# -*- 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