multilingual_quantum_processor.py

# -*- 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
        }