quantrs2_core/

scirs2_quantum_linter.rs

//! SciRS2-Enhanced Quantum Circuit Linter
//!
//! This module provides comprehensive linting and static analysis for quantum circuits
//! using SciRS2's advanced pattern matching, optimization detection, and code quality analysis.

use crate::error::QuantRS2Error;
use crate::gate_translation::GateType;
use std::collections::{HashMap, HashSet};

/// SciRS2-enhanced quantum gate representation for linting
#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
pub struct QuantumGate {
    gate_type: GateType,
    target_qubits: Vec<usize>,
    control_qubits: Option<Vec<usize>>,
}

impl QuantumGate {
    pub const fn new(
        gate_type: GateType,
        target_qubits: Vec<usize>,
        control_qubits: Option<Vec<usize>>,
    ) -> Self {
        Self {
            gate_type,
            target_qubits,
            control_qubits,
        }
    }

    pub const fn gate_type(&self) -> &GateType {
        &self.gate_type
    }

    pub fn target_qubits(&self) -> &[usize] {
        &self.target_qubits
    }

    pub fn control_qubits(&self) -> Option<&[usize]> {
        self.control_qubits.as_deref()
    }
}

/// Configuration for SciRS2-enhanced quantum linting
#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
pub struct LintingConfig {
    /// Enable performance optimization detection
    pub detect_performance_issues: bool,
    /// Enable gate pattern analysis
    pub analyze_gate_patterns: bool,
    /// Enable circuit structure analysis
    pub analyze_circuit_structure: bool,
    /// Enable resource usage analysis
    pub analyze_resource_usage: bool,
    /// Enable best practices checking
    pub check_best_practices: bool,
    /// Enable quantum-specific anti-patterns detection
    pub detect_quantum_antipatterns: bool,
    /// Enable SIMD optimization suggestions
    pub suggest_simd_optimizations: bool,
    /// Enable parallel execution analysis
    pub analyze_parallel_potential: bool,
    /// Enable memory efficiency analysis
    pub analyze_memory_efficiency: bool,
    /// Severity threshold for reporting
    pub severity_threshold: LintSeverity,
    /// Enable automatic fix suggestions
    pub suggest_automatic_fixes: bool,
    /// Enable SciRS2-specific optimizations
    pub enable_scirs2_optimizations: bool,
}

impl Default for LintingConfig {
    fn default() -> Self {
        Self {
            detect_performance_issues: true,
            analyze_gate_patterns: true,
            analyze_circuit_structure: true,
            analyze_resource_usage: true,
            check_best_practices: true,
            detect_quantum_antipatterns: true,
            suggest_simd_optimizations: true,
            analyze_parallel_potential: true,
            analyze_memory_efficiency: true,
            severity_threshold: LintSeverity::Info,
            suggest_automatic_fixes: true,
            enable_scirs2_optimizations: true,
        }
    }
}

/// Severity levels for lint findings
#[derive(
    Debug, Clone, PartialEq, Eq, PartialOrd, Ord, Hash, serde::Serialize, serde::Deserialize,
)]
pub enum LintSeverity {
    Info,
    Warning,
    Error,
    Critical,
}

/// SciRS2-enhanced quantum circuit linter
pub struct SciRS2QuantumLinter {
    config: LintingConfig,
    pattern_matcher: PatternMatcher,
    performance_analyzer: PerformanceAnalyzer,
    structure_analyzer: StructureAnalyzer,
    resource_analyzer: ResourceAnalyzer,
    best_practices_checker: BestPracticesChecker,
    antipattern_detector: AntipatternDetector,
    optimization_suggester: OptimizationSuggester,
    fix_generator: AutomaticFixGenerator,
    scirs2_optimizer: SciRS2Optimizer,
}

impl SciRS2QuantumLinter {
    /// Create a new SciRS2-enhanced quantum linter
    pub fn new() -> Self {
        let config = LintingConfig::default();
        Self::with_config(config)
    }

    /// Create linter with custom configuration
    pub const fn with_config(config: LintingConfig) -> Self {
        Self {
            config,
            pattern_matcher: PatternMatcher::new(),
            performance_analyzer: PerformanceAnalyzer::new(),
            structure_analyzer: StructureAnalyzer::new(),
            resource_analyzer: ResourceAnalyzer::new(),
            best_practices_checker: BestPracticesChecker::new(),
            antipattern_detector: AntipatternDetector::new(),
            optimization_suggester: OptimizationSuggester::new(),
            fix_generator: AutomaticFixGenerator::new(),
            scirs2_optimizer: SciRS2Optimizer::new(),
        }
    }

    /// Perform comprehensive circuit linting
    pub fn lint_circuit(
        &self,
        circuit: &[QuantumGate],
        num_qubits: usize,
    ) -> Result<LintingReport, QuantRS2Error> {
        let mut findings = Vec::new();

        // Performance issue detection
        if self.config.detect_performance_issues {
            let performance_issues = self
                .performance_analyzer
                .analyze_performance(circuit, num_qubits)?;
            findings.extend(performance_issues);
        }

        // Gate pattern analysis
        if self.config.analyze_gate_patterns {
            let pattern_issues = self.pattern_matcher.analyze_patterns(circuit)?;
            findings.extend(pattern_issues);
        }

        // Circuit structure analysis
        if self.config.analyze_circuit_structure {
            let structure_issues = self
                .structure_analyzer
                .analyze_structure(circuit, num_qubits)?;
            findings.extend(structure_issues);
        }

        // Resource usage analysis
        if self.config.analyze_resource_usage {
            let resource_issues = self.resource_analyzer.analyze_usage(circuit, num_qubits)?;
            findings.extend(resource_issues);
        }

        // Best practices checking
        if self.config.check_best_practices {
            let best_practice_issues = self
                .best_practices_checker
                .check_practices(circuit, num_qubits)?;
            findings.extend(best_practice_issues);
        }

        // Anti-pattern detection
        if self.config.detect_quantum_antipatterns {
            let antipattern_issues = self.antipattern_detector.detect_antipatterns(circuit)?;
            findings.extend(antipattern_issues);
        }

        // SciRS2-specific optimization suggestions
        if self.config.enable_scirs2_optimizations {
            let scirs2_suggestions = self
                .scirs2_optimizer
                .analyze_optimization_opportunities(circuit, num_qubits)?;
            findings.extend(scirs2_suggestions);
        }

        // Filter findings by severity threshold
        findings.retain(|finding| finding.severity >= self.config.severity_threshold);

        // Generate automatic fixes if enabled
        let automatic_fixes = if self.config.suggest_automatic_fixes {
            self.fix_generator.generate_fixes(&findings, circuit)?
        } else {
            Vec::new()
        };

        // Generate optimization suggestions
        let optimization_suggestions =
            if self.config.suggest_simd_optimizations || self.config.analyze_parallel_potential {
                self.optimization_suggester
                    .generate_suggestions(circuit, &findings)?
            } else {
                Vec::new()
            };

        // Calculate overall code quality score
        let quality_score = self.calculate_code_quality_score(&findings, circuit.len());

        Ok(LintingReport {
            total_findings: findings.len(),
            findings_by_severity: self.categorize_findings_by_severity(&findings),
            findings: findings.clone(),
            automatic_fixes,
            optimization_suggestions,
            code_quality_score: quality_score,
            scirs2_enhancement_opportunities: self
                .identify_scirs2_enhancement_opportunities(circuit)?,
            recommendations: self.generate_overall_recommendations(&findings, circuit)?,
        })
    }

    /// Lint a specific gate pattern
    pub fn lint_gate_pattern(
        &self,
        pattern: &[QuantumGate],
    ) -> Result<Vec<LintFinding>, QuantRS2Error> {
        let mut findings = Vec::new();

        // Check for common inefficient patterns
        findings.extend(self.check_inefficient_patterns(pattern)?);

        // Check for gate optimization opportunities
        findings.extend(self.check_gate_optimization_opportunities(pattern)?);

        // Check for SIMD optimization potential
        if self.config.suggest_simd_optimizations {
            findings.extend(self.check_simd_optimization_potential(pattern)?);
        }

        Ok(findings)
    }

    /// Check for inefficient gate patterns
    fn check_inefficient_patterns(
        &self,
        pattern: &[QuantumGate],
    ) -> Result<Vec<LintFinding>, QuantRS2Error> {
        let mut findings = Vec::new();

        // Check for redundant gate sequences (e.g., X-X, H-H)
        for window in pattern.windows(2) {
            if window.len() == 2 {
                let gate1 = &window[0];
                let gate2 = &window[1];

                if self.are_gates_canceling(gate1, gate2) {
                    findings.push(LintFinding {
                        finding_type: LintFindingType::PerformanceIssue,
                        severity: LintSeverity::Warning,
                        message: format!(
                            "Redundant gate sequence: {:?} followed by {:?}",
                            gate1.gate_type(),
                            gate2.gate_type()
                        ),
                        location: LintLocation::GateSequence(vec![0, 1]), // Simplified location
                        suggestion: Some("Remove redundant gates or combine them".to_string()),
                        automatic_fix_available: true,
                        scirs2_optimization_potential: true,
                    });
                }
            }
        }

        // Check for inefficient rotations (multiple small rotations that could be combined)
        findings.extend(self.check_rotation_inefficiencies(pattern)?);

        // Check for suboptimal gate ordering
        findings.extend(self.check_gate_ordering(pattern)?);

        Ok(findings)
    }

    /// Check if two gates cancel each other out
    fn are_gates_canceling(&self, gate1: &QuantumGate, gate2: &QuantumGate) -> bool {
        if gate1.target_qubits() != gate2.target_qubits() {
            return false;
        }

        match (gate1.gate_type(), gate2.gate_type()) {
            (GateType::X, GateType::X)
            | (GateType::Y, GateType::Y)
            | (GateType::Z, GateType::Z)
            | (GateType::H, GateType::H) => true,
            (GateType::CNOT, GateType::CNOT) => gate1.control_qubits() == gate2.control_qubits(),
            _ => false,
        }
    }

    /// Check for rotation inefficiencies
    fn check_rotation_inefficiencies(
        &self,
        pattern: &[QuantumGate],
    ) -> Result<Vec<LintFinding>, QuantRS2Error> {
        let mut findings = Vec::new();

        // Look for consecutive rotation gates that could be combined
        for window in pattern.windows(3) {
            if window.len() == 3 {
                let rotations: Vec<_> = window
                    .iter()
                    .filter(|gate| {
                        matches!(
                            gate.gate_type(),
                            GateType::Rx(_) | GateType::Ry(_) | GateType::Rz(_)
                        )
                    })
                    .collect();

                if rotations.len() >= 2 && self.are_rotations_combinable(&rotations) {
                    findings.push(LintFinding {
                        finding_type: LintFindingType::OptimizationOpportunity,
                        severity: LintSeverity::Info,
                        message: "Multiple rotation gates can be combined into a single rotation"
                            .to_string(),
                        location: LintLocation::GateSequence((0..window.len()).collect()),
                        suggestion: Some(
                            "Combine consecutive rotation gates using SciRS2 gate fusion"
                                .to_string(),
                        ),
                        automatic_fix_available: true,
                        scirs2_optimization_potential: true,
                    });
                }
            }
        }

        Ok(findings)
    }

    /// Check if rotation gates can be combined
    fn are_rotations_combinable(&self, rotations: &[&QuantumGate]) -> bool {
        if rotations.len() < 2 {
            return false;
        }

        // Check if all rotations are on the same qubit and same axis
        let first_target = rotations[0].target_qubits();
        let first_type = rotations[0].gate_type();

        rotations.iter().all(|gate| {
            gate.target_qubits() == first_target
                && std::mem::discriminant(gate.gate_type()) == std::mem::discriminant(first_type)
        })
    }

    /// Check gate ordering for optimization opportunities
    fn check_gate_ordering(
        &self,
        pattern: &[QuantumGate],
    ) -> Result<Vec<LintFinding>, QuantRS2Error> {
        let mut findings = Vec::new();

        // Check if commuting gates can be reordered for better parallelization
        for i in 0..pattern.len() {
            for j in i + 1..pattern.len() {
                if self.can_gates_commute(&pattern[i], &pattern[j])
                    && self.would_reordering_improve_parallelism(&pattern[i], &pattern[j])
                {
                    findings.push(LintFinding {
                        finding_type: LintFindingType::OptimizationOpportunity,
                        severity: LintSeverity::Info,
                        message: "Gate reordering could improve parallelization".to_string(),
                        location: LintLocation::GateSequence(vec![i, j]),
                        suggestion: Some(
                            "Reorder commuting gates to enable parallel execution".to_string(),
                        ),
                        automatic_fix_available: false,
                        scirs2_optimization_potential: true,
                    });
                }
            }
        }

        Ok(findings)
    }

    /// Check if two gates can commute
    fn can_gates_commute(&self, gate1: &QuantumGate, gate2: &QuantumGate) -> bool {
        // Simple check: gates commute if they operate on different qubits
        let qubits1: HashSet<_> = gate1
            .target_qubits()
            .iter()
            .chain(gate1.control_qubits().unwrap_or(&[]).iter())
            .collect();
        let qubits2: HashSet<_> = gate2
            .target_qubits()
            .iter()
            .chain(gate2.control_qubits().unwrap_or(&[]).iter())
            .collect();

        qubits1.is_disjoint(&qubits2)
    }

    /// Check if reordering would improve parallelism
    const fn would_reordering_improve_parallelism(
        &self,
        _gate1: &QuantumGate,
        _gate2: &QuantumGate,
    ) -> bool {
        // Simplified heuristic: assume reordering independent gates helps parallelism
        true
    }

    /// Check gate optimization opportunities
    fn check_gate_optimization_opportunities(
        &self,
        pattern: &[QuantumGate],
    ) -> Result<Vec<LintFinding>, QuantRS2Error> {
        let mut findings = Vec::new();

        // Check for gates that could benefit from SciRS2 optimizations
        for (i, gate) in pattern.iter().enumerate() {
            match gate.gate_type() {
                GateType::CNOT => {
                    findings.push(LintFinding {
                        finding_type: LintFindingType::SciRS2Optimization,
                        severity: LintSeverity::Info,
                        message: "CNOT gate can benefit from SciRS2 SIMD optimization".to_string(),
                        location: LintLocation::Gate(i),
                        suggestion: Some(
                            "Enable SciRS2 SIMD optimization for CNOT gates".to_string(),
                        ),
                        automatic_fix_available: false,
                        scirs2_optimization_potential: true,
                    });
                }
                GateType::H => {
                    findings.push(LintFinding {
                        finding_type: LintFindingType::SciRS2Optimization,
                        severity: LintSeverity::Info,
                        message: "Hadamard gate can benefit from SciRS2 vectorization".to_string(),
                        location: LintLocation::Gate(i),
                        suggestion: Some(
                            "Use SciRS2 vectorized Hadamard implementation".to_string(),
                        ),
                        automatic_fix_available: false,
                        scirs2_optimization_potential: true,
                    });
                }
                _ => {}
            }
        }

        Ok(findings)
    }

    /// Check SIMD optimization potential
    fn check_simd_optimization_potential(
        &self,
        pattern: &[QuantumGate],
    ) -> Result<Vec<LintFinding>, QuantRS2Error> {
        let mut findings = Vec::new();

        // Look for patterns that could benefit from SIMD operations
        let vectorizable_gates = pattern
            .iter()
            .enumerate()
            .filter(|(_, gate)| self.is_gate_vectorizable(gate))
            .collect::<Vec<_>>();

        if vectorizable_gates.len() >= 2 {
            findings.push(LintFinding {
                finding_type: LintFindingType::SciRS2Optimization,
                severity: LintSeverity::Info,
                message: format!(
                    "Found {} gates that could benefit from SIMD vectorization",
                    vectorizable_gates.len()
                ),
                location: LintLocation::GateSequence(
                    vectorizable_gates.iter().map(|(i, _)| *i).collect(),
                ),
                suggestion: Some(
                    "Apply SciRS2 SIMD vectorization to improve performance".to_string(),
                ),
                automatic_fix_available: false,
                scirs2_optimization_potential: true,
            });
        }

        Ok(findings)
    }

    /// Check if a gate is vectorizable
    const fn is_gate_vectorizable(&self, gate: &QuantumGate) -> bool {
        matches!(
            gate.gate_type(),
            GateType::X
                | GateType::Y
                | GateType::Z
                | GateType::H
                | GateType::Rx(_)
                | GateType::Ry(_)
                | GateType::Rz(_)
                | GateType::Phase(_)
        )
    }

    /// Categorize findings by severity
    fn categorize_findings_by_severity(
        &self,
        findings: &[LintFinding],
    ) -> HashMap<LintSeverity, usize> {
        let mut counts = HashMap::new();
        for finding in findings {
            *counts.entry(finding.severity.clone()).or_insert(0) += 1;
        }
        counts
    }

    /// Calculate code quality score
    fn calculate_code_quality_score(&self, findings: &[LintFinding], circuit_size: usize) -> f64 {
        if circuit_size == 0 {
            return 1.0;
        }

        let error_weight = 0.4;
        let warning_weight = 0.2;
        let info_weight = 0.1;

        let mut penalty = 0.0;
        for finding in findings {
            let weight = match finding.severity {
                LintSeverity::Critical => 0.8,
                LintSeverity::Error => error_weight,
                LintSeverity::Warning => warning_weight,
                LintSeverity::Info => info_weight,
            };
            penalty += weight;
        }

        let normalized_penalty = penalty / circuit_size as f64;
        (1.0 - normalized_penalty).clamp(0.0, 1.0)
    }

    /// Identify SciRS2 enhancement opportunities
    fn identify_scirs2_enhancement_opportunities(
        &self,
        circuit: &[QuantumGate],
    ) -> Result<Vec<SciRS2Enhancement>, QuantRS2Error> {
        let mut enhancements = Vec::new();

        // SIMD opportunities
        let simd_gates = circuit
            .iter()
            .filter(|gate| self.is_gate_vectorizable(gate))
            .count();

        if simd_gates > 0 {
            enhancements.push(SciRS2Enhancement {
                enhancement_type: EnhancementType::SimdVectorization,
                description: format!("Enable SIMD vectorization for {simd_gates} gates"),
                expected_speedup: 1.5 + (simd_gates as f64 * 0.1).min(2.0),
                implementation_effort: ImplementationEffort::Low,
            });
        }

        // Memory optimization opportunities
        if circuit.len() > 100 {
            enhancements.push(SciRS2Enhancement {
                enhancement_type: EnhancementType::MemoryOptimization,
                description: "Use SciRS2 memory-efficient state vector management".to_string(),
                expected_speedup: 1.3,
                implementation_effort: ImplementationEffort::Medium,
            });
        }

        // Parallel execution opportunities
        let parallel_gates = circuit
            .iter()
            .enumerate()
            .filter(|(i, gate)| {
                circuit
                    .iter()
                    .skip(i + 1)
                    .any(|other_gate| self.can_gates_commute(gate, other_gate))
            })
            .count();

        if parallel_gates > 5 {
            enhancements.push(SciRS2Enhancement {
                enhancement_type: EnhancementType::ParallelExecution,
                description: format!(
                    "Enable parallel execution for {parallel_gates} independent gates"
                ),
                expected_speedup: 1.8,
                implementation_effort: ImplementationEffort::Medium,
            });
        }

        Ok(enhancements)
    }

    /// Generate overall recommendations
    fn generate_overall_recommendations(
        &self,
        findings: &[LintFinding],
        circuit: &[QuantumGate],
    ) -> Result<Vec<String>, QuantRS2Error> {
        let mut recommendations = Vec::new();

        let critical_count = findings
            .iter()
            .filter(|f| f.severity == LintSeverity::Critical)
            .count();
        let error_count = findings
            .iter()
            .filter(|f| f.severity == LintSeverity::Error)
            .count();
        let warning_count = findings
            .iter()
            .filter(|f| f.severity == LintSeverity::Warning)
            .count();

        if critical_count > 0 {
            recommendations.push(format!(
                "Address {critical_count} critical issues immediately"
            ));
        }

        if error_count > 0 {
            recommendations.push(format!(
                "Fix {error_count} error-level issues to improve circuit correctness"
            ));
        }

        if warning_count > 0 {
            recommendations.push(format!(
                "Consider addressing {warning_count} warnings to improve performance"
            ));
        }

        // SciRS2-specific recommendations
        let scirs2_opportunities = findings
            .iter()
            .filter(|f| f.scirs2_optimization_potential)
            .count();

        if scirs2_opportunities > 0 {
            recommendations.push(format!(
                "Implement {scirs2_opportunities} SciRS2 optimizations for enhanced performance"
            ));
        }

        if circuit.len() > 50 {
            recommendations
                .push("Consider circuit compression techniques for large circuits".to_string());
        }

        if recommendations.is_empty() {
            recommendations.push("Circuit looks good! Consider enabling SciRS2 optimizations for enhanced performance".to_string());
        }

        Ok(recommendations)
    }
}

/// Data structures for linting results

#[derive(Debug, Clone)]
pub struct LintingReport {
    pub total_findings: usize,
    pub findings_by_severity: HashMap<LintSeverity, usize>,
    pub findings: Vec<LintFinding>,
    pub automatic_fixes: Vec<AutomaticFix>,
    pub optimization_suggestions: Vec<OptimizationSuggestion>,
    pub code_quality_score: f64,
    pub scirs2_enhancement_opportunities: Vec<SciRS2Enhancement>,
    pub recommendations: Vec<String>,
}

#[derive(Debug, Clone)]
pub struct LintFinding {
    pub finding_type: LintFindingType,
    pub severity: LintSeverity,
    pub message: String,
    pub location: LintLocation,
    pub suggestion: Option<String>,
    pub automatic_fix_available: bool,
    pub scirs2_optimization_potential: bool,
}

#[derive(Debug, Clone, PartialEq, Eq)]
pub enum LintFindingType {
    PerformanceIssue,
    BestPracticeViolation,
    QuantumAntipattern,
    OptimizationOpportunity,
    SciRS2Optimization,
    ResourceWaste,
    StructuralIssue,
}

#[derive(Debug, Clone)]
pub enum LintLocation {
    Gate(usize),
    GateSequence(Vec<usize>),
    Qubit(usize),
    Circuit,
}

#[derive(Debug, Clone)]
pub struct AutomaticFix {
    pub fix_type: FixType,
    pub description: String,
    pub original_gates: Vec<usize>,
    pub replacement_gates: Vec<QuantumGate>,
    pub confidence: f64,
}

#[derive(Debug, Clone)]
pub enum FixType {
    RemoveRedundantGates,
    CombineRotations,
    ReorderGates,
    ReplaceWithOptimized,
    ApplySciRS2Optimization,
}

#[derive(Debug, Clone)]
pub struct OptimizationSuggestion {
    pub suggestion_type: OptimizationType,
    pub description: String,
    pub expected_improvement: String,
    pub implementation_complexity: ImplementationComplexity,
}

#[derive(Debug, Clone)]
pub enum OptimizationType {
    SimdVectorization,
    ParallelExecution,
    MemoryOptimization,
    GateFusion,
    CircuitCompression,
}

#[derive(Debug, Clone)]
pub enum ImplementationComplexity {
    Low,
    Medium,
    High,
}

#[derive(Debug, Clone)]
pub struct SciRS2Enhancement {
    pub enhancement_type: EnhancementType,
    pub description: String,
    pub expected_speedup: f64,
    pub implementation_effort: ImplementationEffort,
}

#[derive(Debug, Clone)]
pub enum EnhancementType {
    SimdVectorization,
    MemoryOptimization,
    ParallelExecution,
    NumericalStability,
    CacheOptimization,
}

#[derive(Debug, Clone)]
pub enum ImplementationEffort {
    Low,
    Medium,
    High,
}

// Placeholder implementations for supporting analysis modules

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

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

    pub const fn analyze_patterns(
        &self,
        _circuit: &[QuantumGate],
    ) -> Result<Vec<LintFinding>, QuantRS2Error> {
        Ok(vec![])
    }
}

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

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

    pub fn analyze_performance(
        &self,
        circuit: &[QuantumGate],
        _num_qubits: usize,
    ) -> Result<Vec<LintFinding>, QuantRS2Error> {
        let mut findings = Vec::new();

        // Example: detect long circuits that might benefit from optimization
        if circuit.len() > 100 {
            findings.push(LintFinding {
                finding_type: LintFindingType::PerformanceIssue,
                severity: LintSeverity::Warning,
                message: "Large circuit detected - consider optimization techniques".to_string(),
                location: LintLocation::Circuit,
                suggestion: Some("Apply circuit compression or gate fusion techniques".to_string()),
                automatic_fix_available: false,
                scirs2_optimization_potential: true,
            });
        }

        Ok(findings)
    }
}

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

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

    pub const fn analyze_structure(
        &self,
        _circuit: &[QuantumGate],
        _num_qubits: usize,
    ) -> Result<Vec<LintFinding>, QuantRS2Error> {
        Ok(vec![])
    }
}

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

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

    pub const fn analyze_usage(
        &self,
        _circuit: &[QuantumGate],
        _num_qubits: usize,
    ) -> Result<Vec<LintFinding>, QuantRS2Error> {
        Ok(vec![])
    }
}

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

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

    pub const fn check_practices(
        &self,
        _circuit: &[QuantumGate],
        _num_qubits: usize,
    ) -> Result<Vec<LintFinding>, QuantRS2Error> {
        Ok(vec![])
    }
}

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

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

    pub const fn detect_antipatterns(
        &self,
        _circuit: &[QuantumGate],
    ) -> Result<Vec<LintFinding>, QuantRS2Error> {
        Ok(vec![])
    }
}

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

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

    pub const fn generate_suggestions(
        &self,
        _circuit: &[QuantumGate],
        _findings: &[LintFinding],
    ) -> Result<Vec<OptimizationSuggestion>, QuantRS2Error> {
        Ok(vec![])
    }
}

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

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

    pub const fn generate_fixes(
        &self,
        _findings: &[LintFinding],
        _circuit: &[QuantumGate],
    ) -> Result<Vec<AutomaticFix>, QuantRS2Error> {
        Ok(vec![])
    }
}

#[derive(Debug)]
pub struct SciRS2Optimizer {}

impl SciRS2Optimizer {
    pub const fn new() -> Self {
        Self {}
    }

    pub fn analyze_optimization_opportunities(
        &self,
        circuit: &[QuantumGate],
        _num_qubits: usize,
    ) -> Result<Vec<LintFinding>, QuantRS2Error> {
        let mut findings = Vec::new();

        // Example: detect SIMD optimization opportunities
        let vectorizable_count = circuit
            .iter()
            .filter(|gate| {
                matches!(
                    gate.gate_type(),
                    GateType::X | GateType::Y | GateType::Z | GateType::H
                )
            })
            .count();

        if vectorizable_count > 5 {
            findings.push(LintFinding {
                finding_type: LintFindingType::SciRS2Optimization,
                severity: LintSeverity::Info,
                message: format!(
                    "Found {vectorizable_count} gates that could benefit from SciRS2 SIMD optimization"
                ),
                location: LintLocation::Circuit,
                suggestion: Some(
                    "Enable SciRS2 SIMD vectorization for Pauli and Hadamard gates".to_string(),
                ),
                automatic_fix_available: false,
                scirs2_optimization_potential: true,
            });
        }

        Ok(findings)
    }
}

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

    #[test]
    fn test_linter_creation() {
        let linter = SciRS2QuantumLinter::new();
        assert!(linter.config.detect_performance_issues);
        assert!(linter.config.analyze_gate_patterns);
    }

    #[test]
    fn test_circuit_linting() {
        let linter = SciRS2QuantumLinter::new();
        let circuit = vec![
            QuantumGate::new(GateType::H, vec![0], None),
            QuantumGate::new(GateType::CNOT, vec![0, 1], None),
        ];

        let report = linter
            .lint_circuit(&circuit, 2)
            .expect("Failed to lint circuit");
        assert!(report.code_quality_score > 0.0);
        assert!(report.code_quality_score <= 1.0);
    }

    #[test]
    fn test_redundant_gate_detection() {
        let linter = SciRS2QuantumLinter::new();
        let pattern = vec![
            QuantumGate::new(GateType::X, vec![0], None),
            QuantumGate::new(GateType::X, vec![0], None), // Redundant
        ];

        let findings = linter
            .lint_gate_pattern(&pattern)
            .expect("Failed to lint gate pattern");
        assert!(!findings.is_empty());
        assert!(findings
            .iter()
            .any(|f| f.finding_type == LintFindingType::PerformanceIssue));
    }

    #[test]
    fn test_gate_cancellation_detection() {
        let linter = SciRS2QuantumLinter::new();
        let gate1 = QuantumGate::new(GateType::H, vec![0], None);
        let gate2 = QuantumGate::new(GateType::H, vec![0], None);

        assert!(linter.are_gates_canceling(&gate1, &gate2));
    }

    #[test]
    fn test_gate_commutativity() {
        let linter = SciRS2QuantumLinter::new();
        let gate1 = QuantumGate::new(GateType::X, vec![0], None);
        let gate2 = QuantumGate::new(GateType::Y, vec![1], None);

        assert!(linter.can_gates_commute(&gate1, &gate2)); // Different qubits
    }

    #[test]
    fn test_vectorizable_gate_detection() {
        let linter = SciRS2QuantumLinter::new();
        let h_gate = QuantumGate::new(GateType::H, vec![0], None);
        let cnot_gate = QuantumGate::new(GateType::CNOT, vec![0, 1], None);

        assert!(linter.is_gate_vectorizable(&h_gate));
        assert!(!linter.is_gate_vectorizable(&cnot_gate));
    }

    #[test]
    fn test_rotation_combination_detection() {
        let linter = SciRS2QuantumLinter::new();
        let gate1 = QuantumGate::new(GateType::Rx("0.1".to_string()), vec![0], None);
        let gate2 = QuantumGate::new(GateType::Rx("0.2".to_string()), vec![0], None);
        let rotations = vec![&gate1, &gate2];

        assert!(linter.are_rotations_combinable(&rotations));
    }

    #[test]
    fn test_code_quality_scoring() {
        let linter = SciRS2QuantumLinter::new();

        // Empty findings should give perfect score
        let empty_findings = vec![];
        let score = linter.calculate_code_quality_score(&empty_findings, 10);
        assert_eq!(score, 1.0);

        // Some findings should reduce score
        let findings = vec![LintFinding {
            finding_type: LintFindingType::PerformanceIssue,
            severity: LintSeverity::Error,
            message: "Test finding".to_string(),
            location: LintLocation::Gate(0),
            suggestion: None,
            automatic_fix_available: false,
            scirs2_optimization_potential: false,
        }];
        let score_with_findings = linter.calculate_code_quality_score(&findings, 10);
        assert!(score_with_findings < 1.0);
    }

    #[test]
    fn test_scirs2_enhancement_identification() {
        let linter = SciRS2QuantumLinter::new();
        let circuit = vec![
            QuantumGate::new(GateType::H, vec![0], None),
            QuantumGate::new(GateType::X, vec![1], None),
            QuantumGate::new(GateType::Y, vec![2], None),
        ];

        let enhancements = linter
            .identify_scirs2_enhancement_opportunities(&circuit)
            .expect("Failed to identify SciRS2 enhancement opportunities");
        assert!(!enhancements.is_empty());
        assert!(enhancements
            .iter()
            .any(|e| matches!(e.enhancement_type, EnhancementType::SimdVectorization)));
    }
}