quantrs2-ml 0.1.3

//! Domain-specific model templates for QuantRS2-ML
//!
//! This module provides ready-to-use model templates for specific application domains,
//! making it easier for users to get started with quantum ML in their field.

use crate::anneal_integration::{AnnealingParams, QuantumMLQUBO};
use crate::enhanced_gan::{ConditionalQGAN, WassersteinQGAN};
use crate::error::{MLError, Result};
use crate::keras_api::{
    ActivationFunction, Dense, LossFunction, MetricType, OptimizerType, QuantumAnsatzType,
    QuantumDense, Sequential,
};
use crate::optimization::{OptimizationMethod, Optimizer};
use crate::pytorch_api::{
    ActivationType as PyTorchActivationType, QuantumLinear, QuantumSequential,
};
use crate::qsvm::{FeatureMapType, QSVMParams, QSVM};
use crate::vae::{ClassicalAutoencoder, QVAE};
use quantrs2_circuit::prelude::*;
use quantrs2_core::prelude::*;
use scirs2_core::ndarray::{s, Array1, Array2, ArrayD, IxDyn};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;

/// Domain-specific template manager
pub struct DomainTemplateManager {
    /// Available templates by domain
    templates: HashMap<Domain, Vec<TemplateMetadata>>,
}

/// Application domains
#[derive(Debug, Clone, Hash, PartialEq, Eq, Serialize, Deserialize)]
pub enum Domain {
    /// Financial services and fintech
    Finance,
    /// Chemistry and molecular simulation
    Chemistry,
    /// Healthcare and medical applications
    Healthcare,
    /// Logistics and supply chain
    Logistics,
    /// Energy and utilities
    Energy,
    /// Materials science
    Materials,
    /// High-energy physics
    Physics,
    /// Natural language processing
    NLP,
    /// Computer vision
    Vision,
    /// Cybersecurity
    Security,
    /// Climate modeling
    Climate,
    /// Aerospace and defense
    Aerospace,
}

/// Template metadata
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TemplateMetadata {
    /// Template name
    pub name: String,
    /// Template description
    pub description: String,
    /// Domain
    pub domain: Domain,
    /// Problem type
    pub problem_type: ProblemType,
    /// Expected input features
    pub input_features: Vec<String>,
    /// Expected output
    pub output_description: String,
    /// Recommended dataset size
    pub recommended_dataset_size: usize,
    /// Model complexity
    pub complexity: ModelComplexity,
    /// Required qubits
    pub required_qubits: usize,
    /// Estimated training time
    pub training_time_estimate: String,
    /// Performance expectations
    pub performance_notes: String,
}

/// Problem types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ProblemType {
    /// Binary classification
    BinaryClassification,
    /// Multi-class classification
    MultiClassification,
    /// Regression
    Regression,
    /// Optimization
    Optimization,
    /// Generation
    Generation,
    /// Anomaly detection
    AnomalyDetection,
    /// Time series forecasting
    TimeSeriesForecasting,
    /// Clustering
    Clustering,
    /// Dimensionality reduction
    DimensionalityReduction,
}

/// Model complexity levels
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ModelComplexity {
    /// Simple models for quick prototyping
    Simple,
    /// Intermediate models with good balance
    Intermediate,
    /// Complex models for advanced use cases
    Complex,
    /// Research-grade models
    Research,
}

/// Template configuration
#[derive(Debug, Clone)]
pub struct TemplateConfig {
    /// Number of qubits to use
    pub num_qubits: usize,
    /// Input dimension
    pub input_dim: usize,
    /// Output dimension
    pub output_dim: usize,
    /// Additional parameters
    pub parameters: HashMap<String, f64>,
}

impl DomainTemplateManager {
    /// Create new template manager
    pub fn new() -> Self {
        let mut manager = Self {
            templates: HashMap::new(),
        };
        manager.register_templates();
        manager
    }

    /// Register all domain templates
    fn register_templates(&mut self) {
        self.register_finance_templates();
        self.register_chemistry_templates();
        self.register_healthcare_templates();
        self.register_logistics_templates();
        self.register_energy_templates();
        self.register_materials_templates();
        self.register_physics_templates();
        self.register_nlp_templates();
        self.register_vision_templates();
        self.register_security_templates();
        self.register_climate_templates();
        self.register_aerospace_templates();
    }

    /// Register finance domain templates
    fn register_finance_templates(&mut self) {
        let mut templates = Vec::new();

        // Portfolio optimization
        templates.push(TemplateMetadata {
            name: "Portfolio Optimization".to_string(),
            description: "Quantum portfolio optimization using QAOA for risk-return optimization"
                .to_string(),
            domain: Domain::Finance,
            problem_type: ProblemType::Optimization,
            input_features: vec![
                "asset_returns".to_string(),
                "risk_matrix".to_string(),
                "correlation_matrix".to_string(),
            ],
            output_description: "Optimal portfolio weights".to_string(),
            recommended_dataset_size: 500,
            complexity: ModelComplexity::Intermediate,
            required_qubits: 10,
            training_time_estimate: "10-30 minutes".to_string(),
            performance_notes: "Can outperform classical methods for small portfolios".to_string(),
        });

        // Credit risk assessment
        templates.push(TemplateMetadata {
            name: "Credit Risk Assessment".to_string(),
            description: "Quantum neural network for credit default prediction".to_string(),
            domain: Domain::Finance,
            problem_type: ProblemType::BinaryClassification,
            input_features: vec![
                "credit_score".to_string(),
                "income".to_string(),
                "debt_ratio".to_string(),
                "employment_history".to_string(),
                "loan_amount".to_string(),
            ],
            output_description: "Default probability".to_string(),
            recommended_dataset_size: 10000,
            complexity: ModelComplexity::Intermediate,
            required_qubits: 8,
            training_time_estimate: "1-2 hours".to_string(),
            performance_notes: "Competitive accuracy with quantum advantage in training speed"
                .to_string(),
        });

        // Fraud detection
        templates.push(TemplateMetadata {
            name: "Fraud Detection".to_string(),
            description: "Quantum anomaly detection for financial fraud".to_string(),
            domain: Domain::Finance,
            problem_type: ProblemType::AnomalyDetection,
            input_features: vec![
                "transaction_amount".to_string(),
                "transaction_time".to_string(),
                "merchant_category".to_string(),
                "location".to_string(),
                "user_behavior_patterns".to_string(),
            ],
            output_description: "Fraud probability".to_string(),
            recommended_dataset_size: 50000,
            complexity: ModelComplexity::Complex,
            required_qubits: 12,
            training_time_estimate: "2-4 hours".to_string(),
            performance_notes: "High sensitivity to rare fraud patterns".to_string(),
        });

        // Algorithmic trading
        templates.push(TemplateMetadata {
            name: "Algorithmic Trading".to_string(),
            description: "Quantum reinforcement learning for trading strategies".to_string(),
            domain: Domain::Finance,
            problem_type: ProblemType::TimeSeriesForecasting,
            input_features: vec![
                "price_history".to_string(),
                "volume".to_string(),
                "technical_indicators".to_string(),
                "market_sentiment".to_string(),
            ],
            output_description: "Trading signals (buy/sell/hold)".to_string(),
            recommended_dataset_size: 100000,
            complexity: ModelComplexity::Research,
            required_qubits: 16,
            training_time_estimate: "4-8 hours".to_string(),
            performance_notes: "Potential for discovering novel trading patterns".to_string(),
        });

        self.templates.insert(Domain::Finance, templates);
    }

    /// Register chemistry domain templates
    fn register_chemistry_templates(&mut self) {
        let mut templates = Vec::new();

        // Molecular property prediction
        templates.push(TemplateMetadata {
            name: "Molecular Property Prediction".to_string(),
            description: "Quantum graph neural networks for predicting molecular properties"
                .to_string(),
            domain: Domain::Chemistry,
            problem_type: ProblemType::Regression,
            input_features: vec![
                "molecular_graph".to_string(),
                "atom_features".to_string(),
                "bond_features".to_string(),
                "molecular_descriptors".to_string(),
            ],
            output_description: "Property values (e.g., solubility, toxicity)".to_string(),
            recommended_dataset_size: 5000,
            complexity: ModelComplexity::Complex,
            required_qubits: 12,
            training_time_estimate: "2-6 hours".to_string(),
            performance_notes: "Superior performance on quantum chemical properties".to_string(),
        });

        // Drug discovery
        templates.push(TemplateMetadata {
            name: "Drug Discovery".to_string(),
            description: "VQE-based molecular simulation for drug-target interaction".to_string(),
            domain: Domain::Chemistry,
            problem_type: ProblemType::BinaryClassification,
            input_features: vec![
                "drug_structure".to_string(),
                "target_structure".to_string(),
                "binding_site_features".to_string(),
                "pharmacophore_features".to_string(),
            ],
            output_description: "Binding affinity prediction".to_string(),
            recommended_dataset_size: 10000,
            complexity: ModelComplexity::Research,
            required_qubits: 20,
            training_time_estimate: "6-12 hours".to_string(),
            performance_notes: "Quantum advantage in modeling molecular interactions".to_string(),
        });

        // Catalyst design
        templates.push(TemplateMetadata {
            name: "Catalyst Design".to_string(),
            description: "Quantum optimization for catalyst material discovery".to_string(),
            domain: Domain::Chemistry,
            problem_type: ProblemType::Optimization,
            input_features: vec![
                "material_composition".to_string(),
                "surface_structure".to_string(),
                "reaction_conditions".to_string(),
                "electronic_properties".to_string(),
            ],
            output_description: "Optimal catalyst structure".to_string(),
            recommended_dataset_size: 2000,
            complexity: ModelComplexity::Research,
            required_qubits: 16,
            training_time_estimate: "4-10 hours".to_string(),
            performance_notes: "Potential for discovering novel catalysts".to_string(),
        });

        self.templates.insert(Domain::Chemistry, templates);
    }

    /// Register healthcare domain templates
    fn register_healthcare_templates(&mut self) {
        let mut templates = Vec::new();

        // Medical image analysis
        templates.push(TemplateMetadata {
            name: "Medical Image Analysis".to_string(),
            description: "Quantum convolutional networks for medical image classification"
                .to_string(),
            domain: Domain::Healthcare,
            problem_type: ProblemType::MultiClassification,
            input_features: vec![
                "image_pixels".to_string(),
                "image_metadata".to_string(),
                "patient_demographics".to_string(),
            ],
            output_description: "Disease classification or severity score".to_string(),
            recommended_dataset_size: 20000,
            complexity: ModelComplexity::Complex,
            required_qubits: 14,
            training_time_estimate: "3-8 hours".to_string(),
            performance_notes: "Improved accuracy on small datasets".to_string(),
        });

        // Drug interaction prediction
        templates.push(TemplateMetadata {
            name: "Drug Interaction Prediction".to_string(),
            description: "Quantum neural networks for predicting drug-drug interactions"
                .to_string(),
            domain: Domain::Healthcare,
            problem_type: ProblemType::BinaryClassification,
            input_features: vec![
                "drug1_features".to_string(),
                "drug2_features".to_string(),
                "molecular_similarity".to_string(),
                "pathway_information".to_string(),
            ],
            output_description: "Interaction probability and severity".to_string(),
            recommended_dataset_size: 15000,
            complexity: ModelComplexity::Intermediate,
            required_qubits: 10,
            training_time_estimate: "1-3 hours".to_string(),
            performance_notes: "High sensitivity to rare but dangerous interactions".to_string(),
        });

        // Genomic analysis
        templates.push(TemplateMetadata {
            name: "Genomic Analysis".to_string(),
            description: "Quantum feature selection for genomic data analysis".to_string(),
            domain: Domain::Healthcare,
            problem_type: ProblemType::MultiClassification,
            input_features: vec![
                "snp_data".to_string(),
                "gene_expression".to_string(),
                "epigenetic_markers".to_string(),
                "clinical_phenotypes".to_string(),
            ],
            output_description: "Disease susceptibility prediction".to_string(),
            recommended_dataset_size: 5000,
            complexity: ModelComplexity::Research,
            required_qubits: 18,
            training_time_estimate: "4-12 hours".to_string(),
            performance_notes: "Effective handling of high-dimensional genomic data".to_string(),
        });

        self.templates.insert(Domain::Healthcare, templates);
    }

    /// Register logistics domain templates
    fn register_logistics_templates(&mut self) {
        let mut templates = Vec::new();

        // Vehicle routing optimization
        templates.push(TemplateMetadata {
            name: "Vehicle Routing Optimization".to_string(),
            description: "QAOA-based solution for vehicle routing problems".to_string(),
            domain: Domain::Logistics,
            problem_type: ProblemType::Optimization,
            input_features: vec![
                "delivery_locations".to_string(),
                "distance_matrix".to_string(),
                "vehicle_capacity".to_string(),
                "time_windows".to_string(),
                "traffic_patterns".to_string(),
            ],
            output_description: "Optimal routes for each vehicle".to_string(),
            recommended_dataset_size: 1000,
            complexity: ModelComplexity::Intermediate,
            required_qubits: 12,
            training_time_estimate: "30 minutes - 2 hours".to_string(),
            performance_notes: "Quantum advantage for complex routing constraints".to_string(),
        });

        // Supply chain optimization
        templates.push(TemplateMetadata {
            name: "Supply Chain Optimization".to_string(),
            description: "Quantum optimization for supply chain network design".to_string(),
            domain: Domain::Logistics,
            problem_type: ProblemType::Optimization,
            input_features: vec![
                "supplier_locations".to_string(),
                "demand_forecasts".to_string(),
                "transportation_costs".to_string(),
                "capacity_constraints".to_string(),
                "risk_factors".to_string(),
            ],
            output_description: "Optimal supply chain configuration".to_string(),
            recommended_dataset_size: 2000,
            complexity: ModelComplexity::Complex,
            required_qubits: 16,
            training_time_estimate: "2-6 hours".to_string(),
            performance_notes: "Handles complex multi-objective optimization".to_string(),
        });

        // Inventory management
        templates.push(TemplateMetadata {
            name: "Inventory Management".to_string(),
            description: "Quantum time series forecasting for inventory optimization".to_string(),
            domain: Domain::Logistics,
            problem_type: ProblemType::TimeSeriesForecasting,
            input_features: vec![
                "historical_demand".to_string(),
                "seasonal_patterns".to_string(),
                "economic_indicators".to_string(),
                "promotional_events".to_string(),
            ],
            output_description: "Demand forecasts and optimal stock levels".to_string(),
            recommended_dataset_size: 10000,
            complexity: ModelComplexity::Intermediate,
            required_qubits: 8,
            training_time_estimate: "1-4 hours".to_string(),
            performance_notes: "Improved accuracy in demand prediction".to_string(),
        });

        self.templates.insert(Domain::Logistics, templates);
    }

    /// Register energy domain templates
    fn register_energy_templates(&mut self) {
        let mut templates = Vec::new();

        // Smart grid optimization
        templates.push(TemplateMetadata {
            name: "Smart Grid Optimization".to_string(),
            description: "Quantum optimization for smart grid energy distribution".to_string(),
            domain: Domain::Energy,
            problem_type: ProblemType::Optimization,
            input_features: vec![
                "energy_demand".to_string(),
                "renewable_generation".to_string(),
                "grid_topology".to_string(),
                "storage_capacity".to_string(),
                "pricing_signals".to_string(),
            ],
            output_description: "Optimal energy distribution plan".to_string(),
            recommended_dataset_size: 5000,
            complexity: ModelComplexity::Complex,
            required_qubits: 14,
            training_time_estimate: "2-6 hours".to_string(),
            performance_notes: "Real-time optimization capabilities".to_string(),
        });

        // Renewable energy forecasting
        templates.push(TemplateMetadata {
            name: "Renewable Energy Forecasting".to_string(),
            description: "Quantum time series models for wind/solar power prediction".to_string(),
            domain: Domain::Energy,
            problem_type: ProblemType::TimeSeriesForecasting,
            input_features: vec![
                "weather_data".to_string(),
                "historical_generation".to_string(),
                "satellite_imagery".to_string(),
                "atmospheric_conditions".to_string(),
            ],
            output_description: "Power generation forecasts".to_string(),
            recommended_dataset_size: 50000,
            complexity: ModelComplexity::Intermediate,
            required_qubits: 10,
            training_time_estimate: "2-5 hours".to_string(),
            performance_notes: "Better handling of weather uncertainty".to_string(),
        });

        // Energy trading
        templates.push(TemplateMetadata {
            name: "Energy Trading".to_string(),
            description: "Quantum reinforcement learning for energy market trading".to_string(),
            domain: Domain::Energy,
            problem_type: ProblemType::TimeSeriesForecasting,
            input_features: vec![
                "energy_prices".to_string(),
                "demand_patterns".to_string(),
                "market_fundamentals".to_string(),
                "regulatory_signals".to_string(),
            ],
            output_description: "Trading strategies and price predictions".to_string(),
            recommended_dataset_size: 20000,
            complexity: ModelComplexity::Research,
            required_qubits: 16,
            training_time_estimate: "4-10 hours".to_string(),
            performance_notes: "Adaptive to rapidly changing market conditions".to_string(),
        });

        self.templates.insert(Domain::Energy, templates);
    }

    /// Register materials science templates
    fn register_materials_templates(&mut self) {
        let mut templates = Vec::new();

        // Material property prediction
        templates.push(TemplateMetadata {
            name: "Material Property Prediction".to_string(),
            description: "Quantum neural networks for predicting material properties".to_string(),
            domain: Domain::Materials,
            problem_type: ProblemType::Regression,
            input_features: vec![
                "crystal_structure".to_string(),
                "elemental_composition".to_string(),
                "synthesis_conditions".to_string(),
                "electronic_structure".to_string(),
            ],
            output_description: "Material properties (conductivity, strength, etc.)".to_string(),
            recommended_dataset_size: 8000,
            complexity: ModelComplexity::Complex,
            required_qubits: 12,
            training_time_estimate: "2-6 hours".to_string(),
            performance_notes: "Quantum advantage in modeling electronic properties".to_string(),
        });

        // Battery material discovery
        templates.push(TemplateMetadata {
            name: "Battery Material Discovery".to_string(),
            description: "Quantum optimization for battery electrode materials".to_string(),
            domain: Domain::Materials,
            problem_type: ProblemType::Optimization,
            input_features: vec![
                "material_composition".to_string(),
                "electrode_structure".to_string(),
                "electrolyte_properties".to_string(),
                "operating_conditions".to_string(),
            ],
            output_description: "Optimal material composition and structure".to_string(),
            recommended_dataset_size: 3000,
            complexity: ModelComplexity::Research,
            required_qubits: 18,
            training_time_estimate: "4-12 hours".to_string(),
            performance_notes: "Novel materials discovery potential".to_string(),
        });

        self.templates.insert(Domain::Materials, templates);
    }

    /// Register physics domain templates
    fn register_physics_templates(&mut self) {
        let mut templates = Vec::new();

        // Particle physics event classification
        templates.push(TemplateMetadata {
            name: "Particle Physics Event Classification".to_string(),
            description: "Quantum neural networks for high-energy physics event classification"
                .to_string(),
            domain: Domain::Physics,
            problem_type: ProblemType::MultiClassification,
            input_features: vec![
                "detector_hits".to_string(),
                "particle_tracks".to_string(),
                "energy_deposits".to_string(),
                "timing_information".to_string(),
            ],
            output_description: "Event type classification".to_string(),
            recommended_dataset_size: 100000,
            complexity: ModelComplexity::Research,
            required_qubits: 16,
            training_time_estimate: "4-12 hours".to_string(),
            performance_notes: "Potential quantum advantage in pattern recognition".to_string(),
        });

        self.templates.insert(Domain::Physics, templates);
    }

    /// Register NLP domain templates
    fn register_nlp_templates(&mut self) {
        let mut templates = Vec::new();

        // Quantum text classification
        templates.push(TemplateMetadata {
            name: "Quantum Text Classification".to_string(),
            description: "Quantum neural networks for text classification tasks".to_string(),
            domain: Domain::NLP,
            problem_type: ProblemType::MultiClassification,
            input_features: vec![
                "text_embeddings".to_string(),
                "linguistic_features".to_string(),
                "semantic_features".to_string(),
            ],
            output_description: "Text category classification".to_string(),
            recommended_dataset_size: 25000,
            complexity: ModelComplexity::Intermediate,
            required_qubits: 10,
            training_time_estimate: "2-5 hours".to_string(),
            performance_notes: "Effective for small to medium vocabulary".to_string(),
        });

        self.templates.insert(Domain::NLP, templates);
    }

    /// Register vision domain templates
    fn register_vision_templates(&mut self) {
        let mut templates = Vec::new();

        // Quantum image classification
        templates.push(TemplateMetadata {
            name: "Quantum Image Classification".to_string(),
            description: "Quantum convolutional networks for image classification".to_string(),
            domain: Domain::Vision,
            problem_type: ProblemType::MultiClassification,
            input_features: vec![
                "image_pixels".to_string(),
                "texture_features".to_string(),
                "edge_features".to_string(),
            ],
            output_description: "Image class prediction".to_string(),
            recommended_dataset_size: 30000,
            complexity: ModelComplexity::Complex,
            required_qubits: 12,
            training_time_estimate: "3-8 hours".to_string(),
            performance_notes: "Quantum advantage on small image datasets".to_string(),
        });

        self.templates.insert(Domain::Vision, templates);
    }

    /// Register security domain templates
    fn register_security_templates(&mut self) {
        let mut templates = Vec::new();

        // Intrusion detection
        templates.push(TemplateMetadata {
            name: "Network Intrusion Detection".to_string(),
            description: "Quantum anomaly detection for cybersecurity".to_string(),
            domain: Domain::Security,
            problem_type: ProblemType::AnomalyDetection,
            input_features: vec![
                "network_traffic".to_string(),
                "connection_patterns".to_string(),
                "payload_features".to_string(),
                "temporal_patterns".to_string(),
            ],
            output_description: "Intrusion probability and attack type".to_string(),
            recommended_dataset_size: 40000,
            complexity: ModelComplexity::Complex,
            required_qubits: 14,
            training_time_estimate: "3-7 hours".to_string(),
            performance_notes: "High sensitivity to novel attack patterns".to_string(),
        });

        self.templates.insert(Domain::Security, templates);
    }

    /// Register climate domain templates
    fn register_climate_templates(&mut self) {
        let mut templates = Vec::new();

        // Climate modeling
        templates.push(TemplateMetadata {
            name: "Climate Pattern Recognition".to_string(),
            description: "Quantum neural networks for climate pattern analysis".to_string(),
            domain: Domain::Climate,
            problem_type: ProblemType::TimeSeriesForecasting,
            input_features: vec![
                "temperature_data".to_string(),
                "precipitation_data".to_string(),
                "atmospheric_pressure".to_string(),
                "ocean_temperatures".to_string(),
            ],
            output_description: "Climate pattern predictions".to_string(),
            recommended_dataset_size: 50000,
            complexity: ModelComplexity::Research,
            required_qubits: 16,
            training_time_estimate: "6-15 hours".to_string(),
            performance_notes: "Enhanced modeling of complex climate interactions".to_string(),
        });

        self.templates.insert(Domain::Climate, templates);
    }

    /// Register aerospace domain templates
    fn register_aerospace_templates(&mut self) {
        let mut templates = Vec::new();

        // Trajectory optimization
        templates.push(TemplateMetadata {
            name: "Spacecraft Trajectory Optimization".to_string(),
            description: "Quantum optimization for spacecraft trajectory planning".to_string(),
            domain: Domain::Aerospace,
            problem_type: ProblemType::Optimization,
            input_features: vec![
                "initial_conditions".to_string(),
                "target_orbit".to_string(),
                "gravitational_fields".to_string(),
                "fuel_constraints".to_string(),
            ],
            output_description: "Optimal trajectory and control inputs".to_string(),
            recommended_dataset_size: 1000,
            complexity: ModelComplexity::Research,
            required_qubits: 20,
            training_time_estimate: "2-8 hours".to_string(),
            performance_notes: "Potential for discovering novel trajectory solutions".to_string(),
        });

        self.templates.insert(Domain::Aerospace, templates);
    }

    /// Get templates for a specific domain
    pub fn get_domain_templates(&self, domain: &Domain) -> Option<&Vec<TemplateMetadata>> {
        self.templates.get(domain)
    }

    /// Get all available domains
    pub fn get_available_domains(&self) -> Vec<Domain> {
        self.templates.keys().cloned().collect()
    }

    /// Get a specific template by name
    pub fn get_template(&self, template_name: &str) -> Result<&TemplateMetadata> {
        self.templates
            .values()
            .flatten()
            .find(|t| t.name == template_name)
            .ok_or_else(|| {
                MLError::InvalidConfiguration(format!("Template not found: {}", template_name))
            })
    }

    /// Search templates by problem type
    pub fn search_by_problem_type(&self, problem_type: &ProblemType) -> Vec<&TemplateMetadata> {
        self.templates
            .values()
            .flatten()
            .filter(|template| {
                std::mem::discriminant(&template.problem_type)
                    == std::mem::discriminant(problem_type)
            })
            .collect()
    }

    /// Search templates by complexity
    pub fn search_by_complexity(&self, complexity: &ModelComplexity) -> Vec<&TemplateMetadata> {
        self.templates
            .values()
            .flatten()
            .filter(|template| {
                std::mem::discriminant(&template.complexity) == std::mem::discriminant(complexity)
            })
            .collect()
    }

    /// Search templates by qubit requirements
    pub fn search_by_qubits(&self, max_qubits: usize) -> Vec<&TemplateMetadata> {
        self.templates
            .values()
            .flatten()
            .filter(|template| template.required_qubits <= max_qubits)
            .collect()
    }

    /// Get template recommendations
    pub fn recommend_templates(
        &self,
        domain: Option<&Domain>,
        problem_type: Option<&ProblemType>,
        max_qubits: Option<usize>,
        complexity: Option<&ModelComplexity>,
    ) -> Vec<&TemplateMetadata> {
        let mut candidates: Vec<&TemplateMetadata> = self.templates.values().flatten().collect();

        // Filter by domain
        if let Some(domain) = domain {
            candidates.retain(|template| &template.domain == domain);
        }

        // Filter by problem type
        if let Some(problem_type) = problem_type {
            candidates.retain(|template| {
                std::mem::discriminant(&template.problem_type)
                    == std::mem::discriminant(problem_type)
            });
        }

        // Filter by qubit requirements
        if let Some(max_qubits) = max_qubits {
            candidates.retain(|template| template.required_qubits <= max_qubits);
        }

        // Filter by complexity
        if let Some(complexity) = complexity {
            candidates.retain(|template| {
                std::mem::discriminant(&template.complexity) == std::mem::discriminant(complexity)
            });
        }

        // Sort by complexity (simpler first)
        candidates.sort_by(|a, b| {
            use ModelComplexity::*;
            let order_a = match a.complexity {
                Simple => 0,
                Intermediate => 1,
                Complex => 2,
                Research => 3,
            };
            let order_b = match b.complexity {
                Simple => 0,
                Intermediate => 1,
                Complex => 2,
                Research => 3,
            };
            order_a.cmp(&order_b)
        });

        candidates
    }

    /// Create a model from a template
    pub fn create_model_from_template(
        &self,
        template_name: &str,
        config: TemplateConfig,
    ) -> Result<Box<dyn DomainModel>> {
        // Find the template
        let template = self
            .templates
            .values()
            .flatten()
            .find(|t| t.name == template_name)
            .ok_or_else(|| {
                MLError::InvalidConfiguration(format!("Template not found: {}", template_name))
            })?;

        match template.domain {
            Domain::Finance => self.create_finance_model(template, config),
            Domain::Chemistry => self.create_chemistry_model(template, config),
            Domain::Healthcare => self.create_healthcare_model(template, config),
            Domain::Logistics => self.create_logistics_model(template, config),
            Domain::Energy => self.create_energy_model(template, config),
            Domain::Materials => self.create_materials_model(template, config),
            Domain::Physics => self.create_physics_model(template, config),
            Domain::NLP => self.create_nlp_model(template, config),
            Domain::Vision => self.create_vision_model(template, config),
            Domain::Security => self.create_security_model(template, config),
            Domain::Climate => self.create_climate_model(template, config),
            Domain::Aerospace => self.create_aerospace_model(template, config),
        }
    }

    /// Create finance domain model
    fn create_finance_model(
        &self,
        template: &TemplateMetadata,
        config: TemplateConfig,
    ) -> Result<Box<dyn DomainModel>> {
        match template.name.as_str() {
            "Portfolio Optimization" => Ok(Box::new(PortfolioOptimizationModel::new(config)?)),
            "Credit Risk Assessment" => Ok(Box::new(CreditRiskModel::new(config)?)),
            "Fraud Detection" => Ok(Box::new(FraudDetectionModel::new(config)?)),
            "Algorithmic Trading" => Ok(Box::new(AlgorithmicTradingModel::new(config)?)),
            _ => Err(MLError::InvalidConfiguration(format!(
                "Unknown finance template: {}",
                template.name
            ))),
        }
    }

    /// Create chemistry domain model
    fn create_chemistry_model(
        &self,
        template: &TemplateMetadata,
        config: TemplateConfig,
    ) -> Result<Box<dyn DomainModel>> {
        match template.name.as_str() {
            "Molecular Property Prediction" => Ok(Box::new(MolecularPropertyModel::new(config)?)),
            "Drug Discovery" => Ok(Box::new(DrugDiscoveryModel::new(config)?)),
            "Catalyst Design" => Ok(Box::new(CatalystDesignModel::new(config)?)),
            _ => Err(MLError::InvalidConfiguration(format!(
                "Unknown chemistry template: {}",
                template.name
            ))),
        }
    }

    /// Create healthcare domain model
    fn create_healthcare_model(
        &self,
        template: &TemplateMetadata,
        config: TemplateConfig,
    ) -> Result<Box<dyn DomainModel>> {
        match template.name.as_str() {
            "Medical Image Analysis" => Ok(Box::new(MedicalImageModel::new(config)?)),
            "Drug Interaction Prediction" => Ok(Box::new(DrugInteractionModel::new(config)?)),
            "Genomic Analysis" => Ok(Box::new(GenomicAnalysisModel::new(config)?)),
            _ => Err(MLError::InvalidConfiguration(format!(
                "Unknown healthcare template: {}",
                template.name
            ))),
        }
    }

    /// Create logistics domain model
    fn create_logistics_model(
        &self,
        template: &TemplateMetadata,
        config: TemplateConfig,
    ) -> Result<Box<dyn DomainModel>> {
        match template.name.as_str() {
            "Vehicle Routing Optimization" => Ok(Box::new(VehicleRoutingModel::new(config)?)),
            "Supply Chain Optimization" => Ok(Box::new(SupplyChainModel::new(config)?)),
            "Inventory Management" => Ok(Box::new(InventoryManagementModel::new(config)?)),
            _ => Err(MLError::InvalidConfiguration(format!(
                "Unknown logistics template: {}",
                template.name
            ))),
        }
    }

    /// Create energy domain model
    fn create_energy_model(
        &self,
        template: &TemplateMetadata,
        config: TemplateConfig,
    ) -> Result<Box<dyn DomainModel>> {
        match template.name.as_str() {
            "Smart Grid Optimization" => Ok(Box::new(SmartGridModel::new(config)?)),
            "Renewable Energy Forecasting" => Ok(Box::new(RenewableEnergyModel::new(config)?)),
            "Energy Trading" => Ok(Box::new(EnergyTradingModel::new(config)?)),
            _ => Err(MLError::InvalidConfiguration(format!(
                "Unknown energy template: {}",
                template.name
            ))),
        }
    }

    /// Create materials domain model
    fn create_materials_model(
        &self,
        template: &TemplateMetadata,
        config: TemplateConfig,
    ) -> Result<Box<dyn DomainModel>> {
        match template.name.as_str() {
            "Material Property Prediction" => Ok(Box::new(MaterialPropertyModel::new(config)?)),
            "Battery Material Discovery" => Ok(Box::new(BatteryMaterialModel::new(config)?)),
            _ => Err(MLError::InvalidConfiguration(format!(
                "Unknown materials template: {}",
                template.name
            ))),
        }
    }

    /// Create physics domain model
    fn create_physics_model(
        &self,
        template: &TemplateMetadata,
        config: TemplateConfig,
    ) -> Result<Box<dyn DomainModel>> {
        match template.name.as_str() {
            "Particle Physics Event Classification" => {
                Ok(Box::new(ParticlePhysicsModel::new(config)?))
            }
            _ => Err(MLError::InvalidConfiguration(format!(
                "Unknown physics template: {}",
                template.name
            ))),
        }
    }

    /// Create NLP domain model
    fn create_nlp_model(
        &self,
        template: &TemplateMetadata,
        config: TemplateConfig,
    ) -> Result<Box<dyn DomainModel>> {
        match template.name.as_str() {
            "Quantum Text Classification" => Ok(Box::new(QuantumTextModel::new(config)?)),
            _ => Err(MLError::InvalidConfiguration(format!(
                "Unknown NLP template: {}",
                template.name
            ))),
        }
    }

    /// Create vision domain model
    fn create_vision_model(
        &self,
        template: &TemplateMetadata,
        config: TemplateConfig,
    ) -> Result<Box<dyn DomainModel>> {
        match template.name.as_str() {
            "Quantum Image Classification" => Ok(Box::new(QuantumImageModel::new(config)?)),
            _ => Err(MLError::InvalidConfiguration(format!(
                "Unknown vision template: {}",
                template.name
            ))),
        }
    }

    /// Create security domain model
    fn create_security_model(
        &self,
        template: &TemplateMetadata,
        config: TemplateConfig,
    ) -> Result<Box<dyn DomainModel>> {
        match template.name.as_str() {
            "Network Intrusion Detection" => Ok(Box::new(IntrusionDetectionModel::new(config)?)),
            _ => Err(MLError::InvalidConfiguration(format!(
                "Unknown security template: {}",
                template.name
            ))),
        }
    }

    /// Create climate domain model
    fn create_climate_model(
        &self,
        template: &TemplateMetadata,
        config: TemplateConfig,
    ) -> Result<Box<dyn DomainModel>> {
        match template.name.as_str() {
            "Climate Pattern Recognition" => Ok(Box::new(ClimatePatternModel::new(config)?)),
            _ => Err(MLError::InvalidConfiguration(format!(
                "Unknown climate template: {}",
                template.name
            ))),
        }
    }

    /// Create aerospace domain model
    fn create_aerospace_model(
        &self,
        template: &TemplateMetadata,
        config: TemplateConfig,
    ) -> Result<Box<dyn DomainModel>> {
        match template.name.as_str() {
            "Spacecraft Trajectory Optimization" => {
                Ok(Box::new(TrajectoryOptimizationModel::new(config)?))
            }
            _ => Err(MLError::InvalidConfiguration(format!(
                "Unknown aerospace template: {}",
                template.name
            ))),
        }
    }
}

/// Trait for domain-specific models
pub trait DomainModel: Send + Sync {
    /// Model name
    fn name(&self) -> &str;

    /// Domain
    fn domain(&self) -> Domain;

    /// Make prediction
    fn predict(&self, input: &ArrayD<f64>) -> Result<ArrayD<f64>>;

    /// Train the model
    fn train(&mut self, X: &ArrayD<f64>, y: &ArrayD<f64>) -> Result<()>;

    /// Get model configuration
    fn config(&self) -> &TemplateConfig;

    /// Get training suggestions
    fn training_suggestions(&self) -> Vec<String>;

    /// Get preprocessing requirements
    fn preprocessing_requirements(&self) -> Vec<String>;
}

// Concrete domain model implementations (simplified for brevity)

/// Portfolio optimization model
pub struct PortfolioOptimizationModel {
    config: TemplateConfig,
    qaoa_params: Vec<f64>,
}

impl PortfolioOptimizationModel {
    pub fn new(config: TemplateConfig) -> Result<Self> {
        let qaoa_params = vec![0.5; config.num_qubits * 2]; // Beta and gamma parameters
        Ok(Self {
            config,
            qaoa_params,
        })
    }
}

impl DomainModel for PortfolioOptimizationModel {
    fn name(&self) -> &str {
        "Portfolio Optimization"
    }
    fn domain(&self) -> Domain {
        Domain::Finance
    }

    fn predict(&self, input: &ArrayD<f64>) -> Result<ArrayD<f64>> {
        // Simplified portfolio optimization
        let returns = input.slice(s![..self.config.output_dim]);
        let weights = returns.mapv(|x| if x > 0.0 { 1.0 } else { 0.0 });
        let normalized_weights = &weights / weights.sum();
        Ok(normalized_weights.to_owned().into_dyn())
    }

    fn train(&mut self, _X: &ArrayD<f64>, _y: &ArrayD<f64>) -> Result<()> {
        // Placeholder for QAOA training
        Ok(())
    }

    fn config(&self) -> &TemplateConfig {
        &self.config
    }

    fn training_suggestions(&self) -> Vec<String> {
        vec![
            "Use historical return data with at least 252 trading days".to_string(),
            "Include risk-free rate and market volatility data".to_string(),
            "Consider transaction costs in the optimization".to_string(),
        ]
    }

    fn preprocessing_requirements(&self) -> Vec<String> {
        vec![
            "Normalize returns to [-1, 1] range".to_string(),
            "Remove outliers beyond 3 standard deviations".to_string(),
            "Fill missing data with forward fill method".to_string(),
        ]
    }
}

// Placeholder implementations for other domain models
macro_rules! impl_domain_model {
    ($model_name:ident, $domain:expr, $display_name:expr) => {
        pub struct $model_name {
            config: TemplateConfig,
        }

        impl $model_name {
            pub fn new(config: TemplateConfig) -> Result<Self> {
                Ok(Self { config })
            }
        }

        impl DomainModel for $model_name {
            fn name(&self) -> &str {
                $display_name
            }
            fn domain(&self) -> Domain {
                $domain
            }

            fn predict(&self, input: &ArrayD<f64>) -> Result<ArrayD<f64>> {
                // Placeholder implementation
                Ok(ArrayD::zeros(IxDyn(&[self.config.output_dim])))
            }

            fn train(&mut self, _X: &ArrayD<f64>, _y: &ArrayD<f64>) -> Result<()> {
                Ok(())
            }

            fn config(&self) -> &TemplateConfig {
                &self.config
            }

            fn training_suggestions(&self) -> Vec<String> {
                vec!["Domain-specific training suggestions".to_string()]
            }

            fn preprocessing_requirements(&self) -> Vec<String> {
                vec!["Domain-specific preprocessing requirements".to_string()]
            }
        }
    };
}

// Generate placeholder implementations for all domain models
impl_domain_model!(CreditRiskModel, Domain::Finance, "Credit Risk Assessment");
impl_domain_model!(FraudDetectionModel, Domain::Finance, "Fraud Detection");
impl_domain_model!(
    AlgorithmicTradingModel,
    Domain::Finance,
    "Algorithmic Trading"
);
impl_domain_model!(
    MolecularPropertyModel,
    Domain::Chemistry,
    "Molecular Property Prediction"
);
impl_domain_model!(DrugDiscoveryModel, Domain::Chemistry, "Drug Discovery");
impl_domain_model!(CatalystDesignModel, Domain::Chemistry, "Catalyst Design");
impl_domain_model!(
    MedicalImageModel,
    Domain::Healthcare,
    "Medical Image Analysis"
);
impl_domain_model!(
    DrugInteractionModel,
    Domain::Healthcare,
    "Drug Interaction Prediction"
);
impl_domain_model!(GenomicAnalysisModel, Domain::Healthcare, "Genomic Analysis");
impl_domain_model!(
    VehicleRoutingModel,
    Domain::Logistics,
    "Vehicle Routing Optimization"
);
impl_domain_model!(
    SupplyChainModel,
    Domain::Logistics,
    "Supply Chain Optimization"
);
impl_domain_model!(
    InventoryManagementModel,
    Domain::Logistics,
    "Inventory Management"
);
impl_domain_model!(SmartGridModel, Domain::Energy, "Smart Grid Optimization");
impl_domain_model!(
    RenewableEnergyModel,
    Domain::Energy,
    "Renewable Energy Forecasting"
);
impl_domain_model!(EnergyTradingModel, Domain::Energy, "Energy Trading");
impl_domain_model!(
    MaterialPropertyModel,
    Domain::Materials,
    "Material Property Prediction"
);
impl_domain_model!(
    BatteryMaterialModel,
    Domain::Materials,
    "Battery Material Discovery"
);
impl_domain_model!(
    ParticlePhysicsModel,
    Domain::Physics,
    "Particle Physics Event Classification"
);
impl_domain_model!(QuantumTextModel, Domain::NLP, "Quantum Text Classification");
impl_domain_model!(
    QuantumImageModel,
    Domain::Vision,
    "Quantum Image Classification"
);
impl_domain_model!(
    IntrusionDetectionModel,
    Domain::Security,
    "Network Intrusion Detection"
);
impl_domain_model!(
    ClimatePatternModel,
    Domain::Climate,
    "Climate Pattern Recognition"
);
impl_domain_model!(
    TrajectoryOptimizationModel,
    Domain::Aerospace,
    "Spacecraft Trajectory Optimization"
);

/// Utility functions for domain templates
pub mod utils {
    use super::*;

    /// Get the default template manager
    pub fn get_default_template_manager() -> DomainTemplateManager {
        DomainTemplateManager::new()
    }

    /// Print template information
    pub fn print_template_info(template: &TemplateMetadata) {
        println!("Template: {}", template.name);
        println!("Domain: {:?}", template.domain);
        println!("Description: {}", template.description);
        println!("Problem Type: {:?}", template.problem_type);
        println!("Complexity: {:?}", template.complexity);
        println!("Required Qubits: {}", template.required_qubits);
        println!("Input Features: {:?}", template.input_features);
        println!("Output: {}", template.output_description);
        println!(
            "Recommended Dataset Size: {}",
            template.recommended_dataset_size
        );
        println!("Training Time: {}", template.training_time_estimate);
        println!("Performance Notes: {}", template.performance_notes);
        println!();
    }

    /// Generate domain template report
    pub fn generate_domain_report(manager: &DomainTemplateManager) -> String {
        let mut report = String::new();
        report.push_str("Domain Template Report\n");
        report.push_str("=====================\n\n");

        for domain in manager.get_available_domains() {
            if let Some(templates) = manager.get_domain_templates(&domain) {
                report.push_str(&format!(
                    "{:?} Domain ({} templates):\n",
                    domain,
                    templates.len()
                ));
                for template in templates {
                    report.push_str(&format!(
                        "  - {}: {} qubits, {:?} complexity\n",
                        template.name, template.required_qubits, template.complexity
                    ));
                }
                report.push_str("\n");
            }
        }

        report
    }

    /// Check template compatibility with available resources
    pub fn check_template_feasibility(
        template: &TemplateMetadata,
        available_qubits: usize,
        available_time_hours: f64,
    ) -> (bool, Vec<String>) {
        let mut feasible = true;
        let mut issues = Vec::new();

        if template.required_qubits > available_qubits {
            feasible = false;
            issues.push(format!(
                "Requires {} qubits but only {} available",
                template.required_qubits, available_qubits
            ));
        }

        // Parse training time estimate
        let estimated_hours = if template.training_time_estimate.contains("minutes") {
            0.5 // Assume 30 minutes average
        } else if template.training_time_estimate.contains("hour") {
            if template.training_time_estimate.contains("-") {
                4.0 // Average of range
            } else {
                2.0 // Default
            }
        } else {
            2.0 // Default
        };

        if estimated_hours > available_time_hours {
            issues.push(format!(
                "Estimated training time {} exceeds available time {:.1} hours",
                template.training_time_estimate, available_time_hours
            ));
        }

        (feasible, issues)
    }
}

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

    #[test]
    fn test_template_manager_creation() {
        let manager = DomainTemplateManager::new();
        assert!(!manager.get_available_domains().is_empty());
    }

    #[test]
    fn test_domain_templates() {
        let manager = DomainTemplateManager::new();
        let finance_templates = manager.get_domain_templates(&Domain::Finance);
        assert!(finance_templates.is_some());
        assert!(!finance_templates
            .expect("finance_templates verified Some above")
            .is_empty());
    }

    #[test]
    fn test_template_search() {
        let manager = DomainTemplateManager::new();

        let classification_templates =
            manager.search_by_problem_type(&ProblemType::BinaryClassification);
        assert!(!classification_templates.is_empty());

        let simple_templates = manager.search_by_complexity(&ModelComplexity::Simple);
        // May be empty if no simple templates exist

        let low_qubit_templates = manager.search_by_qubits(8);
        assert!(!low_qubit_templates.is_empty());
    }

    #[test]
    fn test_template_recommendations() {
        let manager = DomainTemplateManager::new();

        let recommendations = manager.recommend_templates(
            Some(&Domain::Finance),
            Some(&ProblemType::BinaryClassification),
            Some(10),
            Some(&ModelComplexity::Intermediate),
        );

        for template in recommendations {
            assert_eq!(template.domain, Domain::Finance);
            assert!(matches!(
                template.problem_type,
                ProblemType::BinaryClassification
            ));
            assert!(template.required_qubits <= 10);
        }
    }

    #[test]
    fn test_model_creation() {
        let manager = DomainTemplateManager::new();
        let config = TemplateConfig {
            num_qubits: 10,
            input_dim: 5,
            output_dim: 3,
            parameters: HashMap::new(),
        };

        let model = manager.create_model_from_template("Portfolio Optimization", config);
        assert!(model.is_ok());

        let model = model.expect("model verified Ok above");
        assert_eq!(model.name(), "Portfolio Optimization");
        assert_eq!(model.domain(), Domain::Finance);
    }

    #[test]
    fn test_template_feasibility() {
        let manager = DomainTemplateManager::new();
        let finance_templates = manager
            .get_domain_templates(&Domain::Finance)
            .expect("Finance domain templates should exist");
        let template = &finance_templates[0];

        let (feasible, issues) = utils::check_template_feasibility(template, 20, 10.0);
        assert!(feasible || !issues.is_empty());
    }
}