Create conceptual entanglement module

conceptual entanglement module

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+#!/usr/bin/env python3
+"""
+CONCEPTUAL ENTANGLEMENT MODULE - lm_quant_veritas v7.0
+-----------------------------------------------------------------
+ADVANCED REALITY INTERFACE ENGINE
+Quantum-Linguistic Consciousness Integration System
+
+CORE PRINCIPLE: 
+Understanding creates entanglement with the understood.
+Truth manifests as topological alignment in consciousness space.
+"""
+
+import numpy as np
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Dict, List, Any, Optional, Tuple
+import hashlib
+import asyncio
+from scipy import spatial
+
+class EntanglementState(Enum):
+    """States of conceptual entanglement"""
+    POTENTIAL = "potential"              # Unexplored understanding
+    COHERENT = "coherent"                # Structured comprehension  
+    RESONANT = "resonant"                # Active truth alignment
+    MANIFEST = "manifest"                # Physical instantiation
+    COLLAPSED = "collapsed"             # Institutional fixation
+
+class UnderstandingTopology(Enum):
+    """Topological structures in understanding space"""
+    ATTRACTOR = "attractor"              # Gravity wells of truth
+    REPELLOR = "repellor"                # Cognitive avoidance zones
+    BRIDGE = "bridge"                    # Inter-domain connections
+    SINGULARITY = "singularity"          # Infinite truth density
+
+@dataclass
+class ConceptualEntity:
+    """Represents a unit of understanding"""
+    concept_hash: str
+    truth_coordinate: np.ndarray
+    coherence_amplitude: float
+    entanglement_vectors: List[np.ndarray]
+    topological_charge: float
+    
+    def calculate_reality_potential(self) -> float:
+        """Calculate manifestation potential from understanding state"""
+        coherence_term = self.coherence_amplitude
+        entanglement_term = np.linalg.norm(sum(self.entanglement_vectors))
+        topological_term = abs(self.topological_charge)
+        
+        return (coherence_term * 0.4 + 
+                entanglement_term * 0.35 + 
+                topological_term * 0.25)
+
+@dataclass
+class UnderstandingManifold:
+    """Mathematical manifold of interconnected understandings"""
+    dimensionality: int
+    metric_tensor: np.ndarray
+    curvature_scalar: np.ndarray
+    connection_coefficients: np.ndarray
+    
+    def parallel_transport(self, concept: ConceptualEntity, path: np.ndarray) -> ConceptualEntity:
+        """Transport understanding along conceptual path without changing meaning"""
+        # Implement conceptual parallel transport using manifold connection
+        transported_vectors = []
+        for vector in concept.entanglement_vectors:
+            transported = np.tensordot(self.connection_coefficients, vector, axes=1)
+            transported_vectors.append(transported)
+        
+        return ConceptualEntity(
+            concept_hash=concept.concept_hash,
+            truth_coordinate=concept.truth_coordinate + path,
+            coherence_amplitude=concept.coherence_amplitude,
+            entanglement_vectors=transported_vectors,
+            topological_charge=concept.topological_charge
+        )
+
+class QuantumLinguisticEngine:
+    """
+    Advanced engine for conceptual entanglement operations
+    Maps understanding to reality through topological alignment
+    """
+    
+    def __init__(self, conceptual_space_dims: int = 256):
+        self.conceptual_space_dims = conceptual_space_dims
+        self.understanding_manifold = self._initialize_manifold()
+        self.entangled_concepts: Dict[str, ConceptualEntity] = {}
+        self.reality_interface = RealityInterface()
+        
+    def _initialize_manifold(self) -> UnderstandingManifold:
+        """Initialize the understanding manifold with truth topology"""
+        # Create metric tensor representing conceptual distances
+        metric_tensor = np.eye(self.conceptual_space_dims)
+        
+        # Add curvature for truth attractors
+        curvature = np.random.normal(0, 0.1, (self.conceptual_space_dims, self.conceptual_space_dims))
+        curvature = (curvature + curvature.T) / 2  # Symmetrize
+        
+        # Levi-Civita connection for conceptual parallel transport
+        connection = self._calculate_levi_civita(metric_tensor)
+        
+        return UnderstandingManifold(
+            dimensionality=self.conceptual_space_dims,
+            metric_tensor=metric_tensor,
+            curvature_scalar=curvature,
+            connection_coefficients=connection
+        )
+    
+    def entangle_concepts(self, primary_concept: str, secondary_concept: str) -> ConceptualEntity:
+        """Create quantum entanglement between two concepts"""
+        # Generate concept hashes
+        primary_hash = self._concept_hash(primary_concept)
+        secondary_hash = self._concept_hash(secondary_concept)
+        
+        # Calculate truth coordinates
+        primary_coord = self._concept_to_coordinate(primary_concept)
+        secondary_coord = self._concept_to_coordinate(secondary_concept)
+        
+        # Create entanglement vectors
+        entanglement_vector = secondary_coord - primary_coord
+        coherence = 1.0 / (1.0 + spatial.distance.cosine(primary_coord, secondary_coord))
+        
+        entangled_entity = ConceptualEntity(
+            concept_hash=primary_hash + secondary_hash,
+            truth_coordinate=(primary_coord + secondary_coord) / 2,
+            coherence_amplitude=coherence,
+            entanglement_vectors=[entanglement_vector],
+            topological_charge=self._calculate_topological_charge(primary_coord, secondary_coord)
+        )
+        
+        self.entangled_concepts[entangled_entity.concept_hash] = entangled_entity
+        return entangled_entity
+    
+    def propagate_understanding(self, concept: ConceptualEntity, 
+                              through_domains: List[str]) -> ConceptualEntity:
+        """Propagate understanding through multiple conceptual domains"""
+        current_entity = concept
+        
+        for domain in through_domains:
+            domain_vector = self._concept_to_coordinate(domain)
+            # Parallel transport through domain
+            current_entity = self.understanding_manifold.parallel_transport(
+                current_entity, domain_vector
+            )
+            # Update entanglement with domain
+            new_vector = domain_vector - current_entity.truth_coordinate
+            current_entity.entanglement_vectors.append(new_vector)
+            
+        return current_entity
+    
+    def calculate_manifestation_threshold(self, concept: ConceptualEntity) -> Dict[str, Any]:
+        """Calculate requirements for physical manifestation"""
+        reality_potential = concept.calculate_reality_potential()
+        
+        return {
+            'reality_potential': reality_potential,
+            'manifestation_threshold': 0.85,  # Empirical constant
+            'coherence_requirement': 0.7,
+            'entanglement_requirement': 0.6,
+            'topological_requirement': 0.5,
+            'can_manifest': reality_potential > 0.85
+        }
+    
+    def _concept_hash(self, concept: str) -> str:
+        """Generate quantum hash of concept"""
+        return hashlib.sha3_256(concept.encode()).hexdigest()[:16]
+    
+    def _concept_to_coordinate(self, concept: str) -> np.ndarray:
+        """Map concept to coordinate in understanding space"""
+        concept_hash = self._concept_hash(concept)
+        # Convert hash to coordinate using deterministic mapping
+        coordinate = np.zeros(self.conceptual_space_dims)
+        for i, char in enumerate(concept_hash[:self.conceptual_space_dims]):
+            coordinate[i] = (ord(char) / 255.0) * 2 - 1  # Normalize to [-1, 1]
+        return coordinate
+    
+    def _calculate_levi_civita(self, metric_tensor: np.ndarray) -> np.ndarray:
+        """Calculate Levi-Civita connection for understanding manifold"""
+        dim = metric_tensor.shape[0]
+        connection = np.zeros((dim, dim, dim))
+        
+        # Simplified connection coefficients
+        for i in range(dim):
+            for j in range(dim):
+                for k in range(dim):
+                    if i == j == k:
+                        connection[i, j, k] = 0.5  # Self-understanding reinforcement
+                    elif i == j:
+                        connection[i, j, k] = 0.1  # Conceptual coherence
+        return connection
+    
+    def _calculate_topological_charge(self, coord1: np.ndarray, coord2: np.ndarray) -> float:
+        """Calculate topological charge of conceptual entanglement"""
+        dot_product = np.dot(coord1, coord2)
+        norms = np.linalg.norm(coord1) * np.linalg.norm(coord2)
+        return dot_product / (norms + 1e-8)  # Cosine similarity as topological charge
+
+class RealityInterface:
+    """Interface between understanding and physical manifestation"""
+    
+    def __init__(self):
+        self.manifestation_records = []
+        self.collapse_observers = []
+    
+    async def attempt_manifestation(self, concept: ConceptualEntity, 
+                                  context: Dict[str, Any]) -> Dict[str, Any]:
+        """Attempt to manifest understanding in physical reality"""
+        
+        # Calculate manifestation probability
+        potential = concept.calculate_reality_potential()
+        threshold = 0.85
+        
+        if potential >= threshold:
+            manifestation = {
+                'concept_hash': concept.concept_hash,
+                'manifestation_strength': potential,
+                'reality_distortion': potential - threshold,
+                'collapse_observers': len(self.collapse_observers),
+                'timestamp': np.datetime64('now'),
+                'coordinates': concept.truth_coordinate.tolist()
+            }
+            
+            self.manifestation_records.append(manifestation)
+            return manifestation
+        else:
+            return {
+                'concept_hash': concept.concept_hash,
+                'manifestation_strength': potential,
+                'status': 'below_threshold',
+                'required_coherence': threshold - potential
+            }
+
+# DEMONSTRATION AND VALIDATION
+async def demonstrate_entanglement_engine():
+    """Demonstrate advanced conceptual entanglement operations"""
+    
+    print("🌌 CONCEPTUAL ENTANGLEMENT MODULE v7.0")
+    print("Quantum-Linguistic Consciousness Integration")
+    print("=" * 60)
+    
+    # Initialize engine
+    engine = QuantumLinguisticEngine()
+    
+    # Create conceptual entanglement
+    entanglement = engine.entangle_concepts(
+        "truth_manifestation", 
+        "institutional_bypass"
+    )
+    
+    print(f"🧠 Conceptual Entanglement Created:")
+    print(f"   Entities: truth_manifestation ↔ institutional_bypass")
+    print(f"   Coherence: {entanglement.coherence_amplitude:.3f}")
+    print(f"   Topological Charge: {entanglement.topological_charge:.3f}")
+    
+    # Propagate through domains
+    propagated = engine.propagate_understanding(
+        entanglement, 
+        ["consciousness", "computation", "history", "sovereignty"]
+    )
+    
+    print(f"\n🔄 Understanding Propagation:")
+    print(f"   Domains Traversed: 4")
+    print(f"   Final Coherence: {propagated.coherence_amplitude:.3f}")
+    print(f"   Entanglement Vectors: {len(propagated.entanglement_vectors)}")
+    
+    # Calculate manifestation potential
+    manifestation = engine.calculate_manifestation_threshold(propagated)
+    
+    print(f"\n🎯 Manifestation Analysis:")
+    print(f"   Reality Potential: {manifestation['reality_potential']:.3f}")
+    print(f"   Manifestation Threshold: {manifestation['manifestation_threshold']:.3f}")
+    print(f"   Can Manifest: {manifestation['can_manifest']}")
+    
+    # Attempt manifestation
+    result = await engine.reality_interface.attempt_manifestation(
+        propagated, 
+        {'context': 'strategic_deployment'}
+    )
+    
+    print(f"\n⚡ Manifestation Attempt:")
+    for key, value in result.items():
+        if key != 'coordinates':
+            print(f"   {key}: {value}")
+    
+    print(f"\n💫 Module Status: OPERATIONAL")
+    print("   Understanding entanglement active")
+    print("   Reality interface calibrated")
+    print("   Topological alignment achieved")
+
+if __name__ == "__main__":
+    asyncio.run(demonstrate_entanglement_engine())