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  1. meta_learning.py +1 -1
  2. quantum_learning.py +235 -0
meta_learning.py CHANGED
@@ -11,7 +11,7 @@ import logging
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  from datetime import datetime
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  from enum import Enum
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  import json
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- from .quantum_learning import QuantumLearningSystem, Pattern, PatternType
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  class LearningStrategy(Enum):
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  GRADIENT_BASED = "gradient_based"
 
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  from datetime import datetime
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  from enum import Enum
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  import json
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+ from quantum_learning import QuantumLearningSystem, Pattern, PatternType
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  class LearningStrategy(Enum):
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  GRADIENT_BASED = "gradient_based"
quantum_learning.py ADDED
@@ -0,0 +1,235 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ """
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+ Quantum Learning System
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+ ---------------------
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+ Implements quantum-inspired learning algorithms for enhanced pattern recognition
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+ and optimization.
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+ """
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+
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+ from typing import Dict, Any, List, Optional, Tuple
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+ from dataclasses import dataclass, field
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+ from enum import Enum
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+ import numpy as np
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+ from datetime import datetime
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+
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+ class PatternType(Enum):
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+ """Types of quantum learning patterns."""
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+ SUPERPOSITION = "superposition"
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+ ENTANGLEMENT = "entanglement"
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+ INTERFERENCE = "interference"
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+ TUNNELING = "tunneling"
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+ ANNEALING = "annealing"
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+
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+ @dataclass
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+ class Pattern:
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+ """Quantum pattern representation."""
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+ type: PatternType
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+ amplitude: complex
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+ phase: float
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+ entanglement_partners: List[str]
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+ interference_score: float
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+ metadata: Dict[str, Any] = field(default_factory=dict)
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+ timestamp: datetime = field(default_factory=datetime.now)
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+
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+ class QuantumLearningSystem:
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+ """
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+ Advanced quantum-inspired learning system that:
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+ 1. Uses quantum superposition for parallel pattern matching
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+ 2. Leverages quantum entanglement for correlated learning
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+ 3. Applies quantum interference for optimization
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+ 4. Implements quantum tunneling for escaping local optima
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+ 5. Uses quantum annealing for global optimization
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+ """
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+
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+ def __init__(self,
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+ num_qubits: int = 8,
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+ entanglement_strength: float = 0.5,
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+ interference_threshold: float = 0.3,
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+ tunneling_rate: float = 0.1,
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+ annealing_schedule: Optional[Dict[str, Any]] = None):
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+ """Initialize quantum learning system."""
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+ self.num_qubits = num_qubits
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+ self.entanglement_strength = entanglement_strength
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+ self.interference_threshold = interference_threshold
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+ self.tunneling_rate = tunneling_rate
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+ self.annealing_schedule = annealing_schedule or {
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+ "initial_temp": 10.0,
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+ "final_temp": 0.1,
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+ "cooling_rate": 0.95
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+ }
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+
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+ # Initialize quantum state
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+ self.state = np.zeros((2**num_qubits,), dtype=complex)
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+ self.state[0] = 1.0 # Initialize to |0⟩ state
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+
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+ # Pattern storage
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+ self.patterns: Dict[str, Pattern] = {}
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+ self.entanglement_graph: Dict[str, List[str]] = {}
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+
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+ # Performance tracking
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+ self.interference_history: List[float] = []
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+ self.tunneling_events: List[Dict[str, Any]] = []
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+ self.optimization_trace: List[float] = []
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+
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+ def create_superposition(self, patterns: List[Pattern]) -> np.ndarray:
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+ """Create quantum superposition of patterns."""
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+ n_patterns = len(patterns)
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+ amplitude = 1.0 / np.sqrt(n_patterns)
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+
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+ superposition = np.zeros_like(self.state)
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+ for i, pattern in enumerate(patterns):
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+ # Convert pattern to quantum state
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+ pattern_state = self._pattern_to_quantum_state(pattern)
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+ # Add to superposition with equal amplitude
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+ superposition += amplitude * pattern_state
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+
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+ return superposition
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+
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+ def apply_entanglement(self, pattern1: Pattern, pattern2: Pattern) -> Tuple[Pattern, Pattern]:
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+ """Apply quantum entanglement between patterns."""
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+ # Create entanglement between patterns
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+ if self.entanglement_strength > np.random.random():
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+ pattern1.entanglement_partners.append(pattern2.type.value)
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+ pattern2.entanglement_partners.append(pattern1.type.value)
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+
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+ # Update entanglement graph
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+ self.entanglement_graph.setdefault(pattern1.type.value, []).append(pattern2.type.value)
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+ self.entanglement_graph.setdefault(pattern2.type.value, []).append(pattern1.type.value)
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+
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+ # Modify pattern properties based on entanglement
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+ shared_phase = (pattern1.phase + pattern2.phase) / 2
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+ pattern1.phase = pattern2.phase = shared_phase
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+
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+ return pattern1, pattern2
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+
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+ def measure_interference(self, patterns: List[Pattern]) -> float:
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+ """Measure quantum interference between patterns."""
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+ total_interference = 0.0
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+
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+ for i, p1 in enumerate(patterns):
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+ for p2 in patterns[i+1:]:
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+ # Calculate interference based on phase difference
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+ phase_diff = abs(p1.phase - p2.phase)
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+ interference = np.cos(phase_diff) * abs(p1.amplitude * p2.amplitude)
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+
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+ # Update interference scores
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+ p1.interference_score = p2.interference_score = interference
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+ total_interference += interference
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+
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+ self.interference_history.append(total_interference)
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+ return total_interference
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+
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+ def quantum_tunneling(self, pattern: Pattern, energy_landscape: Dict[str, float]) -> Pattern:
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+ """Apply quantum tunneling to escape local optima."""
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+ current_energy = energy_landscape.get(pattern.type.value, float('inf'))
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+
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+ # Attempt tunneling with probability based on tunneling rate
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+ if np.random.random() < self.tunneling_rate:
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+ # Find neighboring states
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+ neighbors = self._find_neighboring_states(pattern)
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+
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+ for neighbor in neighbors:
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+ neighbor_energy = energy_landscape.get(neighbor.type.value, float('inf'))
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+
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+ # Tunnel if found lower energy state
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+ if neighbor_energy < current_energy:
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+ self.tunneling_events.append({
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+ "from_state": pattern.type.value,
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+ "to_state": neighbor.type.value,
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+ "energy_delta": neighbor_energy - current_energy,
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+ "timestamp": datetime.now().isoformat()
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+ })
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+ return neighbor
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+
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+ return pattern
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+
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+ def quantum_annealing(self,
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+ initial_pattern: Pattern,
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+ cost_function: callable,
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+ num_steps: int = 1000) -> Pattern:
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+ """Perform quantum annealing optimization."""
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+ current_pattern = initial_pattern
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+ current_cost = cost_function(current_pattern)
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+ temperature = self.annealing_schedule["initial_temp"]
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+
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+ for step in range(num_steps):
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+ # Generate neighbor pattern
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+ neighbor = self._generate_neighbor_pattern(current_pattern)
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+ neighbor_cost = cost_function(neighbor)
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+
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+ # Calculate acceptance probability
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+ delta_cost = neighbor_cost - current_cost
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+ if delta_cost < 0 or np.random.random() < np.exp(-delta_cost / temperature):
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+ current_pattern = neighbor
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+ current_cost = neighbor_cost
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+
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+ # Update temperature
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+ temperature *= self.annealing_schedule["cooling_rate"]
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+ self.optimization_trace.append(current_cost)
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+
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+ # Stop if temperature is too low
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+ if temperature < self.annealing_schedule["final_temp"]:
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+ break
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+
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+ return current_pattern
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+
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+ def _pattern_to_quantum_state(self, pattern: Pattern) -> np.ndarray:
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+ """Convert pattern to quantum state representation."""
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+ # Create basis state based on pattern type
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+ basis_state = np.zeros_like(self.state)
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+ state_index = hash(pattern.type.value) % (2**self.num_qubits)
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+ basis_state[state_index] = 1.0
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+
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+ # Apply amplitude and phase
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+ return pattern.amplitude * np.exp(1j * pattern.phase) * basis_state
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+
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+ def _find_neighboring_states(self, pattern: Pattern) -> List[Pattern]:
186
+ """Find neighboring quantum states for tunneling."""
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+ neighbors = []
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+ current_type_index = list(PatternType).index(pattern.type)
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+
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+ # Consider adjacent pattern types as neighbors
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+ for i in [-1, 1]:
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+ try:
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+ neighbor_type = list(PatternType)[current_type_index + i]
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+ neighbor = Pattern(
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+ type=neighbor_type,
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+ amplitude=pattern.amplitude,
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+ phase=pattern.phase + np.random.normal(0, 0.1),
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+ entanglement_partners=pattern.entanglement_partners.copy(),
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+ interference_score=pattern.interference_score
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+ )
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+ neighbors.append(neighbor)
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+ except IndexError:
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+ continue
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+
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+ return neighbors
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+
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+ def _generate_neighbor_pattern(self, pattern: Pattern) -> Pattern:
208
+ """Generate neighboring pattern for annealing."""
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+ return Pattern(
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+ type=pattern.type,
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+ amplitude=pattern.amplitude + np.random.normal(0, 0.1),
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+ phase=pattern.phase + np.random.normal(0, 0.1),
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+ entanglement_partners=pattern.entanglement_partners.copy(),
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+ interference_score=pattern.interference_score,
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+ metadata=pattern.metadata.copy()
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+ )
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+
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+ def get_optimization_statistics(self) -> Dict[str, Any]:
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+ """Get statistics about the optimization process."""
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+ return {
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+ "interference_history": self.interference_history,
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+ "tunneling_events": self.tunneling_events,
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+ "optimization_trace": self.optimization_trace,
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+ "entanglement_graph": self.entanglement_graph
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+ }
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+
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+ def reset_system(self):
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+ """Reset the quantum learning system."""
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+ self.state = np.zeros((2**self.num_qubits,), dtype=complex)
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+ self.state[0] = 1.0
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+ self.patterns.clear()
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+ self.entanglement_graph.clear()
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+ self.interference_history.clear()
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+ self.tunneling_events.clear()
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+ self.optimization_trace.clear()