quantum_context_engine.py

# -*- coding: utf-8 -*-
"""

Stage 3: Context Engineering → Quantum Contextuality



Classical context windows collapse ambiguity. Quantum contextuality 

preserves multiple interpretations through superposition and adaptive

context collapse based on feedback.

"""

import numpy as np
from typing import Dict, List, Tuple, Optional, Any, Union
import torch
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
from qiskit.quantum_info import Statevector, partial_trace
from qiskit_aer import AerSimulator
import pennylane as qml
from pennylane import numpy as pnp
import logging
from collections import defaultdict

logger = logging.getLogger(__name__)

class QuantumContextEngine:
    """

    Quantum-enhanced context engineering for multilingual AI.

    

    Encodes context as quantum superpositions preserving cultural nuance

    and polysemy, with adaptive context collapse based on feedback.

    """
    
    def __init__(self, max_context_qubits: int = 20, cultural_dimensions: int = 8):
        """Initialize quantum context engine."""
        self.max_context_qubits = max_context_qubits
        self.cultural_dimensions = cultural_dimensions
        self.simulator = AerSimulator()
        
        # Context state management
        self.context_superpositions = {}
        self.cultural_embeddings = {}
        self.polysemy_maps = {}
        self.feedback_history = []
        
        # PennyLane device for variational circuits
        self.dev = qml.device('default.qubit', wires=max_context_qubits)
        
        logger.info(f"Initialized QuantumContextEngine with {max_context_qubits} qubits, {cultural_dimensions} cultural dimensions")
    
    def encode_context_superposition(self, context_text: str, language: str, 

                                   cultural_context: Dict[str, float] = None) -> QuantumCircuit:
        """

        Encode context as quantum superposition preserving multiple interpretations.

        

        Args:

            context_text: Input text context

            language: Language of the context

            cultural_context: Cultural dimension weights

            

        Returns:

            Quantum circuit encoding context superposition

        """
        # Tokenize and encode context
        tokens = context_text.lower().split()[:self.max_context_qubits]
        num_qubits = min(len(tokens), self.max_context_qubits)
        
        qreg = QuantumRegister(num_qubits, 'context')
        circuit = QuantumCircuit(qreg)
        
        # Create superposition for each token
        for i, token in enumerate(tokens[:num_qubits]):
            circuit.h(qreg[i])
            
            # Encode token-specific phase
            token_phase = (hash(token) % 1000) / 1000 * 2 * np.pi
            circuit.rz(token_phase, qreg[i])
        
        # Language-specific encoding
        language_phases = {
            'indonesian': np.pi/6,
            'arabic': np.pi/4,
            'spanish': np.pi/3,
            'english': np.pi/2
        }
        lang_phase = language_phases.get(language, np.pi/4)
        
        for i in range(num_qubits):
            circuit.ry(lang_phase, qreg[i])
        
        # Cultural context encoding
        if cultural_context:
            for i, (dimension, weight) in enumerate(cultural_context.items()):
                if i < num_qubits:
                    circuit.rz(weight * np.pi, qreg[i])
        
        # Create entanglement for contextual relationships
        for i in range(num_qubits - 1):
            circuit.cx(qreg[i], qreg[i + 1])
        
        self.context_superpositions[f"{language}_{hash(context_text)}"] = circuit
        logger.info(f"Encoded context superposition for {language}: {num_qubits} qubits")
        
        return circuit
    
    def encode_polysemy(self, word: str, meanings: List[str], language: str) -> QuantumCircuit:
        """

        Encode polysemous words as quantum superposition of meanings.

        

        Args:

            word: Polysemous word

            meanings: List of possible meanings

            language: Language context

            

        Returns:

            Quantum circuit encoding polysemy

        """
        num_meanings = min(len(meanings), self.max_context_qubits)
        qreg = QuantumRegister(num_meanings, 'meanings')
        circuit = QuantumCircuit(qreg)
        
        # Create uniform superposition of meanings
        for i in range(num_meanings):
            circuit.h(qreg[i])
        
        # Encode meaning-specific phases
        for i, meaning in enumerate(meanings[:num_meanings]):
            meaning_phase = (hash(meaning) % 1000) / 1000 * 2 * np.pi
            circuit.rz(meaning_phase, qreg[i])
        
        # Language-specific modulation
        lang_weight = hash(language) % 100 / 100
        for i in range(num_meanings):
            circuit.ry(lang_weight * np.pi, qreg[i])
        
        polysemy_key = f"{word}_{language}"
        self.polysemy_maps[polysemy_key] = {
            'circuit': circuit,
            'meanings': meanings[:num_meanings],
            'word': word,
            'language': language
        }
        
        logger.info(f"Encoded polysemy for '{word}' in {language}: {num_meanings} meanings")
        return circuit
    
    def cultural_nuance_embedding(self, text: str, source_culture: str, 

                                target_culture: str) -> Dict[str, Any]:
        """

        Create quantum embedding preserving cultural nuances across cultures.

        

        Args:

            text: Input text

            source_culture: Source cultural context

            target_culture: Target cultural context

            

        Returns:

            Quantum cultural embedding

        """
        # Cultural dimension mappings with comprehensive coverage
        cultural_dimensions = {
            'indonesian': {
                'collectivism': 0.8, 'hierarchy': 0.7, 'context': 0.9, 'harmony': 0.8,
                'relationship_focus': 0.9, 'indirect_communication': 0.8, 'respect': 0.9
            },
            'arabic': {
                'collectivism': 0.7, 'hierarchy': 0.8, 'context': 0.8, 'honor': 0.9,
                'family_centrality': 0.9, 'tradition': 0.8, 'hospitality': 0.9
            },
            'spanish': {
                'collectivism': 0.6, 'hierarchy': 0.6, 'context': 0.7, 'family': 0.8,
                'warmth': 0.8, 'expressiveness': 0.7, 'personal_relationships': 0.8
            },
            'english': {
                'individualism': 0.8, 'directness': 0.7, 'efficiency': 0.8, 'innovation': 0.7,
                'pragmatism': 0.8, 'competition': 0.7, 'time_orientation': 0.8
            },
            'chinese': {
                'collectivism': 0.9, 'hierarchy': 0.9, 'context': 0.9, 'harmony': 0.9,
                'face_saving': 0.9, 'long_term_orientation': 0.9, 'guanxi': 0.8, 'filial_piety': 0.9
            }
        }
        
        source_dims = cultural_dimensions.get(source_culture, {})
        target_dims = cultural_dimensions.get(target_culture, {})
        
        # Create quantum circuit for cultural embedding
        num_qubits = min(self.cultural_dimensions, self.max_context_qubits)
        qreg = QuantumRegister(num_qubits, 'culture')
        circuit = QuantumCircuit(qreg)
        
        # Initialize superposition
        for i in range(num_qubits):
            circuit.h(qreg[i])
        
        # Encode source culture
        for i, (dim, value) in enumerate(list(source_dims.items())[:num_qubits]):
            circuit.ry(value * np.pi, qreg[i])
        
        # Create cultural entanglement
        for i in range(num_qubits - 1):
            circuit.cx(qreg[i], qreg[i + 1])
        
        # Target culture transformation
        for i, (dim, value) in enumerate(list(target_dims.items())[:num_qubits]):
            if i < num_qubits:
                circuit.rz(value * np.pi, qreg[i])
        
        # Measure cultural embedding
        circuit.measure_all()
        
        job = self.simulator.run(circuit, shots=1024)
        result = job.result()
        counts = result.get_counts()
        
        # Extract cultural features
        total_shots = sum(counts.values())
        cultural_distribution = {state: count/total_shots for state, count in counts.items()}
        
        embedding = {
            'source_culture': source_culture,
            'target_culture': target_culture,
            'cultural_distribution': cultural_distribution,
            'dominant_pattern': max(cultural_distribution.keys(), key=cultural_distribution.get),
            'cultural_entropy': -sum(p * np.log2(p + 1e-10) for p in cultural_distribution.values()),
            'cross_cultural_similarity': self._calculate_cultural_similarity(source_dims, target_dims)
        }
        
        embedding_key = f"{source_culture}_{target_culture}_{hash(text)}"
        self.cultural_embeddings[embedding_key] = embedding
        
        logger.info(f"Created cultural embedding: {source_culture} → {target_culture}")
        return embedding
    
    def adaptive_context_collapse(self, context_key: str, feedback: Dict[str, float],

                                user_preference: str = None) -> Dict[str, Any]:
        """

        Adaptively collapse context superposition based on feedback.

        

        Args:

            context_key: Key identifying the context superposition

            feedback: User feedback scores for different interpretations

            user_preference: Preferred interpretation direction

            

        Returns:

            Collapsed context with selected interpretation

        """
        if context_key not in self.context_superpositions:
            logger.warning(f"Context key {context_key} not found")
            return {}
        
        circuit = self.context_superpositions[context_key].copy()
        
        # Add measurement based on feedback
        num_qubits = circuit.num_qubits
        creg = ClassicalRegister(num_qubits, 'collapsed')
        circuit.add_register(creg)
        
        # Apply feedback-weighted rotations before measurement
        for i, (interpretation, score) in enumerate(feedback.items()):
            if i < num_qubits:
                # Higher score = more likely to measure |1⟩
                rotation_angle = score * np.pi / 2
                circuit.ry(rotation_angle, circuit.qregs[0][i])
        
        # Measure all qubits
        circuit.measure(circuit.qregs[0], creg)
        
        # Execute measurement
        job = self.simulator.run(circuit, shots=1024)
        result = job.result()
        counts = result.get_counts()
        
        # Select most probable interpretation
        most_probable = max(counts.keys(), key=counts.get)
        probability = counts[most_probable] / sum(counts.values())
        
        collapsed_context = {
            'original_key': context_key,
            'collapsed_state': most_probable,
            'collapse_probability': probability,
            'measurement_counts': counts,
            'feedback_applied': feedback,
            'collapse_entropy': -sum((c/sum(counts.values())) * np.log2(c/sum(counts.values()) + 1e-10) 
                                   for c in counts.values())
        }
        
        # Store feedback for learning
        self.feedback_history.append({
            'context_key': context_key,
            'feedback': feedback,
            'result': collapsed_context,
            'timestamp': len(self.feedback_history)
        })
        
        logger.info(f"Collapsed context {context_key} with probability {probability:.3f}")
        return collapsed_context
    
    @qml.qnode(device=None)
    def quantum_context_circuit(self, params: pnp.ndarray, context_encoding: List[float]) -> float:
        """

        Variational quantum circuit for context processing.

        

        Args:

            params: Circuit parameters

            context_encoding: Encoded context features

            

        Returns:

            Context relevance score

        """
        # Encode context
        qml.AmplitudeEmbedding(features=context_encoding, wires=range(len(context_encoding)))
        
        # Variational layers
        for layer in range(3):
            for qubit in range(len(context_encoding)):
                qml.RY(params[layer * len(context_encoding) + qubit], wires=qubit)
            
            # Entangling gates
            for qubit in range(len(context_encoding) - 1):
                qml.CNOT(wires=[qubit, qubit + 1])
        
        return qml.expval(qml.PauliZ(0))
    
    def quantum_context_adaptation(self, contexts: List[str], languages: List[str],

                                 adaptation_target: str) -> Dict[str, Any]:
        """

        Adapt contexts across languages using quantum processing.

        

        Args:

            contexts: List of context strings

            languages: Corresponding languages

            adaptation_target: Target adaptation goal

            

        Returns:

            Adapted context results

        """
        # Set device for quantum node
        self.quantum_context_circuit.device = self.dev
        
        adapted_results = {}
        
        for context, language in zip(contexts, languages):
            # Encode context as quantum features
            tokens = context.lower().split()[:8]  # Limit for quantum processing
            context_encoding = np.zeros(8)
            
            for i, token in enumerate(tokens):
                if i < 8:
                    context_encoding[i] = (hash(token) % 1000) / 1000
            
            # Normalize encoding
            context_encoding = context_encoding / (np.linalg.norm(context_encoding) + 1e-10)
            
            # Initialize parameters
            num_params = 3 * len(context_encoding)
            params = pnp.random.random(num_params, requires_grad=True)
            
            # Optimize for adaptation target
            optimizer = qml.AdamOptimizer(stepsize=0.1)
            
            for step in range(50):
                params, cost = optimizer.step_and_cost(
                    lambda p: -self.quantum_context_circuit(p, context_encoding), params
                )
            
            # Get final adapted score
            adapted_score = self.quantum_context_circuit(params, context_encoding)
            
            adapted_results[f"{language}_{hash(context)}"] = {
                'original_context': context,
                'language': language,
                'adapted_score': float(adapted_score),
                'quantum_params': params.tolist(),
                'adaptation_target': adaptation_target
            }
        
        logger.info(f"Quantum context adaptation completed for {len(contexts)} contexts")
        return adapted_results
    
    def _calculate_cultural_similarity(self, culture1: Dict[str, float], 

                                     culture2: Dict[str, float]) -> float:
        """Calculate similarity between cultural dimension vectors."""
        common_dims = set(culture1.keys()) & set(culture2.keys())
        if not common_dims:
            return 0.0
        
        vec1 = np.array([culture1[dim] for dim in common_dims])
        vec2 = np.array([culture2[dim] for dim in common_dims])
        
        # Cosine similarity
        similarity = np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2) + 1e-10)
        return float(similarity)
    
    def get_quantum_context_metrics(self) -> Dict[str, Any]:
        """Get comprehensive metrics for quantum context processing."""
        metrics = {
            'max_context_qubits': self.max_context_qubits,
            'cultural_dimensions': self.cultural_dimensions,
            'context_superpositions_created': len(self.context_superpositions),
            'polysemy_maps_created': len(self.polysemy_maps),
            'cultural_embeddings_created': len(self.cultural_embeddings),
            'feedback_interactions': len(self.feedback_history),
            'quantum_context_advantage': 2 ** self.max_context_qubits  # Exponential state space
        }
        
        # Analyze feedback patterns
        if self.feedback_history:
            feedback_scores = []
            for feedback in self.feedback_history:
                scores = list(feedback['feedback'].values())
                if scores:
                    feedback_scores.extend(scores)
            
            if feedback_scores:
                metrics['average_feedback_score'] = np.mean(feedback_scores)
                metrics['feedback_variance'] = np.var(feedback_scores)
        
        # Cultural embedding analysis
        if self.cultural_embeddings:
            similarities = [emb['cross_cultural_similarity'] for emb in self.cultural_embeddings.values()]
            metrics['average_cultural_similarity'] = np.mean(similarities)
            metrics['cultural_diversity'] = np.var(similarities)
        
        return metrics