"""
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()