space/quantum.py

"""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)}