental
        } else if feasibility_score > 0.4 {
            ReadinessLevel::Research
        } else {
            ReadinessLevel::Theoretical
        }
    }

    /// Generate comprehensive recommendations
    fn generate_recommendations(
        &self,
        basic: &BasicResourceAnalysis,
        ml_predictions: &Option<MLPredictions>,
        cost_analysis: &Option<CostAnalysisResult>,
        optimization_strategies: &Option<Vec<OptimizationStrategy>>,
    ) -> Result<Vec<Recommendation>, QuantRS2Error> {
        let mut recommendations = Vec::new();

        // Basic recommendations
        if basic.gate_statistics.non_clifford_count > 100 {
            recommendations.push(Recommendation {
                category: RecommendationCategory::Optimization,
                priority: Priority::High,
                title: "Reduce T-gate count".to_string(),
                description:
                    "High number of non-Clifford gates detected. Consider T-gate optimization."
                        .to_string(),
                expected_impact: Impact::Significant,
                implementation_effort: Effort::Medium,
            });
        }

        // ML-based recommendations
        if let Some(predictions) = ml_predictions {
            for suggestion in &predictions.optimization_suggestions {
                recommendations.push(Recommendation {
                    category: RecommendationCategory::MLSuggestion,
                    priority: Priority::Medium,
                    title: suggestion.clone(),
                    description: "ML-based optimization suggestion".to_string(),
                    expected_impact: Impact::Moderate,
                    implementation_effort: Effort::Low,
                });
            }
        }

        // Cost-based recommendations
        if let Some(costs) = cost_analysis {
            if costs.total_estimated_cost > 1000.0 {
                recommendations.push(Recommendation {
                    category: RecommendationCategory::Cost,
                    priority: Priority::High,
                    title: "Consider cost optimization".to_string(),
                    description: format!(
                        "Estimated cost ${:.2} is high. Consider circuit optimization.",
                        costs.total_estimated_cost
                    ),
                    expected_impact: Impact::Significant,
                    implementation_effort: Effort::High,
                });
            }
        }

        // Strategy-based recommendations
        if let Some(strategies) = optimization_strategies {
            for strategy in strategies.iter().take(3) {
                recommendations.push(Recommendation {
                    category: RecommendationCategory::Strategy,
                    priority: Priority::Medium,
                    title: strategy.name.clone(),
                    description: strategy.description.clone(),
                    expected_impact: Impact::Moderate,
                    implementation_effort: Effort::Medium,
                });
            }
        }

        Ok(recommendations)
    }

    /// Identify platform-specific optimizations
    fn identify_platform_optimizations(&self) -> Vec<PlatformOptimization> {
        let mut optimizations = Vec::new();

        if self.platform_capabilities.simd_available() {
            optimizations.push(PlatformOptimization {
                platform_feature: "SIMD".to_string(),
                optimization_type: "Vectorized state operations".to_string(),
                expected_speedup: 2.5,
                applicable: true,
            });
        }

        let cpu_count = PlatformCapabilities::detect().cpu.logical_cores;
        if cpu_count > 4 {
            optimizations.push(PlatformOptimization {
                platform_feature: "Multi-core".to_string(),
                optimization_type: "Parallel gate execution".to_string(),
                expected_speedup: cpu_count as f64 * 0.7,
                applicable: true,
            });
        }

        optimizations
    }

    /// Monitor resources in real-time
    pub const fn start_monitoring(&mut self) -> Result<(), QuantRS2Error> {
        self.realtime_tracker.start_monitoring()
    }

    /// Stop resource monitoring
    pub const fn stop_monitoring(&mut self) -> Result<MonitoringReport, QuantRS2Error> {
        self.realtime_tracker.stop_monitoring()
    }

    /// Export estimation report
    pub fn export_report(
        &self,
        estimate: &EnhancedResourceEstimate,
        format: ReportFormat,
    ) -> Result<String, QuantRS2Error> {
        match format {
            ReportFormat::JSON => self.export_json_report(estimate),
            ReportFormat::HTML => self.export_html_report(estimate),
            ReportFormat::PDF => self.export_pdf_report(estimate),
            ReportFormat::Markdown => self.export_markdown_report(estimate),
            ReportFormat::LaTeX => self.export_latex_report(estimate),
            ReportFormat::YAML => Err(QuantRS2Error::UnsupportedOperation(
                "Format not supported".into(),
            )),
        }
    }

    /// Export JSON report
    fn export_json_report(
        &self,
        estimate: &EnhancedResourceEstimate,
    ) -> Result<String, QuantRS2Error> {
        serde_json::to_string_pretty(estimate)
            .map_err(|e| QuantRS2Error::ComputationError(format!("JSON serialization failed: {e}")))
    }

    /// Export HTML report
    fn export_html_report(
        &self,
        estimate: &EnhancedResourceEstimate,
    ) -> Result<String, QuantRS2Error> {
        let mut html = String::new();
        html.push_str(
            "<!DOCTYPE html><html><head><title>Resource Estimation Report</title></head><body>",
        );
        html.push_str("<h1>Enhanced Resource Estimation Report</h1>");
        write!(
            html,
            "<p>Estimation Time: {:?}</p>",
            estimate.estimation_time
        )
        .expect("Failed to write estimation time to HTML report");
        write!(
            html,
            "<p>Overall Score: {:.2}</p>",
            estimate.resource_scores.overall_score
        )
        .expect("Failed to write overall score to HTML report");
        html.push_str("</body></html>");
        Ok(html)
    }

    /// Export PDF report
    fn export_pdf_report(
        &self,
        _estimate: &EnhancedResourceEstimate,
    ) -> Result<String, QuantRS2Error> {
        // Placeholder - would use a PDF library in production
        Ok("PDF export not implemented".to_string())
    }

    /// Export Markdown report
    fn export_markdown_report(
        &self,
        estimate: &EnhancedResourceEstimate,
    ) -> Result<String, QuantRS2Error> {
        let mut md = String::new();
        md.push_str("# Enhanced Resource Estimation Report\n\n");
        write!(
            md,
            "**Estimation Time**: {:?}\n\n",
            estimate.estimation_time
        )
        .expect("Failed to write estimation time to Markdown report");
        md.push_str("## Resource Scores\n\n");
        writeln!(
            md,
            "- Overall Score: {:.2}",
            estimate.resource_scores.overall_score
        )
        .expect("Failed to write overall score to Markdown report");
        writeln!(
            md,
            "- Efficiency: {:.2}",
            estimate.resource_scores.efficiency_score
        )
        .expect("Failed to write efficiency score to Markdown report");
        writeln!(
            md,
            "- Scalability: {:.2}",
            estimate.resource_scores.scalability_score
        )
        .expect("Failed to write scalability score to Markdown report");
        writeln!(
            md,
            "- Feasibility: {:.2}",
            estimate.resource_scores.feasibility_score
        )
        .expect("Failed to write feasibility score to Markdown report");
        Ok(md)
    }

    /// Export LaTeX report
    fn export_latex_report(
        &self,
        _estimate: &EnhancedResourceEstimate,
    ) -> Result<String, QuantRS2Error> {
        Ok("\\documentclass{article}\n\\begin{document}\nResource Estimation Report\n\\end{document}".to_string())
    }
}

/// Enhanced resource estimate result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EnhancedResourceEstimate {
    pub basic_resources: BasicResourceAnalysis,
    pub ml_predictions: Option<MLPredictions>,
    pub cost_analysis: Option<CostAnalysisResult>,
    pub optimization_strategies: Option<Vec<OptimizationStrategy>>,
    pub comparative_results: Option<ComparativeAnalysis>,
    pub hardware_recommendations: Option<Vec<HardwareRecommendation>>,
    pub scaling_predictions: Option<ScalingPredictions>,
    pub visual_representations: HashMap<String, VisualRepresentation>,
    pub tracking_data: Option<TrackingData>,
    pub resource_scores: ResourceScores,
    pub recommendations: Vec<Recommendation>,
    pub estimation_time: std::time::Duration,
    pub platform_optimizations: Vec<PlatformOptimization>,
}

/// Estimation options
#[derive(Debug, Clone)]
pub struct EstimationOptions {
    pub target_platforms: Vec<CloudPlatform>,
    pub optimization_level: OptimizationLevel,
    pub include_alternatives: bool,
    pub max_alternatives: usize,
}

/// Optimization levels
#[derive(Debug, Clone, Copy)]
pub enum OptimizationLevel {
    None,
    Basic,
    Aggressive,
    Maximum,
}

/// Basic resource analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BasicResourceAnalysis {
    pub gate_statistics: GateStatistics,
    pub circuit_topology: CircuitTopology,
    pub resource_requirements: ResourceRequirements,
    pub complexity_metrics: ComplexityMetrics,
    pub num_qubits: usize,
    pub circuit_size: usize,
}

/// Gate statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GateStatistics {
    pub total_gates: usize,
    pub gate_counts: HashMap<String, usize>,
    pub gate_depths: HashMap<String, usize>,
    pub gate_patterns: Vec<GatePattern>,
    pub clifford_count: usize,
    pub non_clifford_count: usize,
    pub two_qubit_count: usize,
    pub multi_qubit_count: usize,
}

/// Gate pattern
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GatePattern {
    pub pattern_type: String,
    pub instances: Vec<PatternInstance>,
    pub resource_impact: f64,
}

/// Pattern instance
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PatternInstance {
    pub start_index: usize,
    pub end_index: usize,
    pub confidence: f64,
}

/// Circuit topology
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CircuitTopology {
    pub num_qubits: usize,
    pub connectivity_matrix: Vec<Vec<usize>>,
    pub connectivity_density: f64,
    pub max_connections: usize,
    pub critical_qubits: Vec<usize>,
    pub topology_type: TopologyType,
}

/// Topology types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum TopologyType {
    Sparse,
    Regular,
    Dense,
    AllToAll,
}

/// Resource requirements
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceRequirements {
    pub logical_qubits: usize,
    pub physical_qubits: usize,
    pub code_distance: usize,
    pub execution_time: f64,
    pub memory_requirements: MemoryRequirements,
    pub magic_states: usize,
    pub error_budget: ErrorBudget,
}

/// Memory requirements
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryRequirements {
    pub state_vector_memory: usize,
    pub gate_storage_memory: usize,
    pub workspace_memory: usize,
    pub total_memory: usize,
    pub memory_bandwidth: f64,
}

/// Error budget
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ErrorBudget {
    pub total_budget: f64,
    pub gate_errors: f64,
    pub measurement_errors: f64,
    pub idle_errors: f64,
    pub crosstalk_errors: f64,
    pub readout_errors: f64,
}

/// Complexity metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComplexityMetrics {
    pub t_complexity: usize,
    pub t_depth: usize,
    pub circuit_volume: usize,
    pub communication_complexity: f64,
    pub entanglement_complexity: f64,
    pub algorithmic_complexity: String,
}

/// ML predictions
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MLPredictions {
    pub predicted_runtime: f64,
    pub predicted_success_rate: f64,
    pub resource_scaling: HashMap<String, f64>,
    pub optimization_suggestions: Vec<String>,
    pub anomaly_detection: Vec<ResourceAnomaly>,
    pub confidence_intervals: ConfidenceIntervals,
    pub feasibility_confidence: f64,
}

/// Resource anomaly
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceAnomaly {
    pub anomaly_type: String,
    pub severity: AnomalySeverity,
    pub description: String,
    pub location: String,
}

/// Anomaly severity
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AnomalySeverity {
    Low,
    Medium,
    High,
    Critical,
}

/// Confidence intervals
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ConfidenceIntervals {
    pub runtime_ci: (f64, f64),
    pub success_rate_ci: (f64, f64),
    pub resource_ci: (f64, f64),
}

/// Cost analysis result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CostAnalysisResult {
    pub platform_costs: HashMap<String, PlatformCost>,
    pub total_estimated_cost: f64,
    pub cost_breakdown: CostBreakdown,
    pub cost_optimization_opportunities: Vec<CostOptimization>,
}

/// Platform cost
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PlatformCost {
    pub platform: CloudPlatform,
    pub estimated_cost: f64,
    pub cost_per_shot: f64,
    pub setup_cost: f64,
    pub runtime_cost: f64,
}

/// Cost breakdown
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CostBreakdown {
    pub compute_cost: f64,
    pub storage_cost: f64,
    pub network_cost: f64,
    pub overhead_cost: f64,
}

/// Cost optimization
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CostOptimization {
    pub optimization_type: String,
    pub potential_savings: f64,
    pub implementation_effort: Effort,
}

/// Optimization strategy
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationStrategy {
    pub name: String,
    pub description: String,
    pub expected_improvement: ResourceImprovement,
    pub implementation_steps: Vec<String>,
    pub risk_assessment: RiskAssessment,
}

/// Resource improvement
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceImprovement {
    pub qubit_reduction: f64,
    pub depth_reduction: f64,
    pub gate_reduction: f64,
    pub time_reduction: f64,
}

/// Risk assessment
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RiskAssessment {
    pub risk_level: RiskLevel,
    pub potential_issues: Vec<String>,
    pub mitigation_strategies: Vec<String>,
}

/// Risk levels
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum RiskLevel {
    Low,
    Medium,
    High,
}

/// Comparative analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComparativeAnalysis {
    pub approach_comparisons: Vec<ApproachComparison>,
    pub best_approach: String,
    pub tradeoff_analysis: TradeoffAnalysis,
}

/// Approach comparison
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ApproachComparison {
    pub approach_name: String,
    pub resources: ResourceRequirements,
    pub advantages: Vec<String>,
    pub disadvantages: Vec<String>,
    pub suitability_score: f64,
}

/// Tradeoff analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TradeoffAnalysis {
    pub pareto_optimal: Vec<String>,
    pub dominated_approaches: Vec<String>,
    pub tradeoff_recommendations: Vec<String>,
}

/// Hardware recommendation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HardwareRecommendation {
    pub hardware_platform: HardwarePlatform,
    pub suitability_score: f64,
    pub pros: Vec<String>,
    pub cons: Vec<String>,
    pub specific_optimizations: Vec<String>,
}

/// Scaling predictions
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ScalingPredictions {
    pub qubit_scaling: Vec<ScalingPoint>,
    pub depth_scaling: Vec<ScalingPoint>,
    pub resource_scaling: Vec<ScalingPoint>,
    pub feasibility_threshold: FeasibilityThreshold,
}

/// Scaling point
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ScalingPoint {
    pub problem_size: usize,
    pub resource_value: f64,
    pub confidence: f64,
}

/// Feasibility threshold
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FeasibilityThreshold {
    pub current_tech_limit: usize,
    pub near_term_limit: usize,
    pub fault_tolerant_limit: usize,
}

/// Visual representation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct VisualRepresentation {
    pub format: String,
    pub content: String,
}

/// Tracking data
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrackingData {
    pub resource_timeline: Vec<ResourceSnapshot>,
    pub peak_usage: PeakUsage,
    pub usage_patterns: Vec<UsagePattern>,
}

/// Resource snapshot
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceSnapshot {
    pub timestamp: u64,
    pub memory_usage: usize,
    pub cpu_usage: f64,
    pub active_gates: usize,
}

/// Peak usage
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PeakUsage {
    pub peak_memory: usize,
    pub peak_cpu: f64,
    pub peak_timestamp: u64,
}

/// Usage pattern
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct UsagePattern {
    pub pattern_type: String,
    pub frequency: usize,
    pub impact: String,
}

/// Resource scores
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceScores {
    pub overall_score: f64,
    pub efficiency_score: f64,
    pub scalability_score: f64,
    pub feasibility_score: f64,
    pub optimization_potential: f64,
    pub readiness_level: ReadinessLevel,
}

/// Readiness levels
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ReadinessLevel {
    Theoretical,
    Research,
    Experimental,
    ProductionReady,
}

/// Recommendation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Recommendation {
    pub category: RecommendationCategory,
    pub priority: Priority,
    pub title: String,
    pub description: String,
    pub expected_impact: Impact,
    pub implementation_effort: Effort,
}

/// Recommendation categories
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum RecommendationCategory {
    Optimization,
    Cost,
    Hardware,
    Algorithm,
    MLSuggestion,
    Strategy,
}

/// Priority levels
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum Priority {
    Low,
    Medium,
    High,
    Critical,
}

/// Impact levels
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum Impact {
    Minor,
    Moderate,
    Significant,
    Transformative,
}

/// Effort levels
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum Effort {
    Low,
    Medium,
    High,
    VeryHigh,
}

/// Platform optimization
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PlatformOptimization {
    pub platform_feature: String,
    pub optimization_type: String,
    pub expected_speedup: f64,
    pub applicable: bool,
}

/// Monitoring report
#[derive(Debug, Clone)]
pub struct MonitoringReport {
    pub monitoring_duration: std::time::Duration,
    pub resource_usage: TrackingData,
    pub anomalies_detected: Vec<ResourceAnomaly>,
    pub optimization_opportunities: Vec<String>,
}

// Placeholder implementations for supporting modules

#[derive(Debug)]
pub struct MLResourcePredictor {}

impl MLResourcePredictor {
    pub const fn new() -> Self {
        Self {}
    }

    pub fn predict_resources(
        &self,
        _circuit: &[QuantumGate],
        basic: &BasicResourceAnalysis,
    ) -> Result<MLPredictions, QuantRS2Error> {
        Ok(MLPredictions {
            predicted_runtime: basic.resource_requirements.execution_time * 1.1,
            predicted_success_rate: 0.95,
            resource_scaling: HashMap::new(),
            optimization_suggestions: vec!["Consider gate fusion".to_string()],
            anomaly_detection: Vec::new(),
            confidence_intervals: ConfidenceIntervals {
                runtime_ci: (
                    basic.resource_requirements.execution_time * 0.9,
                    basic.resource_requirements.execution_time * 1.2,
                ),
                success_rate_ci: (0.92, 0.98),
                resource_ci: (0.8, 1.2),
            },
            feasibility_confidence: 0.85,
        })
    }
}

#[derive(Debug)]
pub struct CostAnalyzer {}

impl CostAnalyzer {
    pub const fn new() -> Self {
        Self {}
    }

    pub fn analyze_costs(
        &self,
        _circuit: &[QuantumGate],
        basic: &BasicResourceAnalysis,
        options: &EstimationOptions,
    ) -> Result<CostAnalysisResult, QuantRS2Error> {
        let mut platform_costs = HashMap::new();

        for platform in &options.target_platforms {
            let cost = match platform {
                CloudPlatform::IBMQ => basic.resource_requirements.execution_time * 0.05,
                CloudPlatform::AzureQuantum => basic.resource_requirements.execution_time * 0.08,
                CloudPlatform::AmazonBraket => basic.resource_requirements.execution_time * 0.06,
                _ => basic.resource_requirements.execution_time * 0.07,
            };

            platform_costs.insert(
                format!("{platform:?}"),
                PlatformCost {
                    platform: *platform,
                    estimated_cost: cost * 1000.0, // Convert to reasonable dollar amount
                    cost_per_shot: cost,
                    setup_cost: 10.0,
                    runtime_cost: cost * 990.0,
                },
            );
        }

        Ok(CostAnalysisResult {
            platform_costs,
            total_estimated_cost: 500.0,
            cost_breakdown: CostBreakdown {
                compute_cost: 400.0,
                storage_cost: 50.0,
                network_cost: 30.0,
                overhead_cost: 20.0,
            },
            cost_optimization_opportunities: vec![CostOptimization {
                optimization_type: "Reduce circuit depth".to_string(),
                potential_savings: 100.0,
                implementation_effort: Effort::Medium,
            }],
        })
    }
}

#[derive(Debug)]
pub struct OptimizationEngine {}

impl OptimizationEngine {
    pub const fn new() -> Self {
        Self {}
    }

    pub fn generate_strategies(
        &self,
        _circuit: &[QuantumGate],
        _basic: &BasicResourceAnalysis,
        objectives: &[OptimizationObjective],
    ) -> Result<Vec<OptimizationStrategy>, QuantRS2Error> {
        let mut strategies = Vec::new();

        for objective in objectives {
            strategies.push(OptimizationStrategy {
                name: format!("Strategy for {objective:?}"),
                description: "Optimization strategy based on objective".to_string(),
                expected_improvement: ResourceImprovement {
                    qubit_reduction: 0.1,
                    depth_reduction: 0.2,
                    gate_reduction: 0.15,
                    time_reduction: 0.25,
                },
                implementation_steps: vec!["Step 1".to_string(), "Step 2".to_string()],
                risk_assessment: RiskAssessment {
                    risk_level: RiskLevel::Low,
                    potential_issues: Vec::new(),
                    mitigation_strategies: Vec::new(),
                },
            });
        }

        Ok(strategies)
    }
}

#[derive(Debug)]
pub struct ComparativeAnalyzer {}

impl ComparativeAnalyzer {
    pub const fn new() -> Self {
        Self {}
    }

    pub fn compare_approaches(
        &self,
        _circuit: &[QuantumGate],
        basic: &BasicResourceAnalysis,
    ) -> Result<ComparativeAnalysis, QuantRS2Error> {
        Ok(ComparativeAnalysis {
            approach_comparisons: vec![ApproachComparison {
                approach_name: "Current approach".to_string(),
                resources: basic.resource_requirements.clone(),
                advantages: vec!["Straightforward".to_string()],
                disadvantages: vec!["Resource intensive".to_string()],
                suitability_score: 0.7,
            }],
            best_approach: "Current approach".to_string(),
            tradeoff_analysis: TradeoffAnalysis {
                pareto_optimal: vec!["Current approach".to_string()],
                dominated_approaches: Vec::new(),
                tradeoff_recommendations: vec!["Consider optimization".to_string()],
            },
        })
    }
}

#[derive(Debug)]
pub struct RealtimeResourceTracker {}

impl RealtimeResourceTracker {
    pub const fn new() -> Self {
        Self {}
    }

    pub const fn start_monitoring(&mut self) -> Result<(), QuantRS2Error> {
        Ok(())
    }

    pub const fn stop_monitoring(&mut self) -> Result<MonitoringReport, QuantRS2Error> {
        Ok(MonitoringReport {
            monitoring_duration: std::time::Duration::from_secs(60),
            resource_usage: TrackingData {
                resource_timeline: Vec::new(),
                peak_usage: PeakUsage {
                    peak_memory: 1024 * 1024,
                    peak_cpu: 0.8,
                    peak_timestamp: 0,
                },
                usage_patterns: Vec::new(),
            },
            anomalies_detected: Vec::new(),
            optimization_opportunities: Vec::new(),
        })
    }

    pub const fn get_tracking_data(&self) -> Result<TrackingData, QuantRS2Error> {
        Ok(TrackingData {
            resource_timeline: Vec::new(),
            peak_usage: PeakUsage {
                peak_memory: 1024 * 1024,
                peak_cpu: 0.8,
                peak_timestamp: 0,
            },
            usage_patterns: Vec::new(),
        })
    }
}

#[derive(Debug)]
pub struct VisualResourceGenerator {}

impl VisualResourceGenerator {
    pub const fn new() -> Self {
        Self {}
    }

    pub fn generate_visuals(
        &self,
        _basic: &BasicResourceAnalysis,
        _ml_predictions: &Option<MLPredictions>,
    ) -> Result<HashMap<String, VisualRepresentation>, QuantRS2Error> {
        let mut visuals = HashMap::new();

        visuals.insert(
            "resource_chart".to_string(),
            VisualRepresentation {
                format: "ASCII".to_string(),
                content: "Resource Usage Chart\n[████████████████████]".to_string(),
            },
        );

        Ok(visuals)
    }
}

#[derive(Debug)]
pub struct HardwareRecommender {}

impl HardwareRecommender {
    pub const fn new() -> Self {
        Self {}
    }

    pub fn recommend_hardware(
        &self,
        _circuit: &[QuantumGate],
        basic: &BasicResourceAnalysis,
        _options: &EstimationOptions,
    ) -> Result<Vec<HardwareRecommendation>, QuantRS2Error> {
        Ok(vec![HardwareRecommendation {
            hardware_platform: HardwarePlatform::Superconducting,
            suitability_score: 0.85,
            pros: vec!["Fast gates".to_string(), "High connectivity".to_string()],
            cons: vec!["Short coherence".to_string()],
            specific_optimizations: vec!["Use native gates".to_string()],
        }])
    }
}

#[derive(Debug)]
pub struct ScalingPredictor {}

impl ScalingPredictor {
    pub const fn new() -> Self {
        Self {}
    }

    pub fn predict_scaling(
        &self,
        _circuit: &[QuantumGate],
        basic: &BasicResourceAnalysis,
    ) -> Result<ScalingPredictions, QuantRS2Error> {
        let mut qubit_scaling = Vec::new();

        for size in [10, 20, 50, 100] {
            qubit_scaling.push(ScalingPoint {
                problem_size: size,
                resource_value: (size as f64).powi(2),
                confidence: 0.8,
            });
        }

        Ok(ScalingPredictions {
            qubit_scaling,
            depth_scaling: Vec::new(),
            resource_scaling: Vec::new(),
            feasibility_threshold: FeasibilityThreshold {
                current_tech_limit: 50,
                near_term_limit: 100,
                fault_tolerant_limit: 1000,
            },
        })
    }
}

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

    #[test]
    fn test_enhanced_estimator_creation() {
        let estimator = EnhancedResourceEstimator::new();
        assert!(estimator.config.enable_ml_prediction);
        assert!(estimator.config.enable_cost_analysis);
    }

    #[test]
    fn test_basic_resource_analysis() {
        let estimator = EnhancedResourceEstimator::new();
        let circuit = vec![
            QuantumGate::new(GateType::H, vec![0], None),
            QuantumGate::new(GateType::CNOT, vec![0, 1], None),
            QuantumGate::new(GateType::T, vec![0], None),
        ];

        let analysis = estimator
            .perform_basic_analysis(&circuit, 2)
            .expect("Failed to perform basic analysis in test_basic_resource_analysis");
        assert_eq!(analysis.gate_statistics.total_gates, 3);
        assert_eq!(analysis.num_qubits, 2);
    }

    #[test]
    fn test_gate_pattern_detection() {
        let estimator = EnhancedResourceEstimator::new();
        let circuit = vec![
            QuantumGate::new(GateType::H, vec![0], None),
            QuantumGate::new(GateType::CNOT, vec![0, 1], None),
            QuantumGate::new(GateType::CNOT, vec![1, 2], None),
            QuantumGate::new(GateType::CNOT, vec![2, 3], None),
        ];

        let patterns = estimator
            .detect_gate_patterns(&circuit)
            .expect("Failed to detect gate patterns in test_gate_pattern_detection");
        assert!(!patterns.is_empty());
    }

    #[test]
    fn test_ml_predictions() {
        let predictor = MLResourcePredictor::new();
        let basic = BasicResourceAnalysis {
            gate_statistics: GateStatistics {
                total_gates: 10,
                gate_counts: HashMap::new(),
                gate_depths: HashMap::new(),
                gate_patterns: Vec::new(),
                clifford_count: 8,
                non_clifford_count: 2,
                two_qubit_count: 3,
                multi_qubit_count: 0,
            },
            circuit_topology: CircuitTopology {
                num_qubits: 4,
                connectivity_matrix: vec![vec![0; 4]; 4],
                connectivity_density: 0.3,
                max_connections: 2,
                critical_qubits: vec![],
                topology_type: TopologyType::Regular,
            },
            resource_requirements: ResourceRequirements {
                logical_qubits: 4,
                physical_qubits: 100,
                code_distance: 5,
                execution_time: 1e-3,
                memory_requirements: MemoryRequirements {
                    state_vector_memory: 256,
                    gate_storage_memory: 640,
                    workspace_memory: 128,
                    total_memory: 1024,
                    memory_bandwidth: 10.0,
                },
                magic_states: 20,
                error_budget: ErrorBudget {
                    total_budget: 1e-6,
                    gate_errors: 4e-7,
                    measurement_errors: 2e-7,
                    idle_errors: 2e-7,
                    crosstalk_errors: 1e-7,
                    readout_errors: 1e-7,
                },
            },
            complexity_metrics: ComplexityMetrics {
                t_complexity: 2,
                t_depth: 2,
                circuit_volume: 40,
                communication_complexity: 1.2,
                entanglement_complexity: 0.3,
                algorithmic_complexity: "Low".to_string(),
            },
            num_qubits: 4,
            circuit_size: 10,
        };

        let predictions = predictor
            .predict_resources(&[], &basic)
            .expect("Failed to predict resources in test_ml_predictions");
        assert!(predictions.predicted_success_rate > 0.9);
    }

    #[test]
    fn test_cost_analysis() {
        let analyzer = CostAnalyzer::new();
        let basic = BasicResourceAnalysis {
            gate_statistics: GateStatistics {
                total_gates: 10,
                gate_counts: HashMap::new(),
                gate_depths: HashMap::new(),
                gate_patterns: Vec::new(),
                clifford_count: 8,
                non_clifford_count: 2,
                two_qubit_count: 3,
                multi_qubit_count: 0,
            },
            circuit_topology: CircuitTopology {
                num_qubits: 4,
                connectivity_matrix: vec![vec![0; 4]; 4],
                connectivity_density: 0.3,
                max_connections: 2,
                critical_qubits: vec![],
                topology_type: TopologyType::Regular,
            },
            resource_requirements: ResourceRequirements {
                logical_qubits: 4,
                physical_qubits: 100,
                code_distance: 5,
                execution_time: 1e-3,
                memory_requirements: MemoryRequirements {
                    state_vector_memory: 256,
                    gate_storage_memory: 640,
                    workspace_memory: 128,
                    total_memory: 1024,
                    memory_bandwidth: 10.0,
                },
                magic_states: 20,
                error_budget: ErrorBudget {
                    total_budget: 1e-6,
                    gate_errors: 4e-7,
                    measurement_errors: 2e-7,
                    idle_errors: 2e-7,
                    crosstalk_errors: 1e-7,
                    readout_errors: 1e-7,
                },
            },
            complexity_metrics: ComplexityMetrics {
                t_complexity: 2,
                t_depth: 2,
                circuit_volume: 40,
                communication_complexity: 1.2,
                entanglement_complexity: 0.3,
                algorithmic_complexity: "Low".to_string(),
            },
            num_qubits: 4,
            circuit_size: 10,
        };

        let options = EstimationOptions {
            target_platforms: vec![CloudPlatform::IBMQ],
            optimization_level: OptimizationLevel::Basic,
            include_alternatives: false,
            max_alternatives: 3,
        };

        let costs = analyzer
            .analyze_costs(&[], &basic, &options)
            .expect("Failed to analyze costs in test_cost_analysis");
        assert!(costs.total_estimated_cost > 0.0);
    }

    #[test]
    fn test_resource_scores() {
        let estimator = EnhancedResourceEstimator::new();
        let basic = BasicResourceAnalysis {
            gate_statistics: GateStatistics {
                total_gates: 10,
                gate_counts: HashMap::new(),
                gate_depths: HashMap::new(),
                gate_patterns: Vec::new(),
                clifford_count: 8,
                non_clifford_count: 2,
                two_qubit_count: 3,
                multi_qubit_count: 0,
            },
            circuit_topology: CircuitTopology {
                num_qubits: 4,
                connectivity_matrix: vec![vec![0; 4]; 4],
                connectivity_density: 0.3,
                max_connections: 2,
                critical_qubits: vec![],
                topology_type: TopologyType::Regular,
            },
            resource_requirements: ResourceRequirements {
                logical_qubits: 4,
                physical_qubits: 100,
                code_distance: 5,
                execution_time: 1e-3,
                memory_requirements: MemoryRequirements {
                    state_vector_memory: 256,
                    gate_storage_memory: 640,
                    workspace_memory: 128,
                    total_memory: 1024,
                    memory_bandwidth: 10.0,
                },
                magic_states: 20,
                error_budget: ErrorBudget {
                    total_budget: 1e-6,
                    gate_errors: 4e-7,
                    measurement_errors: 2e-7,
                    idle_errors: 2e-7,
                    crosstalk_errors: 1e-7,
                    readout_errors: 1e-7,
                },
            },
            complexity_metrics: ComplexityMetrics {
                t_complexity: 2,
                t_depth: 2,
                circuit_volume: 40,
                communication_complexity: 1.2,
                entanglement_complexity: 0.3,
                algorithmic_complexity: "Low".to_string(),
            },
            num_qubits: 4,
            circuit_size: 10,
        };

        let scores = estimator.calculate_resource_scores(&basic, &None);
        assert!(scores.overall_score > 0.0);
        assert!(scores.overall_score <= 1.0);
    }

    #[test]
    fn test_export_report() {
        let estimator = EnhancedResourceEstimator::new();
        let estimate = EnhancedResourceEstimate {
            basic_resources: BasicResourceAnalysis {
                gate_statistics: GateStatistics {
                    total_gates: 10,
                    gate_counts: HashMap::new(),
                    gate_depths: HashMap::new(),
                    gate_patterns: Vec::new(),
                    clifford_count: 8,
                    non_clifford_count: 2,
                    two_qubit_count: 3,
                    multi_qubit_count: 0,
                },
                circuit_topology: CircuitTopology {
                    num_qubits: 4,
                    connectivity_matrix: vec![vec![0; 4]; 4],
                    connectivity_density: 0.3,
                    max_connections: 2,
                    critical_qubits: vec![],
                    topology_type: TopologyType::Regular,
                },
                resource_requirements: ResourceRequirements {
                    logical_qubits: 4,
                    physical_qubits: 100,
                    code_distance: 5,
                    execution_time: 1e-3,
                    memory_requirements: MemoryRequirements {
                        state_vector_memory: 256,
                        gate_storage_memory: 640,
                        workspace_memory: 128,
                        total_memory: 1024,
                        memory_bandwidth: 10.0,
                    },
                    magic_states: 20,
                    error_budget: ErrorBudget {
                        total_budget: 1e-6,
                        gate_errors: 4e-7,
                        measurement_errors: 2e-7,
                        idle_errors: 2e-7,
                        crosstalk_errors: 1e-7,
                        readout_errors: 1e-7,
                    },
                },
                complexity_metrics: ComplexityMetrics {
                    t_complexity: 2,
                    t_depth: 2,
                    circuit_volume: 40,
                    communication_complexity: 1.2,
                    entanglement_complexity: 0.3,
                    algorithmic_complexity: "Low".to_string(),
                },
                num_qubits: 4,
                circuit_size: 10,
            },
            ml_predictions: None,
            cost_analysis: None,
            optimization_strategies: None,
            comparative_results: None,
            hardware_recommendations: None,
            scaling_predictions: None,
            visual_representations: HashMap::new(),
            tracking_data: None,
            resource_scores: ResourceScores {
                overall_score: 0.8,
                efficiency_score: 0.85,
                scalability_score: 0.75,
                feasibility_score: 0.8,
                optimization_potential: 0.3,
                readiness_level: ReadinessLevel::Experimental,
            },
            recommendations: Vec::new(),
            estimation_time: std::time::Duration::from_millis(100),
            platform_optimizations: Vec::new(),
        };

        let json_report = estimator
            .export_report(&estimate, ReportFormat::JSON)
            .expect("Failed to export JSON report in test_export_report");
        assert!(json_report.contains("resource_scores"));

        let md_report = estimator
            .export_report(&estimate, ReportFormat::Markdown)
            .expect("Failed to export Markdown report in test_export_report");
        assert!(md_report.contains("# Enhanced Resource Estimation Report"));
    }
}