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nananie143
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Browse files- meta_learning.py +1 -1
- 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
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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"
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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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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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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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@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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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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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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# 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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# 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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# 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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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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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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return superposition
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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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# 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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# 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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return pattern1, pattern2
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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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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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# 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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self.interference_history.append(total_interference)
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return total_interference
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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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# 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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for neighbor in neighbors:
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neighbor_energy = energy_landscape.get(neighbor.type.value, float('inf'))
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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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return pattern
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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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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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# 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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# 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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# 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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return current_pattern
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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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# Apply amplitude and phase
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return pattern.amplitude * np.exp(1j * pattern.phase) * basis_state
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def _find_neighboring_states(self, pattern: Pattern) -> List[Pattern]:
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"""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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# 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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return neighbors
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def _generate_neighbor_pattern(self, pattern: Pattern) -> Pattern:
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"""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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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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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()
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