reasoning/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, StrategyResult

class QuantumOperationType(Enum):
    """Types of quantum operations."""
    HADAMARD = "hadamard"
    CNOT = "cnot"
    PHASE = "phase"
    MEASURE = "measure"
    ENTANGLE = "entangle"

@dataclass
class QuantumState:
    """Quantum state with superposition and entanglement."""
    name: str
    amplitude: complex
    phase: float
    entangled_states: List[str] = field(default_factory=list)
    timestamp: str = field(default_factory=lambda: datetime.now().isoformat())

@dataclass
class QuantumOperation:
    """Quantum operation applied to states."""
    type: QuantumOperationType
    target_states: List[str]
    parameters: Dict[str, Any]
    timestamp: str = field(default_factory=lambda: datetime.now().isoformat())

@dataclass
class QuantumMeasurement:
    """Result of quantum measurement."""
    state: str
    probability: float
    outcome: Any
    timestamp: str = field(default_factory=lambda: datetime.now().isoformat())

class QuantumStrategy(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)
        
        # 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)
        
        # Performance metrics
        self.performance_metrics = {
            'states_created': 0,
            'operations_applied': 0,
            'measurements_made': 0,
            'successful_operations': 0,
            'failed_operations': 0,
            'avg_state_fidelity': 0.0,
            'operation_distribution': defaultdict(int),
            'measurement_distribution': defaultdict(float),
            'total_qubits_used': 0,
            'total_entanglements': 0
        }
    
    async def reason(
        self,
        query: str,
        context: Dict[str, Any]
    ) -> StrategyResult:
        """
        Apply quantum reasoning to analyze the query.
        
        Args:
            query: The query to reason about
            context: Additional context and parameters
            
        Returns:
            StrategyResult containing the reasoning output and metadata
        """
        try:
            # Initialize quantum states
            states = await self._initialize_states(query, context)
            self.performance_metrics['states_created'] = len(states)
            self.performance_metrics['total_qubits_used'] = sum(
                len(s.entangled_states) + 1 for s in states
            )
            
            # Apply quantum operations
            operations = await self._apply_operations(states, context)
            self.performance_metrics['operations_applied'] = len(operations)
            
            # Update operation distribution
            for op in operations:
                self.performance_metrics['operation_distribution'][op.type.value] += 1
                
            # Perform measurements
            measurements = await self._measure_states(states, context)
            self.performance_metrics['measurements_made'] = len(measurements)
            
            # Update measurement distribution
            for m in measurements:
                self.performance_metrics['measurement_distribution'][m.state] = m.probability
            
            # Analyze results
            result = await self._analyze_results(measurements, context)
            
            # Build reasoning trace
            reasoning_trace = self._build_reasoning_trace(
                states, operations, measurements, result
            )
            
            # Calculate confidence
            confidence = self._calculate_confidence(measurements)
            
            if confidence >= self.min_confidence:
                return StrategyResult(
                    strategy_type="quantum",
                    success=True,
                    answer=result.get('conclusion'),
                    confidence=confidence,
                    reasoning_trace=reasoning_trace,
                    metadata={
                        'num_states': len(states),
                        'num_operations': len(operations),
                        'num_measurements': len(measurements),
                        'quantum_parameters': {
                            'num_qubits': self.num_qubits,
                            'decoherence_rate': self.decoherence_rate
                        }
                    },
                    performance_metrics=self.performance_metrics
                )
            
            return StrategyResult(
                strategy_type="quantum",
                success=False,
                answer=None,
                confidence=confidence,
                reasoning_trace=reasoning_trace,
                metadata={'error': 'Insufficient confidence in results'},
                performance_metrics=self.performance_metrics
            )
            
        except Exception as e:
            logging.error(f"Quantum reasoning error: {str(e)}")
            return StrategyResult(
                strategy_type="quantum",
                success=False,
                answer=None,
                confidence=0.0,
                reasoning_trace=[{
                    'step': 'error',
                    'error': str(e),
                    'timestamp': datetime.now().isoformat()
                }],
                metadata={'error': str(e)},
                performance_metrics=self.performance_metrics
            )
    
    async def _initialize_states(
        self,
        query: str,
        context: Dict[str, Any]
    ) -> List[QuantumState]:
        """Initialize quantum states from query."""
        states = []
        
        # Create initial state
        initial_state = QuantumState(
            name="initial",
            amplitude=complex(1.0, 0.0),
            phase=0.0
        )
        states.append(initial_state)
        
        # Create superposition states
        for i in range(self.num_qubits - 1):
            state = QuantumState(
                name=f"superposition_{i}",
                amplitude=complex(1.0 / np.sqrt(2), 0.0),
                phase=np.pi / 2,
                entangled_states=[initial_state.name]
            )
            states.append(state)
            self.performance_metrics['total_entanglements'] += 1
        
        return states
    
    async def _apply_operations(
        self,
        states: List[QuantumState],
        context: Dict[str, Any]
    ) -> List[QuantumOperation]:
        """Apply quantum operations to states."""
        operations = []
        
        for state in states:
            # Apply Hadamard gate
            operations.append(QuantumOperation(
                type=QuantumOperationType.HADAMARD,
                target_states=[state.name],
                parameters={'angle': np.pi / 2}
            ))
            
            # Apply CNOT if entangled
            if state.entangled_states:
                operations.append(QuantumOperation(
                    type=QuantumOperationType.CNOT,
                    target_states=[state.name] + state.entangled_states,
                    parameters={}
                ))
            
            # Apply phase rotation
            operations.append(QuantumOperation(
                type=QuantumOperationType.PHASE,
                target_states=[state.name],
                parameters={'phase': state.phase}
            ))
            
            # Track success/failure
            success = np.random.random() > self.decoherence_rate
            if success:
                self.performance_metrics['successful_operations'] += 1
            else:
                self.performance_metrics['failed_operations'] += 1
        
        return operations
    
    async def _measure_states(
        self,
        states: List[QuantumState],
        context: Dict[str, Any]
    ) -> List[QuantumMeasurement]:
        """Measure quantum states."""
        measurements = []
        
        for state in states:
            # Calculate measurement probability
            probability = abs(state.amplitude) ** 2
            
            # Apply measurement threshold
            if probability > self.measurement_threshold:
                measurements.append(QuantumMeasurement(
                    state=state.name,
                    probability=probability,
                    outcome=1 if probability > 0.5 else 0
                ))
        
        return measurements
    
    async def _analyze_results(
        self,
        measurements: List[QuantumMeasurement],
        context: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Analyze measurement results."""
        if not measurements:
            return {'conclusion': None, 'confidence': 0.0}
        
        # Calculate weighted outcome
        total_probability = sum(m.probability for m in measurements)
        weighted_outcome = sum(
            m.probability * m.outcome for m in measurements
        ) / total_probability if total_probability > 0 else 0
        
        return {
            'conclusion': f"Quantum analysis suggests outcome: {weighted_outcome:.2f}",
            'confidence': total_probability / len(measurements)
        }
    
    def _calculate_confidence(
        self,
        measurements: List[QuantumMeasurement]
    ) -> float:
        """Calculate overall confidence score."""
        if not measurements:
            return 0.0
        
        # Base confidence from measurements
        confidence = sum(m.probability for m in measurements) / len(measurements)
        
        # Adjust for decoherence
        confidence *= (1 - self.decoherence_rate)
        
        # Adjust for operation success rate
        total_ops = (
            self.performance_metrics['successful_operations'] +
            self.performance_metrics['failed_operations']
        )
        if total_ops > 0:
            success_rate = (
                self.performance_metrics['successful_operations'] / total_ops
            )
            confidence *= success_rate
        
        return min(confidence, 1.0)
    
    def _build_reasoning_trace(
        self,
        states: List[QuantumState],
        operations: List[QuantumOperation],
        measurements: List[QuantumMeasurement],
        result: Dict[str, Any]
    ) -> List[Dict[str, Any]]:
        """Build the reasoning trace for quantum processing."""
        trace = []
        
        # State initialization step
        trace.append({
            'step': 'state_initialization',
            'states': [
                {
                    'name': s.name,
                    'amplitude': abs(s.amplitude),
                    'phase': s.phase,
                    'entangled': len(s.entangled_states)
                }
                for s in states
            ],
            'timestamp': datetime.now().isoformat()
        })
        
        # Operation application step
        trace.append({
            'step': 'operation_application',
            'operations': [
                {
                    'type': o.type.value,
                    'targets': o.target_states,
                    'parameters': o.parameters
                }
                for o in operations
            ],
            'timestamp': datetime.now().isoformat()
        })
        
        # Measurement step
        trace.append({
            'step': 'measurement',
            'measurements': [
                {
                    'state': m.state,
                    'probability': m.probability,
                    'outcome': m.outcome
                }
                for m in measurements
            ],
            'timestamp': datetime.now().isoformat()
        })
        
        # Result analysis step
        trace.append({
            'step': 'result_analysis',
            'result': result,
            'timestamp': datetime.now().isoformat()
        })
        
        return trace


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)}