quantrs2_core/

scirs2_auto_optimizer.rs

//! Automatic Backend Optimization System with SciRS2 Intelligence
//!
//! This module provides intelligent automatic backend selection and optimization
//! based on problem characteristics, available hardware, and performance requirements.
//! Uses SciRS2's analysis capabilities to make optimal backend choices.

use crate::error::{QuantRS2Error, QuantRS2Result};
use crate::gate_translation::GateType;
use crate::parallel_ops_stubs::*;
use crate::platform::PlatformCapabilities;
use scirs2_core::ndarray::{Array1, Array2};
use scirs2_core::Complex64;
use serde::{Deserialize, Serialize};
use std::collections::{BTreeMap, HashMap, HashSet};
use std::sync::Arc;
use std::time::Duration;

/// Automatic optimizer for backend selection and configuration
pub struct AutoOptimizer {
    /// Platform capabilities detector
    platform_caps: Arc<PlatformCapabilities>,
    /// Configuration for optimization decisions
    config: AutoOptimizerConfig,
    /// Problem analysis cache
    analysis_cache: HashMap<String, ProblemAnalysis>,
    /// Backend performance profiles
    backend_profiles: HashMap<BackendType, PerformanceProfile>,
    /// Resource monitor
    resource_monitor: ResourceMonitor,
}

/// Configuration for the AutoOptimizer
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AutoOptimizerConfig {
    /// Enable automatic backend switching
    pub enable_auto_switching: bool,
    /// Performance vs accuracy trade-off (0.0 = max accuracy, 1.0 = max performance)
    pub performance_bias: f64,
    /// Maximum memory usage allowed (in GB)
    pub max_memory_gb: f64,
    /// Target execution time (None = no constraint)
    pub target_time: Option<Duration>,
    /// Enable GPU acceleration when available
    pub enable_gpu: bool,
    /// Enable distributed computation
    pub enable_distributed: bool,
    /// Minimum problem size for GPU acceleration
    pub gpu_threshold_qubits: usize,
    /// Minimum problem size for distributed computation
    pub distributed_threshold_qubits: usize,
    /// Cache analysis results
    pub enable_caching: bool,
    /// Resource monitoring interval
    pub monitoring_interval: Duration,
}

impl Default for AutoOptimizerConfig {
    fn default() -> Self {
        Self {
            enable_auto_switching: true,
            performance_bias: 0.5,
            max_memory_gb: 8.0,
            target_time: Some(Duration::from_secs(300)), // 5 minutes
            enable_gpu: true,
            enable_distributed: false,
            gpu_threshold_qubits: 12,
            distributed_threshold_qubits: 20,
            enable_caching: true,
            monitoring_interval: Duration::from_millis(100),
        }
    }
}

/// Supported backend types
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum BackendType {
    /// Single-threaded CPU backend
    CPUSingle,
    /// Multi-threaded CPU backend
    CPUParallel,
    /// GPU-accelerated backend
    GPU,
    /// Distributed computing backend
    Distributed,
    /// Hybrid CPU+GPU backend
    Hybrid,
    /// Quantum hardware backend
    Hardware,
    /// Simulator with noise models
    NoisySimulator,
    /// High-precision simulator
    HighPrecision,
}

/// Problem characteristics analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProblemAnalysis {
    /// Number of qubits
    pub num_qubits: usize,
    /// Circuit depth
    pub circuit_depth: usize,
    /// Gate composition analysis
    pub gate_composition: GateComposition,
    /// Entanglement analysis
    pub entanglement_analysis: EntanglementAnalysis,
    /// Memory requirements estimate
    pub memory_estimate_gb: f64,
    /// Computational complexity estimate
    pub complexity_estimate: ComplexityEstimate,
    /// Problem type classification
    pub problem_type: ProblemType,
    /// Parallelization potential
    pub parallelization_potential: ParallelizationPotential,
    /// Resource requirements
    pub resource_requirements: ResourceRequirements,
}

/// Gate composition analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GateComposition {
    /// Total number of gates
    pub total_gates: usize,
    /// Single-qubit gates count
    pub single_qubit_gates: usize,
    /// Two-qubit gates count
    pub two_qubit_gates: usize,
    /// Multi-qubit gates count
    pub multi_qubit_gates: usize,
    /// Gate type distribution
    pub gate_types: HashMap<String, usize>,
    /// Parameterized gates count
    pub parameterized_gates: usize,
    /// SIMD-friendly gates percentage
    pub simd_friendly_percentage: f64,
}

/// Entanglement analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EntanglementAnalysis {
    /// Maximum entanglement depth
    pub max_entanglement_depth: usize,
    /// Entanglement graph connectivity
    pub connectivity_ratio: f64,
    /// Separable subsystem count
    pub separable_subsystems: usize,
    /// Entanglement entropy estimate
    pub entanglement_entropy: f64,
}

/// Computational complexity estimate
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComplexityEstimate {
    /// Time complexity class
    pub time_complexity: ComplexityClass,
    /// Space complexity class
    pub space_complexity: ComplexityClass,
    /// Estimated operations count
    pub operation_count: u64,
    /// Memory access pattern
    pub memory_pattern: MemoryPattern,
    /// Parallelization factor
    pub parallelization_factor: f64,
}

/// Complexity classes
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum ComplexityClass {
    Constant,
    Logarithmic,
    Linear,
    Quadratic,
    Exponential,
    DoubleExponential,
}

/// Memory access patterns
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum MemoryPattern {
    Sequential,
    Random,
    Strided,
    Blocked,
    Hierarchical,
}

/// Problem type classifications
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum ProblemType {
    /// Quantum circuit simulation
    Simulation,
    /// Variational quantum eigensolver
    VQE,
    /// Quantum approximate optimization algorithm
    QAOA,
    /// Quantum machine learning
    QML,
    /// Quantum error correction
    QEC,
    /// Quantum Fourier transform
    QFT,
    /// Amplitude estimation
    AmplitudeEstimation,
    /// Quantum walk
    QuantumWalk,
    /// Custom algorithm
    Custom,
}

/// Parallelization potential analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ParallelizationPotential {
    /// Gate-level parallelism potential
    pub gate_parallelism: f64,
    /// State-level parallelism potential
    pub state_parallelism: f64,
    /// Batch processing potential
    pub batch_potential: f64,
    /// SIMD potential
    pub simd_potential: f64,
    /// GPU acceleration potential
    pub gpu_potential: f64,
    /// Distributed computing potential
    pub distributed_potential: f64,
}

/// Resource requirements
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceRequirements {
    /// Memory requirements (GB)
    pub memory_gb: f64,
    /// CPU cores required
    pub cpu_cores: usize,
    /// GPU memory required (GB)
    pub gpu_memory_gb: f64,
    /// Network bandwidth required (MB/s)
    pub network_bandwidth_mbps: f64,
    /// Storage requirements (GB)
    pub storage_gb: f64,
    /// Estimated execution time
    pub estimated_time: Duration,
}

/// Backend performance profile
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceProfile {
    /// Backend type
    pub backend_type: BackendType,
    /// Performance metrics
    pub metrics: PerformanceMetrics,
    /// Resource utilization
    pub resource_utilization: ResourceUtilization,
    /// Supported problem sizes
    pub problem_size_limits: ProblemSizeLimits,
    /// Optimization recommendations
    pub optimizations: Vec<OptimizationRecommendation>,
}

/// Performance metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceMetrics {
    /// Throughput (operations/second)
    pub throughput: f64,
    /// Latency (seconds)
    pub latency: f64,
    /// Memory efficiency (0.0 to 1.0)
    pub memory_efficiency: f64,
    /// CPU utilization (0.0 to 1.0)
    pub cpu_utilization: f64,
    /// GPU utilization (0.0 to 1.0)
    pub gpu_utilization: f64,
    /// Energy efficiency (operations/watt)
    pub energy_efficiency: f64,
}

/// Resource utilization
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceUtilization {
    /// CPU usage percentage
    pub cpu_usage: f64,
    /// Memory usage (GB)
    pub memory_usage_gb: f64,
    /// GPU usage percentage
    pub gpu_usage: f64,
    /// GPU memory usage (GB)
    pub gpu_memory_usage_gb: f64,
    /// Network usage (MB/s)
    pub network_usage_mbps: f64,
}

/// Problem size limits for each backend
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProblemSizeLimits {
    /// Maximum number of qubits
    pub max_qubits: usize,
    /// Maximum circuit depth
    pub max_depth: usize,
    /// Maximum gates
    pub max_gates: usize,
    /// Maximum memory usage (GB)
    pub max_memory_gb: f64,
}

/// Optimization recommendations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationRecommendation {
    /// Recommendation type
    pub recommendation_type: RecommendationType,
    /// Description
    pub description: String,
    /// Expected performance improvement
    pub expected_improvement: f64,
    /// Implementation difficulty (1-10)
    pub difficulty: u8,
    /// Resource requirements
    pub resource_cost: ResourceCost,
}

/// Types of optimization recommendations
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum RecommendationType {
    /// Use different backend
    BackendSwitch,
    /// Adjust parallelization
    ParallelizationTuning,
    /// Memory optimization
    MemoryOptimization,
    /// Algorithm-specific optimization
    AlgorithmOptimization,
    /// Hardware-specific optimization
    HardwareOptimization,
    /// Numerical precision adjustment
    PrecisionAdjustment,
}

/// Resource cost for implementing recommendations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceCost {
    /// Additional memory required (GB)
    pub memory_gb: f64,
    /// Additional CPU cores required
    pub cpu_cores: usize,
    /// Additional GPU memory required (GB)
    pub gpu_memory_gb: f64,
    /// Setup time required
    pub setup_time: Duration,
}

/// Resource monitoring system
#[derive(Debug, Clone)]
pub struct ResourceMonitor {
    /// CPU usage history
    pub cpu_history: Vec<f64>,
    /// Memory usage history
    pub memory_history: Vec<f64>,
    /// GPU usage history
    pub gpu_history: Vec<f64>,
    /// Network usage history
    pub network_history: Vec<f64>,
    /// Monitoring interval
    pub interval: Duration,
}

/// Backend selection result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BackendSelection {
    /// Selected backend type
    pub backend_type: BackendType,
    /// Confidence score (0.0 to 1.0)
    pub confidence: f64,
    /// Reasoning for selection
    pub reasoning: Vec<String>,
    /// Expected performance
    pub expected_performance: PerformanceMetrics,
    /// Configuration parameters
    pub configuration: BackendConfiguration,
    /// Alternative backends
    pub alternatives: Vec<(BackendType, f64)>,
}

/// Backend configuration parameters
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BackendConfiguration {
    /// Number of threads to use
    pub num_threads: usize,
    /// Batch size for parallel operations
    pub batch_size: usize,
    /// Memory allocation strategy
    pub memory_strategy: MemoryStrategy,
    /// Precision settings
    pub precision_settings: PrecisionSettings,
    /// GPU configuration
    pub gpu_config: Option<GPUConfiguration>,
    /// Distributed configuration
    pub distributed_config: Option<DistributedConfiguration>,
}

/// Memory allocation strategies
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum MemoryStrategy {
    /// Standard heap allocation
    Heap,
    /// Memory pool allocation
    Pool,
    /// Memory-mapped allocation
    MemoryMapped,
    /// Shared memory allocation
    Shared,
}

/// Precision settings
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PrecisionSettings {
    /// Floating-point precision
    pub float_precision: FloatPrecision,
    /// Numerical tolerance
    pub tolerance: f64,
    /// Adaptive precision enabled
    pub adaptive_precision: bool,
}

/// Floating-point precision levels
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum FloatPrecision {
    /// Single precision (32-bit)
    Single,
    /// Double precision (64-bit)
    Double,
    /// Extended precision (80-bit)
    Extended,
    /// Quadruple precision (128-bit)
    Quadruple,
}

/// GPU configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GPUConfiguration {
    /// GPU device ID
    pub device_id: usize,
    /// Block size for GPU kernels
    pub block_size: usize,
    /// Grid size for GPU kernels
    pub grid_size: usize,
    /// Memory allocation strategy
    pub memory_strategy: GPUMemoryStrategy,
    /// Use unified memory
    pub use_unified_memory: bool,
}

/// GPU memory allocation strategies
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum GPUMemoryStrategy {
    /// Standard GPU memory
    Standard,
    /// Unified memory
    Unified,
    /// Memory pool
    Pool,
    /// Pinned memory
    Pinned,
}

/// Distributed configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DistributedConfiguration {
    /// Number of nodes
    pub num_nodes: usize,
    /// Communication backend
    pub comm_backend: CommunicationBackend,
    /// Load balancing strategy
    pub load_balancing: LoadBalancingStrategy,
    /// Fault tolerance enabled
    pub fault_tolerance: bool,
}

/// Communication backends for distributed computing
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum CommunicationBackend {
    /// MPI (Message Passing Interface)
    MPI,
    /// TCP/IP networking
    TCP,
    /// InfiniBand
    InfiniBand,
    /// Shared memory
    SharedMemory,
}

/// Load balancing strategies
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum LoadBalancingStrategy {
    /// Static load balancing
    Static,
    /// Dynamic load balancing
    Dynamic,
    /// Work stealing
    WorkStealing,
    /// Hierarchical balancing
    Hierarchical,
}

impl AutoOptimizer {
    /// Create a new AutoOptimizer
    pub fn new(config: AutoOptimizerConfig) -> QuantRS2Result<Self> {
        let platform_caps = Arc::new(PlatformCapabilities::detect());
        let analysis_cache = HashMap::new();
        let backend_profiles = Self::initialize_backend_profiles(&platform_caps)?;
        let resource_monitor = ResourceMonitor {
            cpu_history: Vec::new(),
            memory_history: Vec::new(),
            gpu_history: Vec::new(),
            network_history: Vec::new(),
            interval: config.monitoring_interval,
        };

        Ok(Self {
            platform_caps,
            config,
            analysis_cache,
            backend_profiles,
            resource_monitor,
        })
    }

    /// Initialize backend performance profiles
    fn initialize_backend_profiles(
        platform_caps: &PlatformCapabilities,
    ) -> QuantRS2Result<HashMap<BackendType, PerformanceProfile>> {
        let mut profiles = HashMap::new();

        // CPU Single-threaded profile
        profiles.insert(
            BackendType::CPUSingle,
            PerformanceProfile {
                backend_type: BackendType::CPUSingle,
                metrics: PerformanceMetrics {
                    throughput: 1000.0,
                    latency: 0.001,
                    memory_efficiency: 0.8,
                    cpu_utilization: 0.25,
                    gpu_utilization: 0.0,
                    energy_efficiency: 100.0,
                },
                resource_utilization: ResourceUtilization {
                    cpu_usage: 25.0,
                    memory_usage_gb: 1.0,
                    gpu_usage: 0.0,
                    gpu_memory_usage_gb: 0.0,
                    network_usage_mbps: 0.0,
                },
                problem_size_limits: ProblemSizeLimits {
                    max_qubits: 20,
                    max_depth: 1000,
                    max_gates: 10000,
                    max_memory_gb: 4.0,
                },
                optimizations: vec![OptimizationRecommendation {
                    recommendation_type: RecommendationType::BackendSwitch,
                    description: "Consider CPU parallel for larger problems".to_string(),
                    expected_improvement: 3.0,
                    difficulty: 2,
                    resource_cost: ResourceCost {
                        memory_gb: 0.5,
                        cpu_cores: 3,
                        gpu_memory_gb: 0.0,
                        setup_time: Duration::from_millis(10),
                    },
                }],
            },
        );

        // CPU Parallel profile
        profiles.insert(
            BackendType::CPUParallel,
            PerformanceProfile {
                backend_type: BackendType::CPUParallel,
                metrics: PerformanceMetrics {
                    throughput: platform_caps.cpu.logical_cores as f64 * 800.0,
                    latency: 0.002,
                    memory_efficiency: 0.7,
                    cpu_utilization: 0.8,
                    gpu_utilization: 0.0,
                    energy_efficiency: 80.0,
                },
                resource_utilization: ResourceUtilization {
                    cpu_usage: 80.0,
                    memory_usage_gb: 2.0,
                    gpu_usage: 0.0,
                    gpu_memory_usage_gb: 0.0,
                    network_usage_mbps: 0.0,
                },
                problem_size_limits: ProblemSizeLimits {
                    max_qubits: 25,
                    max_depth: 2000,
                    max_gates: 50000,
                    max_memory_gb: 16.0,
                },
                optimizations: vec![OptimizationRecommendation {
                    recommendation_type: RecommendationType::ParallelizationTuning,
                    description: "Optimize thread count for problem size".to_string(),
                    expected_improvement: 1.5,
                    difficulty: 3,
                    resource_cost: ResourceCost {
                        memory_gb: 1.0,
                        cpu_cores: 0,
                        gpu_memory_gb: 0.0,
                        setup_time: Duration::from_millis(50),
                    },
                }],
            },
        );

        // GPU profile (if available)
        if platform_caps.has_gpu() {
            profiles.insert(
                BackendType::GPU,
                PerformanceProfile {
                    backend_type: BackendType::GPU,
                    metrics: PerformanceMetrics {
                        throughput: 50000.0,
                        latency: 0.005,
                        memory_efficiency: 0.9,
                        cpu_utilization: 0.2,
                        gpu_utilization: 0.8,
                        energy_efficiency: 200.0,
                    },
                    resource_utilization: ResourceUtilization {
                        cpu_usage: 20.0,
                        memory_usage_gb: 2.0,
                        gpu_usage: 80.0,
                        gpu_memory_usage_gb: 4.0,
                        network_usage_mbps: 0.0,
                    },
                    problem_size_limits: ProblemSizeLimits {
                        max_qubits: 30,
                        max_depth: 5000,
                        max_gates: 100_000,
                        max_memory_gb: 8.0,
                    },
                    optimizations: vec![OptimizationRecommendation {
                        recommendation_type: RecommendationType::HardwareOptimization,
                        description: "Optimize GPU kernel parameters".to_string(),
                        expected_improvement: 2.0,
                        difficulty: 7,
                        resource_cost: ResourceCost {
                            memory_gb: 1.0,
                            cpu_cores: 0,
                            gpu_memory_gb: 2.0,
                            setup_time: Duration::from_millis(100),
                        },
                    }],
                },
            );
        }

        Ok(profiles)
    }

    /// Analyze problem characteristics
    pub fn analyze_problem(
        &mut self,
        circuit_gates: &[GateType],
        num_qubits: usize,
        problem_type: ProblemType,
    ) -> QuantRS2Result<ProblemAnalysis> {
        // Create cache key
        let cache_key = format!("{:?}-{}-{}", problem_type, num_qubits, circuit_gates.len());

        if self.config.enable_caching {
            if let Some(cached) = self.analysis_cache.get(&cache_key) {
                return Ok(cached.clone());
            }
        }

        let circuit_depth = Self::calculate_circuit_depth(circuit_gates);
        let gate_composition = Self::analyze_gate_composition(circuit_gates);
        let entanglement_analysis = Self::analyze_entanglement(circuit_gates, num_qubits);
        let memory_estimate_gb = Self::estimate_memory_requirements(num_qubits, circuit_depth);
        let complexity_estimate = Self::estimate_complexity(circuit_gates, num_qubits);
        let parallelization_potential =
            Self::analyze_parallelization_potential(circuit_gates, num_qubits, &gate_composition);
        let resource_requirements = Self::estimate_resource_requirements(
            num_qubits,
            circuit_depth,
            &complexity_estimate,
            &parallelization_potential,
        );

        let analysis = ProblemAnalysis {
            num_qubits,
            circuit_depth,
            gate_composition,
            entanglement_analysis,
            memory_estimate_gb,
            complexity_estimate,
            problem_type,
            parallelization_potential,
            resource_requirements,
        };

        if self.config.enable_caching {
            self.analysis_cache.insert(cache_key, analysis.clone());
        }

        Ok(analysis)
    }

    /// Select optimal backend based on problem analysis
    pub fn select_backend(&self, analysis: &ProblemAnalysis) -> QuantRS2Result<BackendSelection> {
        let mut scores = HashMap::new();
        let mut reasoning = Vec::new();

        // Score each available backend
        for (backend_type, profile) in &self.backend_profiles {
            let score = self.score_backend(analysis, profile)?;
            scores.insert(*backend_type, score);

            // Add reasoning for high-scoring backends
            if score > 0.7 {
                reasoning.push(format!(
                    "{backend_type:?} backend scores {score:.2} due to good fit for problem characteristics"
                ));
            }
        }

        // Find best backend
        let (best_backend, best_score) = scores
            .iter()
            .max_by(|a, b| a.1.partial_cmp(b.1).unwrap_or(std::cmp::Ordering::Equal))
            .ok_or_else(|| QuantRS2Error::InvalidInput("No backends available".to_string()))?;

        // Generate alternatives
        let mut alternatives: Vec<(BackendType, f64)> = scores
            .iter()
            .filter(|(backend, _)| *backend != best_backend)
            .map(|(backend, score)| (*backend, *score))
            .collect();
        alternatives.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
        alternatives.truncate(3); // Top 3 alternatives

        // Generate configuration
        let configuration = self.generate_backend_configuration(analysis, *best_backend)?;

        // Get expected performance
        let expected_performance = self.backend_profiles[best_backend].metrics.clone();

        // Add selection reasoning
        reasoning.push(format!(
            "Selected {best_backend:?} backend with confidence {best_score:.2}"
        ));

        if analysis.num_qubits >= self.config.gpu_threshold_qubits && self.platform_caps.has_gpu() {
            reasoning.push(
                "Problem size exceeds GPU threshold, GPU acceleration recommended".to_string(),
            );
        }

        if analysis.parallelization_potential.gate_parallelism > 0.5 {
            reasoning.push("High gate parallelism potential detected".to_string());
        }

        Ok(BackendSelection {
            backend_type: *best_backend,
            confidence: *best_score,
            reasoning,
            expected_performance,
            configuration,
            alternatives,
        })
    }

    /// Score a backend for the given problem
    fn score_backend(
        &self,
        analysis: &ProblemAnalysis,
        profile: &PerformanceProfile,
    ) -> QuantRS2Result<f64> {
        let mut score = 0.0;
        let mut weight_sum = 0.0;

        // Problem size compatibility
        let size_weight = 0.3;
        let size_score = if analysis.num_qubits <= profile.problem_size_limits.max_qubits {
            (analysis.num_qubits as f64 / profile.problem_size_limits.max_qubits as f64)
                .mul_add(-0.5, 1.0)
        } else {
            0.0 // Cannot handle problem size
        };
        score += size_score * size_weight;
        weight_sum += size_weight;

        // Performance requirements
        let perf_weight = 0.25;
        let perf_score = match self.config.performance_bias {
            bias if bias > 0.7 => profile.metrics.throughput / 10000.0, // Prefer high throughput
            bias if bias < 0.3 => 1.0 / (profile.metrics.latency * 1000.0), // Prefer low latency
            _ => f64::midpoint(
                profile.metrics.throughput / 10000.0,
                1.0 / (profile.metrics.latency * 1000.0),
            ),
        };
        score += perf_score.min(1.0) * perf_weight;
        weight_sum += perf_weight;

        // Resource constraints
        let resource_weight = 0.2;
        let resource_score = if analysis.memory_estimate_gb <= self.config.max_memory_gb {
            (analysis.memory_estimate_gb / self.config.max_memory_gb).mul_add(-0.3, 1.0)
        } else {
            0.0 // Exceeds memory limits
        };
        score += resource_score * resource_weight;
        weight_sum += resource_weight;

        // Backend-specific bonuses
        let bonus_weight = 0.15;
        let bonus_score = match profile.backend_type {
            BackendType::GPU
                if self.config.enable_gpu
                    && analysis.num_qubits >= self.config.gpu_threshold_qubits =>
            {
                1.0
            }
            BackendType::CPUParallel
                if analysis.parallelization_potential.gate_parallelism > 0.5 =>
            {
                0.8
            }
            BackendType::Distributed
                if self.config.enable_distributed
                    && analysis.num_qubits >= self.config.distributed_threshold_qubits =>
            {
                0.9
            }
            _ => 0.5,
        };
        score += bonus_score * bonus_weight;
        weight_sum += bonus_weight;

        // Parallelization potential alignment
        let parallel_weight = 0.1;
        let parallel_score = match profile.backend_type {
            BackendType::GPU => analysis.parallelization_potential.gpu_potential,
            BackendType::CPUParallel => analysis.parallelization_potential.gate_parallelism,
            BackendType::Distributed => analysis.parallelization_potential.distributed_potential,
            _ => 0.5,
        };
        score += parallel_score * parallel_weight;
        weight_sum += parallel_weight;

        // Normalize score
        if weight_sum > 0.0 {
            score /= weight_sum;
        }

        Ok(score.clamp(0.0, 1.0))
    }

    /// Generate backend configuration
    fn generate_backend_configuration(
        &self,
        analysis: &ProblemAnalysis,
        backend_type: BackendType,
    ) -> QuantRS2Result<BackendConfiguration> {
        let num_threads = match backend_type {
            BackendType::CPUParallel | BackendType::Hybrid => {
                (self.platform_caps.cpu.logical_cores as f64 * 0.8).round() as usize
            }
            _ => 1,
        };

        let batch_size = match backend_type {
            BackendType::GPU => 1024,
            BackendType::CPUParallel => 64,
            _ => 1,
        };

        let memory_strategy = match backend_type {
            BackendType::GPU => MemoryStrategy::Pool,
            BackendType::Distributed => MemoryStrategy::Shared,
            _ => MemoryStrategy::Heap,
        };

        let precision_settings = PrecisionSettings {
            float_precision: if analysis.problem_type == ProblemType::QEC {
                FloatPrecision::Quadruple
            } else {
                FloatPrecision::Double
            },
            tolerance: 1e-12,
            adaptive_precision: true,
        };

        let gpu_config = if backend_type == BackendType::GPU || backend_type == BackendType::Hybrid
        {
            Some(GPUConfiguration {
                device_id: 0,
                block_size: 256,
                grid_size: (analysis.num_qubits * analysis.num_qubits + 255) / 256,
                memory_strategy: GPUMemoryStrategy::Pool,
                use_unified_memory: false, // Default to false, can be overridden based on GPU capabilities
            })
        } else {
            None
        };

        let distributed_config = if backend_type == BackendType::Distributed {
            Some(DistributedConfiguration {
                num_nodes: 4,
                comm_backend: CommunicationBackend::TCP,
                load_balancing: LoadBalancingStrategy::Dynamic,
                fault_tolerance: true,
            })
        } else {
            None
        };

        Ok(BackendConfiguration {
            num_threads,
            batch_size,
            memory_strategy,
            precision_settings,
            gpu_config,
            distributed_config,
        })
    }

    /// Calculate circuit depth
    fn calculate_circuit_depth(gates: &[GateType]) -> usize {
        // Simplified depth calculation - in reality would need dependency analysis
        (gates.len() as f64 * 0.7).round() as usize
    }

    /// Analyze gate composition
    fn analyze_gate_composition(gates: &[GateType]) -> GateComposition {
        let mut gate_types = HashMap::new();
        let mut single_qubit_gates = 0;
        let mut two_qubit_gates = 0;
        let mut multi_qubit_gates = 0;
        let mut parameterized_gates = 0;

        for gate in gates {
            let gate_name = format!("{gate:?}");
            *gate_types.entry(gate_name).or_insert(0) += 1;

            match gate {
                GateType::H
                | GateType::X
                | GateType::Y
                | GateType::Z
                | GateType::S
                | GateType::T
                | GateType::Rx(_)
                | GateType::Ry(_)
                | GateType::Rz(_) => {
                    single_qubit_gates += 1;
                    if matches!(gate, GateType::Rx(_) | GateType::Ry(_) | GateType::Rz(_)) {
                        parameterized_gates += 1;
                    }
                }
                GateType::CNOT | GateType::CZ | GateType::SWAP => {
                    two_qubit_gates += 1;
                }
                _ => {
                    multi_qubit_gates += 1;
                }
            }
        }

        let total_gates = gates.len();
        let simd_friendly_gates = single_qubit_gates + two_qubit_gates;
        let simd_friendly_percentage = if total_gates > 0 {
            simd_friendly_gates as f64 / total_gates as f64
        } else {
            0.0
        };

        GateComposition {
            total_gates,
            single_qubit_gates,
            two_qubit_gates,
            multi_qubit_gates,
            gate_types,
            parameterized_gates,
            simd_friendly_percentage,
        }
    }

    /// Analyze entanglement properties
    fn analyze_entanglement(gates: &[GateType], num_qubits: usize) -> EntanglementAnalysis {
        let mut entangling_gates = 0;
        let mut connectivity_set = HashSet::new();

        for gate in gates {
            match gate {
                GateType::CNOT | GateType::CZ | GateType::SWAP => {
                    entangling_gates += 1;
                    // Would need actual qubit indices for proper connectivity analysis
                    connectivity_set.insert((0, 1)); // Simplified
                }
                _ => {}
            }
        }

        let max_entanglement_depth = (entangling_gates as f64).log2().ceil() as usize;
        let connectivity_ratio =
            connectivity_set.len() as f64 / (num_qubits * (num_qubits - 1) / 2) as f64;
        let separable_subsystems = if entangling_gates == 0 { num_qubits } else { 1 };
        let entanglement_entropy = if entangling_gates > 0 {
            (num_qubits as f64).log2()
        } else {
            0.0
        };

        EntanglementAnalysis {
            max_entanglement_depth,
            connectivity_ratio,
            separable_subsystems,
            entanglement_entropy,
        }
    }

    /// Estimate memory requirements
    fn estimate_memory_requirements(num_qubits: usize, circuit_depth: usize) -> f64 {
        let state_vector_size = 2_usize.pow(num_qubits as u32);
        let complex_size = 16; // bytes per Complex64
        let base_memory = (state_vector_size * complex_size) as f64 / (1024.0 * 1024.0 * 1024.0);
        let overhead_factor = 1.5; // Account for intermediate calculations
        let depth_factor = (circuit_depth as f64).mul_add(0.01, 1.0);

        base_memory * overhead_factor * depth_factor
    }

    /// Estimate computational complexity
    fn estimate_complexity(gates: &[GateType], num_qubits: usize) -> ComplexityEstimate {
        let operation_count = gates.len() as u64 * 2_u64.pow(num_qubits as u32);

        let time_complexity = if num_qubits <= 10 {
            ComplexityClass::Linear
        } else if num_qubits <= 20 {
            ComplexityClass::Quadratic
        } else {
            ComplexityClass::Exponential
        };

        let space_complexity = if num_qubits <= 25 {
            ComplexityClass::Exponential
        } else {
            ComplexityClass::DoubleExponential
        };

        let memory_pattern = if gates.len() > 1000 {
            MemoryPattern::Blocked
        } else {
            MemoryPattern::Sequential
        };

        let parallelization_factor = if num_qubits >= 12 {
            (num_qubits as f64 / 4.0).min(8.0)
        } else {
            1.0
        };

        ComplexityEstimate {
            time_complexity,
            space_complexity,
            operation_count,
            memory_pattern,
            parallelization_factor,
        }
    }

    /// Analyze parallelization potential
    fn analyze_parallelization_potential(
        gates: &[GateType],
        num_qubits: usize,
        gate_composition: &GateComposition,
    ) -> ParallelizationPotential {
        let gate_parallelism = if gate_composition.single_qubit_gates > 0 {
            gate_composition.single_qubit_gates as f64 / gate_composition.total_gates as f64
        } else {
            0.0
        };

        let state_parallelism = if num_qubits >= 8 {
            (num_qubits as f64 - 7.0) / 10.0
        } else {
            0.1
        };

        let batch_potential = if gate_composition.total_gates > 100 {
            0.8
        } else {
            0.3
        };

        let simd_potential = gate_composition.simd_friendly_percentage;

        let gpu_potential = if num_qubits >= 12 && gate_composition.total_gates > 500 {
            0.9
        } else {
            0.2
        };

        let distributed_potential = if num_qubits >= 20 { 0.8 } else { 0.1 };

        ParallelizationPotential {
            gate_parallelism,
            state_parallelism,
            batch_potential,
            simd_potential,
            gpu_potential,
            distributed_potential,
        }
    }

    /// Estimate resource requirements
    fn estimate_resource_requirements(
        num_qubits: usize,
        circuit_depth: usize,
        complexity: &ComplexityEstimate,
        parallelization: &ParallelizationPotential,
    ) -> ResourceRequirements {
        let memory_gb = Self::estimate_memory_requirements(num_qubits, circuit_depth);

        let cpu_cores = if parallelization.gate_parallelism > 0.5 {
            (num_qubits / 2).clamp(2, 16)
        } else {
            1
        };

        let gpu_memory_gb = if parallelization.gpu_potential > 0.5 {
            memory_gb * 1.2
        } else {
            0.0
        };

        let network_bandwidth_mbps = if parallelization.distributed_potential > 0.5 {
            100.0 * num_qubits as f64
        } else {
            0.0
        };

        let storage_gb = memory_gb * 0.1; // For checkpointing and results

        let base_time_ms = match complexity.time_complexity {
            ComplexityClass::Linear => circuit_depth as f64 * 0.1,
            ComplexityClass::Quadratic => circuit_depth as f64 * circuit_depth as f64 * 0.001,
            ComplexityClass::Exponential => (num_qubits as f64 * 0.5).exp2(),
            _ => 1000.0,
        };

        let estimated_time =
            Duration::from_millis((base_time_ms / complexity.parallelization_factor) as u64);

        ResourceRequirements {
            memory_gb,
            cpu_cores,
            gpu_memory_gb,
            network_bandwidth_mbps,
            storage_gb,
            estimated_time,
        }
    }

    /// Get optimization recommendations
    pub fn get_recommendations(
        &self,
        analysis: &ProblemAnalysis,
        selection: &BackendSelection,
    ) -> Vec<OptimizationRecommendation> {
        let mut recommendations = Vec::new();

        // Memory optimization recommendations
        if analysis.memory_estimate_gb > self.config.max_memory_gb * 0.8 {
            recommendations.push(OptimizationRecommendation {
                recommendation_type: RecommendationType::MemoryOptimization,
                description:
                    "Consider using memory-efficient state representation or chunked computation"
                        .to_string(),
                expected_improvement: 0.6,
                difficulty: 6,
                resource_cost: ResourceCost {
                    memory_gb: -analysis.memory_estimate_gb * 0.3,
                    cpu_cores: 1,
                    gpu_memory_gb: 0.0,
                    setup_time: Duration::from_millis(200),
                },
            });
        }

        // Backend switching recommendations
        if selection.confidence < 0.8 {
            for (alt_backend, score) in &selection.alternatives {
                if *score > selection.confidence {
                    recommendations.push(OptimizationRecommendation {
                        recommendation_type: RecommendationType::BackendSwitch,
                        description: format!(
                            "Consider switching to {alt_backend:?} backend for better performance"
                        ),
                        expected_improvement: score - selection.confidence,
                        difficulty: 3,
                        resource_cost: ResourceCost {
                            memory_gb: 0.5,
                            cpu_cores: 0,
                            gpu_memory_gb: if *alt_backend == BackendType::GPU {
                                2.0
                            } else {
                                0.0
                            },
                            setup_time: Duration::from_millis(100),
                        },
                    });
                }
            }
        }

        // Parallelization recommendations
        if analysis.parallelization_potential.gate_parallelism > 0.7
            && selection.backend_type == BackendType::CPUSingle
        {
            recommendations.push(OptimizationRecommendation {
                recommendation_type: RecommendationType::ParallelizationTuning,
                description:
                    "High parallelization potential detected - consider parallel CPU backend"
                        .to_string(),
                expected_improvement: analysis.parallelization_potential.gate_parallelism,
                difficulty: 4,
                resource_cost: ResourceCost {
                    memory_gb: 1.0,
                    cpu_cores: self.platform_caps.cpu.logical_cores - 1,
                    gpu_memory_gb: 0.0,
                    setup_time: Duration::from_millis(50),
                },
            });
        }

        // GPU recommendations
        if analysis.parallelization_potential.gpu_potential > 0.8
            && selection.backend_type != BackendType::GPU
            && self.platform_caps.has_gpu()
        {
            recommendations.push(OptimizationRecommendation {
                recommendation_type: RecommendationType::HardwareOptimization,
                description: "Problem shows high GPU acceleration potential".to_string(),
                expected_improvement: analysis.parallelization_potential.gpu_potential,
                difficulty: 7,
                resource_cost: ResourceCost {
                    memory_gb: 1.0,
                    cpu_cores: 0,
                    gpu_memory_gb: analysis.memory_estimate_gb,
                    setup_time: Duration::from_millis(300),
                },
            });
        }

        // Algorithm-specific recommendations
        match analysis.problem_type {
            ProblemType::VQE => {
                recommendations.push(OptimizationRecommendation {
                    recommendation_type: RecommendationType::AlgorithmOptimization,
                    description: "Consider using gradient-free optimization methods for VQE"
                        .to_string(),
                    expected_improvement: 0.3,
                    difficulty: 5,
                    resource_cost: ResourceCost {
                        memory_gb: 0.5,
                        cpu_cores: 0,
                        gpu_memory_gb: 0.0,
                        setup_time: Duration::from_millis(100),
                    },
                });
            }
            ProblemType::QAOA => {
                recommendations.push(OptimizationRecommendation {
                    recommendation_type: RecommendationType::AlgorithmOptimization,
                    description: "Consider using warm-start initialization for QAOA parameters"
                        .to_string(),
                    expected_improvement: 0.4,
                    difficulty: 4,
                    resource_cost: ResourceCost {
                        memory_gb: 0.2,
                        cpu_cores: 0,
                        gpu_memory_gb: 0.0,
                        setup_time: Duration::from_millis(50),
                    },
                });
            }
            _ => {}
        }

        // Precision recommendations
        if analysis.problem_type == ProblemType::Simulation
            && selection.configuration.precision_settings.float_precision == FloatPrecision::Double
        {
            recommendations.push(OptimizationRecommendation {
                recommendation_type: RecommendationType::PrecisionAdjustment,
                description: "Consider single precision for simulation if acceptable accuracy"
                    .to_string(),
                expected_improvement: 0.5,
                difficulty: 2,
                resource_cost: ResourceCost {
                    memory_gb: -analysis.memory_estimate_gb * 0.5,
                    cpu_cores: 0,
                    gpu_memory_gb: 0.0,
                    setup_time: Duration::from_millis(10),
                },
            });
        }

        recommendations
    }

    /// Monitor and update backend selection dynamically
    pub fn monitor_and_update(
        &mut self,
        current_selection: &BackendSelection,
        current_performance: &PerformanceMetrics,
    ) -> QuantRS2Result<Option<BackendSelection>> {
        if !self.config.enable_auto_switching {
            return Ok(None);
        }

        // Update resource monitoring
        self.update_resource_monitoring(current_performance)?;

        // Check if current backend is underperforming
        let expected_performance = &current_selection.expected_performance;
        let performance_ratio = current_performance.throughput / expected_performance.throughput;

        if performance_ratio < 0.7 {
            // Performance is significantly below expectations
            // Consider switching to a different backend
            // This would require re-analysis of the current problem
            // For now, return None indicating no change
            return Ok(None);
        }

        // Check resource constraints
        if current_performance.memory_efficiency < 0.5 {
            // Memory efficiency is poor, might need optimization
            return Ok(None);
        }

        Ok(None)
    }

    /// Update resource monitoring
    fn update_resource_monitoring(
        &mut self,
        current_performance: &PerformanceMetrics,
    ) -> QuantRS2Result<()> {
        self.resource_monitor
            .cpu_history
            .push(current_performance.cpu_utilization);
        self.resource_monitor
            .memory_history
            .push(current_performance.memory_efficiency);
        self.resource_monitor
            .gpu_history
            .push(current_performance.gpu_utilization);

        // Keep only recent history
        let max_history = 100;
        if self.resource_monitor.cpu_history.len() > max_history {
            self.resource_monitor.cpu_history.remove(0);
        }
        if self.resource_monitor.memory_history.len() > max_history {
            self.resource_monitor.memory_history.remove(0);
        }
        if self.resource_monitor.gpu_history.len() > max_history {
            self.resource_monitor.gpu_history.remove(0);
        }

        Ok(())
    }
}

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

    #[test]
    fn test_auto_optimizer_creation() {
        let config = AutoOptimizerConfig::default();
        let optimizer = AutoOptimizer::new(config);
        assert!(optimizer.is_ok());
    }

    #[test]
    fn test_problem_analysis() {
        let config = AutoOptimizerConfig::default();
        let mut optimizer = AutoOptimizer::new(config).expect("Failed to create AutoOptimizer");

        let gates = vec![
            GateType::H,
            GateType::CNOT,
            GateType::Rz("0.5".to_string()),
            GateType::X,
        ];

        let analysis = optimizer.analyze_problem(&gates, 5, ProblemType::Simulation);
        assert!(analysis.is_ok());

        let analysis = analysis.expect("Failed to analyze problem");
        assert_eq!(analysis.num_qubits, 5);
        assert_eq!(analysis.gate_composition.total_gates, 4);
        assert_eq!(analysis.problem_type, ProblemType::Simulation);
    }

    #[test]
    fn test_backend_selection() {
        let config = AutoOptimizerConfig::default();
        let optimizer = AutoOptimizer::new(config).expect("Failed to create AutoOptimizer");

        let analysis = ProblemAnalysis {
            num_qubits: 10,
            circuit_depth: 50,
            gate_composition: GateComposition {
                total_gates: 100,
                single_qubit_gates: 60,
                two_qubit_gates: 30,
                multi_qubit_gates: 10,
                gate_types: HashMap::new(),
                parameterized_gates: 20,
                simd_friendly_percentage: 0.9,
            },
            entanglement_analysis: EntanglementAnalysis {
                max_entanglement_depth: 5,
                connectivity_ratio: 0.3,
                separable_subsystems: 2,
                entanglement_entropy: 3.0,
            },
            memory_estimate_gb: 2.0,
            complexity_estimate: ComplexityEstimate {
                time_complexity: ComplexityClass::Quadratic,
                space_complexity: ComplexityClass::Exponential,
                operation_count: 1000000,
                memory_pattern: MemoryPattern::Sequential,
                parallelization_factor: 4.0,
            },
            problem_type: ProblemType::Simulation,
            parallelization_potential: ParallelizationPotential {
                gate_parallelism: 0.8,
                state_parallelism: 0.6,
                batch_potential: 0.7,
                simd_potential: 0.9,
                gpu_potential: 0.5,
                distributed_potential: 0.2,
            },
            resource_requirements: ResourceRequirements {
                memory_gb: 2.0,
                cpu_cores: 4,
                gpu_memory_gb: 0.0,
                network_bandwidth_mbps: 0.0,
                storage_gb: 0.2,
                estimated_time: Duration::from_secs(60),
            },
        };

        let selection = optimizer.select_backend(&analysis);
        assert!(selection.is_ok());

        let selection = selection.expect("Failed to select backend");
        assert!(selection.confidence > 0.0);
        assert!(!selection.reasoning.is_empty());
    }

    #[test]
    fn test_recommendations() {
        let config = AutoOptimizerConfig::default();
        let optimizer = AutoOptimizer::new(config).expect("Failed to create AutoOptimizer");

        let analysis = ProblemAnalysis {
            num_qubits: 15,
            circuit_depth: 100,
            gate_composition: GateComposition {
                total_gates: 200,
                single_qubit_gates: 120,
                two_qubit_gates: 60,
                multi_qubit_gates: 20,
                gate_types: HashMap::new(),
                parameterized_gates: 40,
                simd_friendly_percentage: 0.85,
            },
            entanglement_analysis: EntanglementAnalysis {
                max_entanglement_depth: 8,
                connectivity_ratio: 0.4,
                separable_subsystems: 1,
                entanglement_entropy: 4.0,
            },
            memory_estimate_gb: 8.0,
            complexity_estimate: ComplexityEstimate {
                time_complexity: ComplexityClass::Exponential,
                space_complexity: ComplexityClass::Exponential,
                operation_count: 10000000,
                memory_pattern: MemoryPattern::Blocked,
                parallelization_factor: 6.0,
            },
            problem_type: ProblemType::VQE,
            parallelization_potential: ParallelizationPotential {
                gate_parallelism: 0.85,
                state_parallelism: 0.7,
                batch_potential: 0.8,
                simd_potential: 0.85,
                gpu_potential: 0.9,
                distributed_potential: 0.3,
            },
            resource_requirements: ResourceRequirements {
                memory_gb: 8.0,
                cpu_cores: 6,
                gpu_memory_gb: 4.0,
                network_bandwidth_mbps: 0.0,
                storage_gb: 0.8,
                estimated_time: Duration::from_secs(300),
            },
        };

        let selection = optimizer
            .select_backend(&analysis)
            .expect("Failed to select backend");
        let recommendations = optimizer.get_recommendations(&analysis, &selection);

        assert!(!recommendations.is_empty());

        // Should have recommendations for high-performance problem
        let has_memory_rec = recommendations
            .iter()
            .any(|r| r.recommendation_type == RecommendationType::MemoryOptimization);

        assert!(has_memory_rec || !recommendations.is_empty());
    }
}