""" Quantum Learning System --------------------- Implements quantum-inspired learning algorithms for enhanced pattern recognition and optimization. """ from typing import Dict, Any, List, Optional, Tuple from dataclasses import dataclass, field from enum import Enum import numpy as np from datetime import datetime class PatternType(Enum): """Types of quantum learning patterns.""" SUPERPOSITION = "superposition" ENTANGLEMENT = "entanglement" INTERFERENCE = "interference" TUNNELING = "tunneling" ANNEALING = "annealing" @dataclass class Pattern: """Quantum pattern representation.""" type: PatternType amplitude: complex phase: float entanglement_partners: List[str] interference_score: float metadata: Dict[str, Any] = field(default_factory=dict) timestamp: datetime = field(default_factory=datetime.now) class QuantumLearningSystem: """ Advanced quantum-inspired learning system that: 1. Uses quantum superposition for parallel pattern matching 2. Leverages quantum entanglement for correlated learning 3. Applies quantum interference for optimization 4. Implements quantum tunneling for escaping local optima 5. Uses quantum annealing for global optimization """ def __init__(self, config: Optional[Dict[str, Any]] = None): """Initialize quantum learning system.""" self.config = config or {} # Quantum system parameters self.num_qubits = self.config.get('num_qubits', 8) self.entanglement_strength = self.config.get('entanglement_strength', 0.5) self.interference_threshold = self.config.get('interference_threshold', 0.3) self.tunneling_rate = self.config.get('tunneling_rate', 0.1) self.annealing_schedule = self.config.get('annealing_schedule', { 'initial_temp': 1.0, 'final_temp': 0.01, 'steps': 100, 'cooling_rate': 0.95 }) # Standard reasoning parameters self.min_confidence = self.config.get('min_confidence', 0.7) self.parallel_threshold = self.config.get('parallel_threshold', 3) self.learning_rate = self.config.get('learning_rate', 0.1) self.strategy_weights = self.config.get('strategy_weights', { "LOCAL_LLM": 0.8, "CHAIN_OF_THOUGHT": 0.6, "TREE_OF_THOUGHTS": 0.5, "META_LEARNING": 0.4 }) # Initialize quantum state self.state = np.zeros((2**self.num_qubits,), dtype=complex) self.state[0] = 1.0 # Initialize to |0⟩ state # Pattern storage self.patterns: Dict[str, Pattern] = {} self.entanglement_graph: Dict[str, List[str]] = {} # Performance tracking self.interference_history: List[float] = [] self.tunneling_events: List[Dict[str, Any]] = [] self.optimization_trace: List[float] = [] def create_superposition(self, patterns: List[Pattern]) -> np.ndarray: """Create quantum superposition of patterns.""" n_patterns = len(patterns) amplitude = 1.0 / np.sqrt(n_patterns) superposition = np.zeros_like(self.state) for i, pattern in enumerate(patterns): # Convert pattern to quantum state pattern_state = self._pattern_to_quantum_state(pattern) # Add to superposition with equal amplitude superposition += amplitude * pattern_state return superposition def apply_entanglement(self, pattern1: Pattern, pattern2: Pattern) -> Tuple[Pattern, Pattern]: """Apply quantum entanglement between patterns.""" # Create entanglement between patterns if self.entanglement_strength > np.random.random(): pattern1.entanglement_partners.append(pattern2.type.value) pattern2.entanglement_partners.append(pattern1.type.value) # Update entanglement graph self.entanglement_graph.setdefault(pattern1.type.value, []).append(pattern2.type.value) self.entanglement_graph.setdefault(pattern2.type.value, []).append(pattern1.type.value) # Modify pattern properties based on entanglement shared_phase = (pattern1.phase + pattern2.phase) / 2 pattern1.phase = pattern2.phase = shared_phase return pattern1, pattern2 def measure_interference(self, patterns: List[Pattern]) -> float: """Measure quantum interference between patterns.""" total_interference = 0.0 for i, p1 in enumerate(patterns): for p2 in patterns[i+1:]: # Calculate interference based on phase difference phase_diff = abs(p1.phase - p2.phase) interference = np.cos(phase_diff) * abs(p1.amplitude * p2.amplitude) # Update interference scores p1.interference_score = p2.interference_score = interference total_interference += interference self.interference_history.append(total_interference) return total_interference def quantum_tunneling(self, pattern: Pattern, energy_landscape: Dict[str, float]) -> Pattern: """Apply quantum tunneling to escape local optima.""" current_energy = energy_landscape.get(pattern.type.value, float('inf')) # Attempt tunneling with probability based on tunneling rate if np.random.random() < self.tunneling_rate: # Find neighboring states neighbors = self._find_neighboring_states(pattern) for neighbor in neighbors: neighbor_energy = energy_landscape.get(neighbor.type.value, float('inf')) # Tunnel if found lower energy state if neighbor_energy < current_energy: self.tunneling_events.append({ "from_state": pattern.type.value, "to_state": neighbor.type.value, "energy_delta": neighbor_energy - current_energy, "timestamp": datetime.now().isoformat() }) return neighbor return pattern def quantum_annealing(self, initial_pattern: Pattern, cost_function: callable, num_steps: int = 1000) -> Pattern: """Perform quantum annealing optimization.""" current_pattern = initial_pattern current_cost = cost_function(current_pattern) temperature = self.annealing_schedule["initial_temp"] for step in range(num_steps): # Generate neighbor pattern neighbor = self._generate_neighbor_pattern(current_pattern) neighbor_cost = cost_function(neighbor) # Calculate acceptance probability delta_cost = neighbor_cost - current_cost if delta_cost < 0 or np.random.random() < np.exp(-delta_cost / temperature): current_pattern = neighbor current_cost = neighbor_cost # Update temperature temperature *= self.annealing_schedule["cooling_rate"] self.optimization_trace.append(current_cost) # Stop if temperature is too low if temperature < self.annealing_schedule["final_temp"]: break return current_pattern def _pattern_to_quantum_state(self, pattern: Pattern) -> np.ndarray: """Convert pattern to quantum state representation.""" # Create basis state based on pattern type basis_state = np.zeros_like(self.state) state_index = hash(pattern.type.value) % (2**self.num_qubits) basis_state[state_index] = 1.0 # Apply amplitude and phase return pattern.amplitude * np.exp(1j * pattern.phase) * basis_state def _find_neighboring_states(self, pattern: Pattern) -> List[Pattern]: """Find neighboring quantum states for tunneling.""" neighbors = [] current_type_index = list(PatternType).index(pattern.type) # Consider adjacent pattern types as neighbors for i in [-1, 1]: try: neighbor_type = list(PatternType)[current_type_index + i] neighbor = Pattern( type=neighbor_type, amplitude=pattern.amplitude, phase=pattern.phase + np.random.normal(0, 0.1), entanglement_partners=pattern.entanglement_partners.copy(), interference_score=pattern.interference_score ) neighbors.append(neighbor) except IndexError: continue return neighbors def _generate_neighbor_pattern(self, pattern: Pattern) -> Pattern: """Generate neighboring pattern for annealing.""" return Pattern( type=pattern.type, amplitude=pattern.amplitude + np.random.normal(0, 0.1), phase=pattern.phase + np.random.normal(0, 0.1), entanglement_partners=pattern.entanglement_partners.copy(), interference_score=pattern.interference_score, metadata=pattern.metadata.copy() ) def get_optimization_statistics(self) -> Dict[str, Any]: """Get statistics about the optimization process.""" return { "interference_history": self.interference_history, "tunneling_events": self.tunneling_events, "optimization_trace": self.optimization_trace, "entanglement_graph": self.entanglement_graph } def reset_system(self): """Reset the quantum learning system.""" self.state = np.zeros((2**self.num_qubits,), dtype=complex) self.state[0] = 1.0 self.patterns.clear() self.entanglement_graph.clear() self.interference_history.clear() self.tunneling_events.clear() self.optimization_trace.clear()