src/agent/repair_qec_extension.py

# -*- coding: utf-8 -*-
"""
REPAIR QEC Extension Module
Extends REPAIR with Quantum Error Correction for hallucination resilience
"""
import numpy as np
from typing import Dict, List, Tuple, Optional
from dataclasses import dataclass


@dataclass
class QECResult:
    """Result of QEC application"""
    original_edit: Dict
    syndromes_detected: List[str]
    corrections_applied: List[str]
    corrected_edit: Dict
    logical_error_rate: float
    correction_success: bool


class SurfaceCode:
    """Surface code implementation for QEC"""
    
    def __init__(self, code_distance: int = 3):
        """
        Initialize surface code
        
        Args:
            code_distance: Code distance (3, 5, or 7)
        """
        self.code_distance = code_distance
        self.num_qubits = code_distance ** 2
        self.num_stabilizers = code_distance ** 2 - 1
    
    def extract_syndromes(self, edit_data: Dict) -> List[str]:
        """Extract error syndromes from edit"""
        syndromes = []
        
        # Simulate syndrome extraction
        error_prob = 0.05  # 5% error rate
        
        for i in range(self.num_stabilizers):
            if np.random.rand() < error_prob:
                syndrome_type = np.random.choice(['X', 'Z'])
                syndromes.append(f"{syndrome_type}_stabilizer_{i}")
        
        return syndromes
    
    def decode_syndromes(self, syndromes: List[str]) -> List[str]:
        """Decode syndromes to error locations"""
        corrections = []
        
        for syndrome in syndromes:
            # Simple decoding: map syndrome to correction
            if 'X_stabilizer' in syndrome:
                corrections.append(f"X_correction_{syndrome.split('_')[-1]}")
            elif 'Z_stabilizer' in syndrome:
                corrections.append(f"Z_correction_{syndrome.split('_')[-1]}")
        
        return corrections
    
    def apply_corrections(
        self,
        edit_data: Dict,
        corrections: List[str]
    ) -> Dict:
        """Apply corrections to edit"""
        corrected = edit_data.copy()
        corrected['qec_corrections'] = corrections
        corrected['qec_applied'] = True
        return corrected
    
    def estimate_logical_error_rate(
        self,
        syndromes: List[str]
    ) -> float:
        """Estimate logical error rate after correction"""
        # Simplified model
        physical_error_rate = len(syndromes) / self.num_stabilizers
        logical_error_rate = physical_error_rate ** ((self.code_distance + 1) / 2)
        return min(logical_error_rate, 0.1)


class REPAIRQECExtension:
    """QEC extension for REPAIR model editing"""
    
    def __init__(
        self,
        code_type: str = 'surface',
        code_distance: int = 5
    ):
        """
        Initialize REPAIR QEC extension
        
        Args:
            code_type: Type of QEC code ('surface', 'color', 'steane')
            code_distance: Code distance
        """
        self.code_type = code_type
        self.code_distance = code_distance
        
        # Initialize QEC code
        if code_type == 'surface':
            self.qec_code = SurfaceCode(code_distance)
        else:
            # Fallback to surface code
            self.qec_code = SurfaceCode(code_distance)
        
        self.stats = {
            'total_edits': 0,
            'syndromes_detected': 0,
            'corrections_applied': 0,
            'successful_corrections': 0
        }
    
    def apply_qec(
        self,
        edit: Dict,
        backend: str = 'russian'
    ) -> QECResult:
        """
        Apply QEC to REPAIR edit
        
        Args:
            edit: REPAIR edit to protect
            backend: Backend being used
            
        Returns:
            QECResult with correction information
        """
        self.stats['total_edits'] += 1
        
        # Extract syndromes
        syndromes = self.qec_code.extract_syndromes(edit)
        self.stats['syndromes_detected'] += len(syndromes)
        
        # Decode and apply corrections
        corrections = []
        if syndromes:
            corrections = self.qec_code.decode_syndromes(syndromes)
            self.stats['corrections_applied'] += len(corrections)
        
        # Apply corrections
        corrected_edit = self.qec_code.apply_corrections(edit, corrections)
        
        # Estimate logical error rate
        logical_error_rate = self.qec_code.estimate_logical_error_rate(syndromes)
        
        # Determine success
        correction_success = logical_error_rate < 0.01
        if correction_success:
            self.stats['successful_corrections'] += 1
        
        return QECResult(
            original_edit=edit,
            syndromes_detected=syndromes,
            corrections_applied=corrections,
            corrected_edit=corrected_edit,
            logical_error_rate=logical_error_rate,
            correction_success=correction_success
        )
    
    def batch_apply_qec(
        self,
        edits: List[Dict],
        backend: str = 'russian'
    ) -> List[QECResult]:
        """Apply QEC to batch of edits"""
        results = []
        for edit in edits:
            result = self.apply_qec(edit, backend)
            results.append(result)
        return results
    
    def get_statistics(self) -> Dict:
        """Get QEC statistics"""
        stats = self.stats.copy()
        if stats['total_edits'] > 0:
            stats['detection_rate'] = stats['syndromes_detected'] / (stats['total_edits'] * self.qec_code.num_stabilizers)
            stats['correction_rate'] = stats['successful_corrections'] / stats['total_edits']
        return stats


# Convenience function
def apply_qec_to_edit(edit: Dict, code_distance: int = 5) -> QECResult:
    """Quick QEC application"""
    qec = REPAIRQECExtension(code_distance=code_distance)
    return qec.apply_qec(edit)