"""Quantum-inspired reasoning implementations."""
import logging
from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple
import json
from dataclasses import dataclass, field
from enum import Enum
from datetime import datetime
import numpy as np
from collections import defaultdict
from .base import ReasoningStrategy
@dataclass
class QuantumState:
"""Quantum state with superposition and entanglement."""
name: str
amplitude: complex
phase: float
entangled_states: List[str] = field(default_factory=list)
class QuantumReasoning(ReasoningStrategy):
"""
Advanced quantum reasoning that:
1. Creates quantum states
2. Applies quantum operations
3. Measures outcomes
4. Handles superposition
5. Models entanglement
"""
def __init__(self, config: Optional[Dict[str, Any]] = None):
"""Initialize quantum reasoning."""
super().__init__()
self.config = config or {}
# 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
})
# Configure quantum parameters
self.num_qubits = self.config.get('num_qubits', 3)
self.measurement_threshold = self.config.get('measurement_threshold', 0.1)
self.decoherence_rate = self.config.get('decoherence_rate', 0.01)
async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
"""
Apply quantum reasoning to analyze complex decisions.
Args:
query: The input query to reason about
context: Additional context and parameters
Returns:
Dict containing reasoning results and confidence scores
"""
try:
# Initialize quantum states
states = await self._initialize_states(query, context)
# Apply quantum operations
evolved_states = await self._apply_operations(states, context)
# Measure outcomes
measurements = await self._measure_states(evolved_states, context)
# Generate analysis
analysis = await self._generate_analysis(measurements, context)
return {
'answer': self._format_analysis(analysis),
'confidence': self._calculate_confidence(measurements),
'states': states,
'evolved_states': evolved_states,
'measurements': measurements,
'analysis': analysis
}
except Exception as e:
logging.error(f"Quantum reasoning failed: {str(e)}")
return {
'error': f"Quantum reasoning failed: {str(e)}",
'confidence': 0.0
}
async def _initialize_states(
self,
query: str,
context: Dict[str, Any]
) -> List[QuantumState]:
"""Initialize quantum states."""
states = []
# Extract key terms for state initialization
terms = set(query.lower().split())
# Create quantum states based on terms
for i, term in enumerate(terms):
if i >= self.num_qubits:
break
# Calculate initial amplitude and phase
amplitude = 1.0 / np.sqrt(len(terms[:self.num_qubits]))
phase = 2 * np.pi * i / len(terms[:self.num_qubits])
states.append(QuantumState(
name=term,
amplitude=complex(amplitude * np.cos(phase), amplitude * np.sin(phase)),
phase=phase
))
# Create entangled states if specified
if context.get('entangle', False):
self._entangle_states(states)
return states
async def _apply_operations(
self,
states: List[QuantumState],
context: Dict[str, Any]
) -> List[QuantumState]:
"""Apply quantum operations to states."""
evolved_states = []
# Get operation parameters
rotation = context.get('rotation', 0.0)
phase_shift = context.get('phase_shift', 0.0)
for state in states:
# Apply rotation
rotated_amplitude = state.amplitude * np.exp(1j * rotation)
# Apply phase shift
shifted_phase = (state.phase + phase_shift) % (2 * np.pi)
# Apply decoherence
decohered_amplitude = rotated_amplitude * (1 - self.decoherence_rate)
evolved_states.append(QuantumState(
name=state.name,
amplitude=decohered_amplitude,
phase=shifted_phase,
entangled_states=state.entangled_states.copy()
))
return evolved_states
async def _measure_states(
self,
states: List[QuantumState],
context: Dict[str, Any]
) -> Dict[str, float]:
"""Measure quantum states."""
measurements = {}
# Calculate total probability
total_probability = sum(
abs(state.amplitude) ** 2
for state in states
)
if total_probability > 0:
# Normalize and store measurements
for state in states:
probability = (abs(state.amplitude) ** 2) / total_probability
if probability > self.measurement_threshold:
measurements[state.name] = probability
return measurements
def _entangle_states(self, states: List[QuantumState]) -> None:
"""Create entanglement between states."""
if len(states) < 2:
return
# Simple entanglement: connect adjacent states
for i in range(len(states) - 1):
states[i].entangled_states.append(states[i + 1].name)
states[i + 1].entangled_states.append(states[i].name)
async def _generate_analysis(
self,
measurements: Dict[str, float],
context: Dict[str, Any]
) -> Dict[str, Any]:
"""Generate quantum analysis."""
# Sort states by measurement probability
ranked_states = sorted(
measurements.items(),
key=lambda x: x[1],
reverse=True
)
# Calculate quantum statistics
amplitudes = list(measurements.values())
mean = np.mean(amplitudes) if amplitudes else 0
std = np.std(amplitudes) if amplitudes else 0
# Calculate quantum entropy
entropy = -sum(
p * np.log2(p) if p > 0 else 0
for p in measurements.values()
)
return {
'top_state': ranked_states[0][0] if ranked_states else '',
'probability': ranked_states[0][1] if ranked_states else 0,
'alternatives': [
{'name': name, 'probability': prob}
for name, prob in ranked_states[1:]
],
'statistics': {
'mean': mean,
'std': std,
'entropy': entropy
}
}
def _format_analysis(self, analysis: Dict[str, Any]) -> str:
"""Format analysis into readable text."""
sections = []
# Top quantum state
if analysis['top_state']:
sections.append(
f"Most probable quantum state: {analysis['top_state']} "
f"(probability: {analysis['probability']:.2%})"
)
# Alternative states
if analysis['alternatives']:
sections.append("\nAlternative quantum states:")
for alt in analysis['alternatives']:
sections.append(
f"- {alt['name']}: {alt['probability']:.2%}"
)
# Quantum statistics
stats = analysis['statistics']
sections.append("\nQuantum statistics:")
sections.append(f"- Mean amplitude: {stats['mean']:.2%}")
sections.append(f"- Standard deviation: {stats['std']:.2%}")
sections.append(f"- Quantum entropy: {stats['entropy']:.2f} bits")
return "\n".join(sections)
def _calculate_confidence(self, measurements: Dict[str, float]) -> float:
"""Calculate overall confidence score."""
if not measurements:
return 0.0
# Base confidence
confidence = 0.5
# Adjust based on measurement distribution
probs = list(measurements.values())
# Strong leading measurement increases confidence
max_prob = max(probs)
if max_prob > 0.8:
confidence += 0.3
elif max_prob > 0.6:
confidence += 0.2
elif max_prob > 0.4:
confidence += 0.1
# Low entropy (clear distinction) increases confidence
entropy = -sum(p * np.log2(p) if p > 0 else 0 for p in probs)
max_entropy = -np.log2(1/len(probs)) # Maximum possible entropy
if entropy < 0.3 * max_entropy:
confidence += 0.2
elif entropy < 0.6 * max_entropy:
confidence += 0.1
return min(confidence, 1.0)
class QuantumInspiredStrategy(ReasoningStrategy):
"""Implements Quantum-Inspired reasoning."""
async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
try:
# Create a clean context for serialization
clean_context = {k: v for k, v in context.items() if k != "groq_api"}
prompt = f"""
You are a meta-learning reasoning system that adapts its approach based on problem characteristics.
Problem Type:
Query: {query}
Context: {json.dumps(clean_context)}
Analyze this problem using meta-learning principles. Structure your response EXACTLY as follows:
PROBLEM ANALYSIS:
- [First key aspect or complexity factor]
- [Second key aspect or complexity factor]
- [Third key aspect or complexity factor]
SOLUTION PATHS:
- Path 1: [Specific solution approach]
- Path 2: [Alternative solution approach]
- Path 3: [Another alternative approach]
META INSIGHTS:
- Learning 1: [Key insight about the problem space]
- Learning 2: [Key insight about solution approaches]
- Learning 3: [Key insight about trade-offs]
CONCLUSION:
[Final synthesized solution incorporating meta-learnings]
"""
response = await context["groq_api"].predict(prompt)
if not response["success"]:
return response
# Parse response into components
lines = response["answer"].split("\n")
problem_analysis = []
solution_paths = []
meta_insights = []
conclusion = ""
section = None
for line in lines:
line = line.strip()
if not line:
continue
if "PROBLEM ANALYSIS:" in line:
section = "analysis"
elif "SOLUTION PATHS:" in line:
section = "paths"
elif "META INSIGHTS:" in line:
section = "insights"
elif "CONCLUSION:" in line:
section = "conclusion"
elif line.startswith("-"):
content = line.lstrip("- ").strip()
if section == "analysis":
problem_analysis.append(content)
elif section == "paths":
solution_paths.append(content)
elif section == "insights":
meta_insights.append(content)
elif section == "conclusion":
conclusion += line + " "
return {
"success": True,
"problem_analysis": problem_analysis,
"solution_paths": solution_paths,
"meta_insights": meta_insights,
"conclusion": conclusion.strip(),
# Add standard fields for compatibility
"reasoning_path": problem_analysis + solution_paths + meta_insights,
"conclusion": conclusion.strip()
}
except Exception as e:
return {"success": False, "error": str(e)}