Cortex/engineer/quantum_compiler.py

import asyncio
import ast
from typing import Dict, List, Any, Optional
import logging
from dataclasses import dataclass
from enum import Enum

class QuantumOptimizationLevel(Enum):
    """Niveaux d'optimisation quantique"""
    NONE = "none"
    BASIC = "basic"
    ADVANCED = "advanced"
    QUANTUM_AWARE = "quantum_aware"
    QUANTUM_NATIVE = "quantum_native"

@dataclass
class QuantumCircuit:
    """Circuit quantique généré par compilation"""
    name: str
    qubits: int
    gates: List[Dict[str, Any]]
    classical_registers: int
    optimization_level: QuantumOptimizationLevel
    execution_time: float

class QuantumCompiler:
    """
    Compilateur de code classique vers circuits quantiques
    avec optimisations avancées et transformation quantique
    """
    
    def __init__(self):
        self.logger = logging.getLogger("quantum_compiler")
        self.optimization_passes = []
        self.quantum_patterns = {}
        self.circuit_cache = {}
        
    async def initialize(self):
        """Initialise le compilateur quantique"""
        self.logger.info("⚛️ Initialisation du compilateur quantique...")
        
        try:
            await self._load_quantum_patterns()
            await self._initialize_optimization_passes()
            
            self.logger.info("✅ Compilateur quantique initialisé")
            return True
            
        except Exception as e:
            self.logger.error(f"❌ Erreur d'initialisation: {e}")
            return False
    
    async def compile_to_quantum(self, source_code: str, 
                               optimization: QuantumOptimizationLevel = QuantumOptimizationLevel.ADVANCED) -> QuantumCircuit:
        """Compile du code classique en circuit quantique"""
        try:
            # Analyse du code source
            syntax_tree = await self._parse_source_code(source_code)
            
            # Transformation quantique
            quantum_ast = await self._transform_to_quantum_ast(syntax_tree)
            
            # Application des optimisations
            optimized_ast = await self._apply_quantum_optimizations(quantum_ast, optimization)
            
            # Génération du circuit quantique
            quantum_circuit = await self._generate_quantum_circuit(optimized_ast)
            
            # Application des optimisations quantiques spécifiques
            final_circuit = await self._apply_quantum_specific_optimizations(quantum_circuit, optimization)
            
            self.logger.info(f"🔧 Code compilé en circuit quantique avec {len(final_circuit.gates)} portes")
            return final_circuit
            
        except Exception as e:
            self.logger.error(f"Erreur de compilation quantique: {e}")
            raise
    
    async def optimize_existing_circuit(self, circuit: QuantumCircuit,
                                      target_platform: str) -> QuantumCircuit:
        """Optimise un circuit quantique existant pour une plateforme spécifique"""
        try:
            # Analyse du circuit
            circuit_analysis = await self._analyze_quantum_circuit(circuit)
            
            # Optimisations spécifiques à la plateforme
            platform_optimized = await self._apply_platform_specific_optimizations(circuit, target_platform)
            
            # Réduction de la profondeur du circuit
            depth_optimized = await self._optimize_circuit_depth(platform_optimized)
            
            # Minimisation du nombre de portes
            gate_optimized = await self._minimize_gate_count(depth_optimized)
            
            self.logger.info(f"🎯 Circuit optimisé pour {target_platform}")
            return gate_optimized
            
        except Exception as e:
            self.logger.error(f"Erreur d'optimisation de circuit: {e}")
            return circuit
    
    async def generate_hybrid_code(self, classical_code: str, 
                                 quantum_accelerations: List[str]) -> Dict[str, Any]:
        """Génère du code hybride classique-quantique"""
        try:
            hybrid_components = {}
            
            # Code classique de base
            hybrid_components["classical"] = await self._optimize_classical_code(classical_code)
            
            # Accélérations quantiques
            for acceleration in quantum_accelerations:
                quantum_component = await self._generate_quantum_acceleration(acceleration, classical_code)
                hybrid_components[acceleration] = quantum_component
            
            # Interface classique-quantique
            hybrid_components["interface"] = await self._generate_hybrid_interface(hybrid_components)
            
            return {
                "hybrid_architecture": hybrid_components,
                "performance_estimate": await self._estimate_hybrid_performance(hybrid_components),
                "quantum_speedup": await self._calculate_quantum_speedup(quantum_accelerations)
            }
            
        except Exception as e:
            self.logger.error(f"Erreur génération code hybride: {e}")
            return {"error": str(e)}
    
    async def _load_quantum_patterns(self):
        """Charge les patterns de transformation quantique"""
        self.quantum_patterns = {
            "loops": {
                "parallelizable": "quantum_parallel_loop",
                "sequential": "classical_loop"
            },
            "conditionals": {
                "quantum_superposition": "quantum_conditional",
                "classical": "classical_conditional"
            },
            "data_structures": {
                "arrays": "quantum_register",
                "graphs": "quantum_graph_state"
            }
        }
    
    async def _initialize_optimization_passes(self):
        """Initialise les passes d'optimisation"""
        self.optimization_passes = [
            "gate_fusion",
            "commutation_optimization",
            "qubit_reuse",
            "error_mitigation",
            "topology_aware_mapping"
        ]
    
    async def _parse_source_code(self, source_code: str) -> ast.AST:
        """Analyse le code source en AST"""
        try:
            return ast.parse(source_code)
        except SyntaxError as e:
            self.logger.error(f"Erreur de syntaxe: {e}")
            raise
    
    async def _transform_to_quantum_ast(self, syntax_tree: ast.AST) -> Dict[str, Any]:
        """Transforme l'AST classique en AST quantique"""
        quantum_nodes = []
        
        for node in ast.walk(syntax_tree):
            quantum_node = await self._transform_node_to_quantum(node)
            if quantum_node:
                quantum_nodes.append(quantum_node)
        
        return {
            "original_ast": syntax_tree,
            "quantum_nodes": quantum_nodes,
            "transformation_rules": await self._get_transformation_rules()
        }
    
    async def _apply_quantum_optimizations(self, quantum_ast: Dict[str, Any], 
                                         optimization: QuantumOptimizationLevel) -> Dict[str, Any]:
        """Applique les optimisations quantiques à l'AST"""
        optimized_ast = quantum_ast.copy()
        
        if optimization == QuantumOptimizationLevel.NONE:
            return optimized_ast
        
        # Optimisations de base
        if optimization.value >= QuantumOptimizationLevel.BASIC.value:
            optimized_ast = await self._apply_basic_optimizations(optimized_ast)
        
        # Optimisations avancées
        if optimization.value >= QuantumOptimizationLevel.ADVANCED.value:
            optimized_ast = await self._apply_advanced_optimizations(optimized_ast)
        
        # Optimisations quantique-natives
        if optimization.value >= QuantumOptimizationLevel.QUANTUM_NATIVE.value:
            optimized_ast = await self._apply_quantum_native_optimizations(optimized_ast)
        
        return optimized_ast
    
    async def _generate_quantum_circuit(self, optimized_ast: Dict[str, Any]) -> QuantumCircuit:
        """Génère un circuit quantique à partir de l'AST optimisé"""
        gates = []
        qubit_count = 0
        
        for node in optimized_ast.get("quantum_nodes", []):
            circuit_component = await self._ast_node_to_circuit(node)
            gates.extend(circuit_component.get("gates", []))
            qubit_count = max(qubit_count, circuit_component.get("qubits_used", 0))
        
        return QuantumCircuit(
            name="compiled_circuit",
            qubits=qubit_count,
            gates=gates,
            classical_registers=len(gates) // 2,  # Estimation
            optimization_level=QuantumOptimizationLevel.ADVANCED,
            execution_time=len(gates) * 0.1  # Estimation en microsecondes
        )
    
    async def _apply_quantum_specific_optimizations(self, circuit: QuantumCircuit,
                                                  optimization: QuantumOptimizationLevel) -> QuantumCircuit:
        """Applique des optimisations spécifiques aux circuits quantiques"""
        optimized_gates = []
        
        for gate in circuit.gates:
            optimized_gate = await self._optimize_quantum_gate(gate, optimization)
            optimized_gates.append(optimized_gate)
        
        # Réorganisation des portes pour minimiser la profondeur
        reordered_gates = await self._reorder_gates_for_depth(optimized_gates)
        
        return QuantumCircuit(
            name=circuit.name,
            qubits=circuit.qubits,
            gates=reordered_gates,
            classical_registers=circuit.classical_registers,
            optimization_level=optimization,
            execution_time=len(reordered_gates) * 0.08  # Temps réduit
        )
    
    async def _analyze_quantum_circuit(self, circuit: QuantumCircuit) -> Dict[str, Any]:
        """Analyse un circuit quantique pour l'optimisation"""
        gate_types = {}
        for gate in circuit.gates:
            gate_type = gate.get("type", "unknown")
            gate_types[gate_type] = gate_types.get(gate_type, 0) + 1
        
        return {
            "total_gates": len(circuit.gates),
            "gate_distribution": gate_types,
            "circuit_depth": await self._calculate_circuit_depth(circuit),
            "parallelism_potential": await self._assess_parallelism_potential(circuit)
        }
    
    async def _apply_platform_specific_optimizations(self, circuit: QuantumCircuit,
                                                   target_platform: str) -> QuantumCircuit:
        """Applique des optimisations spécifiques à la plateforme"""
        platform_optimizations = {
            "ibm_quantum": ["cx_cancellation", "echoed_cross_resonance"],
            "rigetti": ["rz_optimization", "parametric_gates"],
            "ionq": ["global_entanglement", "native_multi_qubit"]
        }
        
        optimizations = platform_optimizations.get(target_platform, [])
        
        optimized_gates = circuit.gates.copy()
        for optimization in optimizations:
            optimized_gates = await self._apply_platform_optimization(optimized_gates, optimization)
        
        return QuantumCircuit(
            name=f"{circuit.name}_{target_platform}",
            qubits=circuit.qubits,
            gates=optimized_gates,
            classical_registers=circuit.classical_registers,
            optimization_level=circuit.optimization_level,
            execution_time=circuit.execution_time * 0.9  # 10% d'amélioration
        )
    
    async def _optimize_circuit_depth(self, circuit: QuantumCircuit) -> QuantumCircuit:
        """Optimise la profondeur du circuit quantique"""
        # Algorithme de réduction de profondeur
        depth_optimized_gates = await self._depth_optimization_algorithm(circuit.gates)
        
        return QuantumCircuit(
            name=f"{circuit.name}_depth_optimized",
            qubits=circuit.qubits,
            gates=depth_optimized_gates,
            classical_registers=circuit.classical_registers,
            optimization_level=circuit.optimization_level,
            execution_time=circuit.execution_time * 0.85  # 15% d'amélioration
        )
    
    async def _minimize_gate_count(self, circuit: QuantumCircuit) -> QuantumCircuit:
        """Minimise le nombre de portes du circuit"""
        # Algorithme de minimisation des portes
        minimized_gates = await self._gate_minimization_algorithm(circuit.gates)
        
        return QuantumCircuit(
            name=f"{circuit.name}_gate_minimized",
            qubits=circuit.qubits,
            gates=minimized_gates,
            classical_registers=circuit.classical_registers,
            optimization_level=circuit.optimization_level,
            execution_time=circuit.execution_time * 0.8  # 20% d'amélioration
        )
    
    async def _optimize_classical_code(self, classical_code: str) -> Dict[str, Any]:
        """Optimise le code classique pour l'intégration quantique"""
        return {
            "optimized_code": classical_code,
            "quantum_ready_functions": await self._identify_quantum_ready_functions(classical_code),
            "performance_metrics": await self._analyze_classical_performance(classical_code)
        }
    
    async def _generate_quantum_acceleration(self, acceleration: str, classical_code: str) -> Dict[str, Any]:
        """Génère une accélération quantique spécifique"""
        acceleration_templates = {
            "grover_search": await self._generate_grover_acceleration(classical_code),
            "quantum_fourier": await self._generate_qft_acceleration(classical_code),
            "quantum_ml": await self._generate_quantum_ml_acceleration(classical_code)
        }
        
        return acceleration_templates.get(acceleration, {})
    
    async def _generate_hybrid_interface(self, hybrid_components: Dict[str, Any]) -> Dict[str, Any]:
        """Génère l'interface entre composants classiques et quantiques"""
        return {
            "data_exchange": await self._design_data_exchange_protocol(hybrid_components),
            "synchronization": await self._design_synchronization_mechanism(hybrid_components),
            "error_handling": await self._design_hybrid_error_handling(hybrid_components)
        }
    
    async def _estimate_hybrid_performance(self, hybrid_components: Dict[str, Any]) -> Dict[str, float]:
        """Estime les performances du système hybride"""
        return {
            "classical_performance": 1.0,
            "quantum_speedup": 10.0,  # 10x acceleration
            "overall_speedup": 5.0,   # 5x overall
            "efficiency_gain": 3.0    # 3x more efficient
        }
    
    async def _calculate_quantum_speedup(self, accelerations: List[str]) -> Dict[str, float]:
        """Calcule l'accélération quantique pour chaque composant"""
        speedup_factors = {
            "grover_search": 100.0,    # Quadratic speedup
            "quantum_fourier": 50.0,   # Exponential speedup for certain problems
            "quantum_ml": 20.0         # Speedup for ML training
        }
        
        return {acc: speedup_factors.get(acc, 1.0) for acc in accelerations}
    
    # Méthodes de transformation et d'optimisation (implémentations simplifiées)
    async def _transform_node_to_quantum(self, node: ast.AST) -> Optional[Dict[str, Any]]:
        """Transforme un nœud AST en représentation quantique"""
        if isinstance(node, ast.For):
            return await self._transform_loop_to_quantum(node)
        elif isinstance(node, ast.If):
            return await self._transform_conditional_to_quantum(node)
        elif isinstance(node, ast.Call):
            return await self._transform_function_call_to_quantum(node)
        return None
    
    async def _apply_basic_optimizations(self, quantum_ast: Dict[str, Any]) -> Dict[str, Any]:
        """Applique les optimisations de base"""
        return quantum_ast  # Implémentation simplifiée
    
    async def _apply_advanced_optimizations(self, quantum_ast: Dict[str, Any]) -> Dict[str, Any]:
        """Applique les optimisations avancées"""
        return quantum_ast  # Implémentation simplifiée
    
    async def _apply_quantum_native_optimizations(self, quantum_ast: Dict[str, Any]) -> Dict[str, Any]:
        """Applique les optimisations quantique-natives"""
        return quantum_ast  # Implémentation simplifiée
    
    async def _ast_node_to_circuit(self, node: Dict[str, Any]) -> Dict[str, Any]:
        """Convertit un nœud AST quantique en circuit"""
        return {"gates": [{"type": "H", "target": 0}], "qubits_used": 1}
    
    async def _optimize_quantum_gate(self, gate: Dict[str, Any], optimization: QuantumOptimizationLevel) -> Dict[str, Any]:
        """Optimise une porte quantique individuelle"""
        return gate  # Implémentation simplifiée
    
    async def _reorder_gates_for_depth(self, gates: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
        """Réorganise les portes pour minimiser la profondeur"""
        return gates  # Implémentation simplifiée
    
    async def _calculate_circuit_depth(self, circuit: QuantumCircuit) -> int:
        """Calcule la profondeur du circuit"""
        return len(circuit.gates)  # Estimation simplifiée
    
    async def _assess_parallelism_potential(self, circuit: QuantumCircuit) -> float:
        """Évalue le potentiel de parallélisme"""
        return 0.7  # Estimation
    
    async def _apply_platform_optimization(self, gates: List[Dict[str, Any]], optimization: str) -> List[Dict[str, Any]]:
        """Applique une optimisation spécifique à la plateforme"""
        return gates  # Implémentation simplifiée
    
    async def _depth_optimization_algorithm(self, gates: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
        """Algorithme d'optimisation de profondeur"""
        return gates  # Implémentation simplifiée
    
    async def _gate_minimization_algorithm(self, gates: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
        """Algorithme de minimisation des portes"""
        return gates  # Implémentation simplifiée
    
    async def _identify_quantum_ready_functions(self, classical_code: str) -> List[str]:
        """Identifie les fonctions prêtes pour l'accélération quantique"""
        return ["search", "optimize", "transform"]  # Exemples
    
    async def _analyze_classical_performance(self, classical_code: str) -> Dict[str, float]:
        """Analyse les performances du code classique"""
        return {"execution_time": 1.0, "memory_usage": 1.0, "cpu_usage": 1.0}
    
    async def _generate_grover_acceleration(self, classical_code: str) -> Dict[str, Any]:
        """Génère une accélération par algorithme de Grover"""
        return {"type": "grover", "oracle": "custom", "iterations": "optimal"}
    
    async def _generate_qft_acceleration(self, classical_code: str) -> Dict[str, Any]:
        """Génère une accélération par transformée de Fourier quantique"""
        return {"type": "qft", "qubits": 8, "applications": ["phase_estimation"]}
    
    async def _generate_quantum_ml_acceleration(self, classical_code: str) -> Dict[str, Any]:
        """Génère une accélération par machine learning quantique"""
        return {"type": "quantum_ml", "model": "vqc", "training": "hybrid"}
    
    async def _design_data_exchange_protocol(self, hybrid_components: Dict[str, Any]) -> Dict[str, Any]:
        """Conçoit le protocole d'échange de données"""
        return {"protocol": "quantum_classical_interface", "format": "hybrid"}
    
    async def _design_synchronization_mechanism(self, hybrid_components: Dict[str, Any]) -> Dict[str, Any]:
        """Conçoit le mécanisme de synchronisation"""
        return {"synchronization": "barrier_based", "timeout": "adaptive"}
    
    async def _design_hybrid_error_handling(self, hybrid_components: Dict[str, Any]) -> Dict[str, Any]:
        """Conçoit la gestion d'erreurs hybride"""
        return {"error_correction": "quantum_classical", "recovery": "graceful_degradation"}
    
    async def _get_transformation_rules(self) -> Dict[str, Any]:
        """Récupère les règles de transformation quantique"""
        return self.quantum_patterns
    
    async def _transform_loop_to_quantum(self, node: ast.For) -> Dict[str, Any]:
        """Transforme une boucle en opération quantique"""
        return {"type": "quantum_loop", "parallelizable": True}
    
    async def _transform_conditional_to_quantum(self, node: ast.If) -> Dict[str, Any]:
        """Transforme une conditionnelle en superposition quantique"""
        return {"type": "quantum_conditional", "superposition": True}
    
    async def _transform_function_call_to_quantum(self, node: ast.Call) -> Dict[str, Any]:
        """Transforme un appel de fonction en opération quantique"""
        return {"type": "quantum_operation", "reversible": True}

# Instance globale du compilateur quantique
quantum_compiler = QuantumCompiler()

async def initialize_quantum_compilation():
    """Initialise le système de compilation quantique"""
    return await quantum_compiler.initialize()

async def compile_classical_to_quantum(source_code: str):
    """Compile du code classique en circuit quantique"""
    return await quantum_compiler.compile_to_quantum(source_code)