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meta_learning.py

@@ -11,7 +11,7 @@ import logging
 from datetime import datetime
 from enum import Enum
 import json
-from .quantum_learning import QuantumLearningSystem, Pattern, PatternType
+from quantum_learning import QuantumLearningSystem, Pattern, PatternType
 
 class LearningStrategy(Enum):
     GRADIENT_BASED = "gradient_based"

quantum_learning.py

@@ -0,0 +1,235 @@
+"""
+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, 
+                 num_qubits: int = 8,
+                 entanglement_strength: float = 0.5,
+                 interference_threshold: float = 0.3,
+                 tunneling_rate: float = 0.1,
+                 annealing_schedule: Optional[Dict[str, Any]] = None):
+        """Initialize quantum learning system."""
+        self.num_qubits = num_qubits
+        self.entanglement_strength = entanglement_strength
+        self.interference_threshold = interference_threshold
+        self.tunneling_rate = tunneling_rate
+        self.annealing_schedule = annealing_schedule or {
+            "initial_temp": 10.0,
+            "final_temp": 0.1,
+            "cooling_rate": 0.95
+        }
+        
+        # Initialize quantum state
+        self.state = np.zeros((2**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()