quantum_learning.py

"""
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()