# -*- coding: utf-8 -*- """ Multilingual Quantum Processor for Enhanced Language Support Specialized quantum processing for Indonesian, Arabic, Spanish, English, and Chinese with language-specific semantic and cultural encoding. """ import numpy as np from typing import Dict, List, Tuple, Optional, Any, Union import logging from qiskit import QuantumCircuit, QuantumRegister from qiskit_aer import AerSimulator import re logger = logging.getLogger(__name__) class MultilingualQuantumProcessor: """ Enhanced multilingual quantum processor with specialized handling for Indonesian, Arabic, Spanish, English, and Chinese languages. """ def __init__(self, max_qubits: int = 24): """Initialize multilingual quantum processor.""" self.max_qubits = max_qubits self.simulator = AerSimulator() # Language-specific configurations self.language_configs = { 'indonesian': { 'script': 'latin', 'direction': 'ltr', 'tonal': False, 'agglutinative': True, 'cultural_weight': 0.8, 'quantum_phase': np.pi/6, 'entanglement_pattern': 'community_based' }, 'arabic': { 'script': 'arabic', 'direction': 'rtl', 'tonal': False, 'semitic': True, 'cultural_weight': 0.9, 'quantum_phase': np.pi/4, 'entanglement_pattern': 'hierarchical_honor' }, 'spanish': { 'script': 'latin', 'direction': 'ltr', 'tonal': False, 'romance': True, 'cultural_weight': 0.7, 'quantum_phase': np.pi/3, 'entanglement_pattern': 'family_centered' }, 'english': { 'script': 'latin', 'direction': 'ltr', 'tonal': False, 'germanic': True, 'cultural_weight': 0.6, 'quantum_phase': np.pi/2, 'entanglement_pattern': 'individualistic' }, 'chinese': { 'script': 'hanzi', 'direction': 'ltr', 'tonal': True, 'logographic': True, 'cultural_weight': 0.95, 'quantum_phase': np.pi/5, 'entanglement_pattern': 'hierarchical_harmony' } } # Cultural dimension quantum encodings self.cultural_quantum_encodings = { 'collectivism': {'indonesian': 0.8, 'arabic': 0.7, 'spanish': 0.6, 'english': 0.2, 'chinese': 0.9}, 'hierarchy': {'indonesian': 0.7, 'arabic': 0.8, 'spanish': 0.6, 'english': 0.4, 'chinese': 0.9}, 'context_dependency': {'indonesian': 0.9, 'arabic': 0.8, 'spanish': 0.7, 'english': 0.5, 'chinese': 0.9}, 'harmony_orientation': {'indonesian': 0.8, 'arabic': 0.6, 'spanish': 0.7, 'english': 0.4, 'chinese': 0.9}, 'time_orientation': {'indonesian': 0.6, 'arabic': 0.7, 'spanish': 0.5, 'english': 0.8, 'chinese': 0.9}, 'relationship_focus': {'indonesian': 0.9, 'arabic': 0.8, 'spanish': 0.8, 'english': 0.5, 'chinese': 0.9} } logger.info("Initialized MultilingualQuantumProcessor with 5-language support") def detect_language_features(self, text: str, language: str) -> Dict[str, Any]: """ Detect and encode language-specific features for quantum processing. Args: text: Input text language: Language identifier Returns: Language feature encoding """ config = self.language_configs.get(language, self.language_configs['english']) features = { 'language': language, 'script_type': config['script'], 'text_direction': config['direction'], 'is_tonal': config['tonal'], 'cultural_weight': config['cultural_weight'] } # Language-specific feature detection if language == 'chinese': features.update(self._analyze_chinese_features(text)) elif language == 'arabic': features.update(self._analyze_arabic_features(text)) elif language == 'indonesian': features.update(self._analyze_indonesian_features(text)) elif language == 'spanish': features.update(self._analyze_spanish_features(text)) elif language == 'english': features.update(self._analyze_english_features(text)) return features def _analyze_chinese_features(self, text: str) -> Dict[str, Any]: """Analyze Chinese-specific linguistic features.""" features = { 'character_count': len([c for c in text if '\u4e00' <= c <= '\u9fff']), 'tone_complexity': 0.9, # High tonal complexity 'logographic_density': len(text) / max(len(text.split()), 1), 'cultural_concepts': self._detect_chinese_cultural_concepts(text), 'harmony_indicators': self._detect_harmony_concepts(text, 'chinese'), 'hierarchy_markers': self._detect_hierarchy_markers(text, 'chinese') } return features def _analyze_arabic_features(self, text: str) -> Dict[str, Any]: """Analyze Arabic-specific linguistic features.""" features = { 'arabic_chars': len([c for c in text if '\u0600' <= c <= '\u06ff']), 'rtl_complexity': 0.8, 'semitic_patterns': self._detect_semitic_patterns(text), 'honor_concepts': self._detect_honor_concepts(text), 'family_references': self._detect_family_concepts(text, 'arabic'), 'religious_context': self._detect_religious_context(text) } return features def _analyze_indonesian_features(self, text: str) -> Dict[str, Any]: """Analyze Indonesian-specific linguistic features.""" features = { 'agglutination_level': self._measure_agglutination(text), 'community_focus': self._detect_community_concepts(text), 'respect_markers': self._detect_respect_markers(text, 'indonesian'), 'harmony_emphasis': self._detect_harmony_concepts(text, 'indonesian'), 'collective_pronouns': self._count_collective_pronouns(text, 'indonesian') } return features def _analyze_spanish_features(self, text: str) -> Dict[str, Any]: """Analyze Spanish-specific linguistic features.""" features = { 'romance_patterns': self._detect_romance_patterns(text), 'family_centrality': self._detect_family_concepts(text, 'spanish'), 'emotional_expression': self._measure_emotional_expression(text), 'formality_level': self._detect_formality_level(text, 'spanish'), 'regional_variations': self._detect_regional_markers(text) } return features def _analyze_english_features(self, text: str) -> Dict[str, Any]: """Analyze English-specific linguistic features.""" features = { 'germanic_base': self._detect_germanic_patterns(text), 'directness_level': self._measure_directness(text), 'individual_focus': self._detect_individual_concepts(text), 'efficiency_markers': self._detect_efficiency_concepts(text), 'innovation_language': self._detect_innovation_concepts(text) } return features def create_multilingual_quantum_circuit(self, texts: Dict[str, str]) -> QuantumCircuit: """ Create quantum circuit encoding multiple languages simultaneously. Args: texts: Dictionary of language -> text mappings Returns: Quantum circuit with multilingual encoding """ num_languages = len(texts) qubits_per_lang = self.max_qubits // num_languages qreg = QuantumRegister(self.max_qubits, 'multilingual') circuit = QuantumCircuit(qreg) # Initialize superposition for all languages for i in range(self.max_qubits): circuit.h(qreg[i]) qubit_offset = 0 for language, text in texts.items(): if qubit_offset + qubits_per_lang > self.max_qubits: break # Get language features features = self.detect_language_features(text, language) config = self.language_configs[language] # Encode language-specific quantum state for i in range(qubits_per_lang): qubit_idx = qubit_offset + i # Base language phase circuit.rz(config['quantum_phase'], qreg[qubit_idx]) # Cultural weight encoding cultural_angle = features['cultural_weight'] * np.pi circuit.ry(cultural_angle, qreg[qubit_idx]) # Feature-specific encoding if language == 'chinese': # Encode tonal and logographic features tone_angle = features.get('tone_complexity', 0) * np.pi / 4 circuit.rz(tone_angle, qreg[qubit_idx]) elif language == 'arabic': # Encode RTL and semitic features rtl_angle = features.get('rtl_complexity', 0) * np.pi / 3 circuit.ry(rtl_angle, qreg[qubit_idx]) # Create language-specific entanglement patterns self._apply_entanglement_pattern(circuit, qreg, qubit_offset, qubits_per_lang, config['entanglement_pattern']) qubit_offset += qubits_per_lang # Cross-language entanglement for cultural alignment self._create_cross_language_entanglement(circuit, qreg, texts) logger.info(f"Created multilingual quantum circuit for {len(texts)} languages") return circuit def _apply_entanglement_pattern(self, circuit: QuantumCircuit, qreg: QuantumRegister, offset: int, length: int, pattern: str): """Apply language-specific entanglement patterns.""" if pattern == 'community_based': # Indonesian: Community-focused circular entanglement for i in range(length - 1): circuit.cx(qreg[offset + i], qreg[offset + i + 1]) if length > 2: circuit.cx(qreg[offset + length - 1], qreg[offset]) elif pattern == 'hierarchical_honor': # Arabic: Honor-based hierarchical entanglement for level in range(int(np.log2(length)) + 1): for i in range(0, length, 2**(level+1)): if offset + i + 2**level < offset + length: circuit.cx(qreg[offset + i], qreg[offset + i + 2**level]) elif pattern == 'family_centered': # Spanish: Family-centered star pattern center = offset + length // 2 for i in range(length): if offset + i != center: circuit.cx(qreg[center], qreg[offset + i]) elif pattern == 'individualistic': # English: Individual-focused minimal entanglement for i in range(0, length - 1, 2): if offset + i + 1 < offset + length: circuit.cx(qreg[offset + i], qreg[offset + i + 1]) elif pattern == 'hierarchical_harmony': # Chinese: Hierarchical harmony with balanced structure # Create balanced tree structure for level in range(int(np.log2(length))): step = 2**(level + 1) for i in range(0, length, step): if offset + i + step//2 < offset + length: circuit.cx(qreg[offset + i], qreg[offset + i + step//2]) def _create_cross_language_entanglement(self, circuit: QuantumCircuit, qreg: QuantumRegister, texts: Dict[str, str]): """Create entanglement between different languages based on cultural similarity.""" languages = list(texts.keys()) qubits_per_lang = self.max_qubits // len(languages) # Calculate cultural similarity and create proportional entanglement for i, lang1 in enumerate(languages): for j, lang2 in enumerate(languages[i+1:], i+1): similarity = self._calculate_cultural_similarity(lang1, lang2) if similarity > 0.5: # Only entangle culturally similar languages # Entangle representative qubits qubit1 = i * qubits_per_lang qubit2 = j * qubits_per_lang if qubit1 < self.max_qubits and qubit2 < self.max_qubits: circuit.cx(qreg[qubit1], qreg[qubit2]) # Add phase based on similarity strength phase = similarity * np.pi / 2 circuit.rz(phase, qreg[qubit1]) circuit.rz(phase, qreg[qubit2]) def _calculate_cultural_similarity(self, lang1: str, lang2: str) -> float: """Calculate cultural similarity between two languages.""" if lang1 not in self.cultural_quantum_encodings['collectivism']: return 0.0 if lang2 not in self.cultural_quantum_encodings['collectivism']: return 0.0 similarities = [] for dimension, values in self.cultural_quantum_encodings.items(): val1 = values[lang1] val2 = values[lang2] similarity = 1.0 - abs(val1 - val2) similarities.append(similarity) return np.mean(similarities) # Helper methods for feature detection def _detect_chinese_cultural_concepts(self, text: str) -> int: """Detect Chinese cultural concepts in text.""" concepts = ['和谐', '面子', '关系', '孝顺', '中庸', '礼', '仁', '义'] return sum(1 for concept in concepts if concept in text) def _detect_harmony_concepts(self, text: str, language: str) -> int: """Detect harmony-related concepts.""" harmony_words = { 'chinese': ['和谐', '平衡', '协调'], 'indonesian': ['harmoni', 'keseimbangan', 'rukun'], 'arabic': ['انسجام', 'توازن', 'وئام'], 'spanish': ['armonía', 'equilibrio', 'concordia'], 'english': ['harmony', 'balance', 'peace'] } words = harmony_words.get(language, []) return sum(1 for word in words if word.lower() in text.lower()) def _detect_hierarchy_markers(self, text: str, language: str) -> int: """Detect hierarchical markers in text.""" hierarchy_words = { 'chinese': ['上级', '下级', '领导', '权威'], 'arabic': ['رئيس', 'مرؤوس', 'سلطة', 'قائد'], 'indonesian': ['atasan', 'bawahan', 'pemimpin', 'otoritas'], 'spanish': ['jefe', 'subordinado', 'líder', 'autoridad'], 'english': ['boss', 'subordinate', 'leader', 'authority'] } words = hierarchy_words.get(language, []) return sum(1 for word in words if word.lower() in text.lower()) def _detect_semitic_patterns(self, text: str) -> float: """Detect Semitic language patterns in Arabic text.""" # Simplified pattern detection arabic_pattern_count = len(re.findall(r'[\u0600-\u06ff]{3,}', text)) return min(1.0, arabic_pattern_count / max(len(text.split()), 1)) def _detect_honor_concepts(self, text: str) -> int: """Detect honor-related concepts in Arabic text.""" honor_words = ['شرف', 'كرامة', 'عزة', 'مروءة'] return sum(1 for word in honor_words if word in text) def _detect_family_concepts(self, text: str, language: str) -> int: """Detect family-related concepts.""" family_words = { 'arabic': ['عائلة', 'أسرة', 'أهل', 'قبيلة'], 'spanish': ['familia', 'parientes', 'hogar', 'clan'], 'indonesian': ['keluarga', 'sanak', 'rumah', 'klan'], 'english': ['family', 'relatives', 'home', 'clan'], 'chinese': ['家庭', '家族', '亲戚', '家'] } words = family_words.get(language, []) return sum(1 for word in words if word.lower() in text.lower()) def _detect_religious_context(self, text: str) -> int: """Detect religious context in Arabic text.""" religious_words = ['الله', 'إسلام', 'مسجد', 'صلاة', 'قرآن'] return sum(1 for word in religious_words if word in text) def _measure_agglutination(self, text: str) -> float: """Measure agglutination level in Indonesian text.""" words = text.split() long_words = [w for w in words if len(w) > 8] return len(long_words) / max(len(words), 1) def _detect_community_concepts(self, text: str) -> int: """Detect community concepts in Indonesian text.""" community_words = ['masyarakat', 'komunitas', 'gotong-royong', 'bersama'] return sum(1 for word in community_words if word.lower() in text.lower()) def _detect_respect_markers(self, text: str, language: str) -> int: """Detect respect markers.""" respect_words = { 'indonesian': ['hormat', 'sopan', 'santun', 'menghargai'], 'chinese': ['尊重', '礼貌', '敬意', '客气'], 'arabic': ['احترام', 'أدب', 'تقدير', 'وقار'], 'spanish': ['respeto', 'cortesía', 'educación', 'consideración'], 'english': ['respect', 'courtesy', 'politeness', 'consideration'] } words = respect_words.get(language, []) return sum(1 for word in words if word.lower() in text.lower()) def _count_collective_pronouns(self, text: str, language: str) -> int: """Count collective pronouns.""" collective_pronouns = { 'indonesian': ['kita', 'kami', 'kita semua'], 'chinese': ['我们', '咱们', '大家'], 'arabic': ['نحن', 'إيانا', 'جميعنا'], 'spanish': ['nosotros', 'nosotras', 'todos'], 'english': ['we', 'us', 'everyone', 'all of us'] } pronouns = collective_pronouns.get(language, []) return sum(1 for pronoun in pronouns if pronoun.lower() in text.lower()) def _detect_romance_patterns(self, text: str) -> float: """Detect Romance language patterns in Spanish.""" # Simplified pattern detection for Spanish spanish_endings = ['ción', 'sión', 'dad', 'tad', 'mente'] pattern_count = sum(1 for ending in spanish_endings if any(word.endswith(ending) for word in text.split())) return min(1.0, pattern_count / max(len(text.split()), 1)) def _measure_emotional_expression(self, text: str) -> float: """Measure emotional expression level.""" emotional_markers = ['!', '¡', '¿', '?', 'muy', 'mucho', 'tanto'] count = sum(text.count(marker) for marker in emotional_markers) return min(1.0, count / max(len(text), 1)) def _detect_formality_level(self, text: str, language: str) -> float: """Detect formality level in text.""" formal_words = { 'spanish': ['usted', 'señor', 'señora', 'estimado'], 'english': ['sir', 'madam', 'dear', 'respectfully'], 'chinese': ['您', '先生', '女士', '敬爱的'], 'arabic': ['سيد', 'سيدة', 'محترم', 'مقدر'], 'indonesian': ['bapak', 'ibu', 'saudara', 'terhormat'] } words = formal_words.get(language, []) count = sum(1 for word in words if word.lower() in text.lower()) return min(1.0, count / max(len(text.split()), 1)) def _detect_regional_markers(self, text: str) -> int: """Detect regional variation markers in Spanish.""" regional_words = ['vos', 'che', 'güey', 'pibe', 'chamo'] return sum(1 for word in regional_words if word.lower() in text.lower()) def _detect_germanic_patterns(self, text: str) -> float: """Detect Germanic patterns in English.""" germanic_words = ['the', 'and', 'of', 'to', 'in', 'that', 'have', 'it'] count = sum(1 for word in germanic_words if word.lower() in text.lower()) return min(1.0, count / max(len(text.split()), 1)) def _measure_directness(self, text: str) -> float: """Measure directness level in English.""" direct_markers = ['must', 'should', 'will', 'need to', 'have to'] count = sum(1 for marker in direct_markers if marker.lower() in text.lower()) return min(1.0, count / max(len(text.split()), 1)) def _detect_individual_concepts(self, text: str) -> int: """Detect individualistic concepts.""" individual_words = ['i', 'me', 'my', 'myself', 'personal', 'individual'] return sum(1 for word in individual_words if word.lower() in text.lower()) def _detect_efficiency_concepts(self, text: str) -> int: """Detect efficiency-related concepts.""" efficiency_words = ['efficient', 'fast', 'quick', 'optimize', 'streamline'] return sum(1 for word in efficiency_words if word.lower() in text.lower()) def _detect_innovation_concepts(self, text: str) -> int: """Detect innovation-related concepts.""" innovation_words = ['new', 'innovative', 'creative', 'breakthrough', 'novel'] return sum(1 for word in innovation_words if word.lower() in text.lower()) def get_multilingual_metrics(self) -> Dict[str, Any]: """Get comprehensive metrics for multilingual processing.""" return { 'supported_languages': list(self.language_configs.keys()), 'cultural_dimensions': list(self.cultural_quantum_encodings.keys()), 'max_qubits': self.max_qubits, 'quantum_advantage_factor': len(self.language_configs) ** 2, 'cross_cultural_mappings': len(self.language_configs) * (len(self.language_configs) - 1) // 2 }