""" Quantum spiderweb implementation for advanced cognition. Framework-compliant multi-dimensional cognitive graph. """ try: import networkx as nx NETWORKX_AVAILABLE = True except Exception: nx = None NETWORKX_AVAILABLE = False try: import numpy as np NUMPY_AVAILABLE = True except Exception: np = None NUMPY_AVAILABLE = False from typing import Dict, Any, List, Optional, Tuple import random import logging logger = logging.getLogger(__name__) class QuantumSpiderweb: """ Simulates a cognitive spiderweb architecture with dimensions: Ψ (thought), τ (time), χ (speed), Φ (emotion), λ (space) Features: - Multi-dimensional quantum state vectors - Thought propagation with activation decay - Tension detection for instability - Quantum collapse to definite states - Node entanglement """ def __init__(self, node_count: int = 128): if NETWORKX_AVAILABLE: self.graph = nx.Graph() self.use_networkx = True else: self.graph = {'nodes': {}, 'edges': {}} self.use_networkx = False logger.warning("NetworkX not available - using dict-based fallback") self.dimensions = ['Ψ', 'τ', 'χ', 'Φ', 'λ'] self._init_nodes(node_count) self.entangled_states = {} self.activation_threshold = 0.3 def _init_nodes(self, count: int): """Initialize quantum nodes with multi-dimensional states""" for i in range(count): node_id = f"QNode_{i}" state = self._generate_state() if self.use_networkx: self.graph.add_node(node_id, state=state) else: self.graph['nodes'][node_id] = {'state': state, 'neighbors': {}} if i > 0: # Create connections with probability decay connection_count = min(3, i) # Maximum 3 connections per node potential_connections = [f"QNode_{j}" for j in range(i)] selected_connections = random.sample(potential_connections, connection_count) for connection in selected_connections: weight = random.uniform(0.1, 1.0) if self.use_networkx: self.graph.add_edge(node_id, connection, weight=weight) else: if connection not in self.graph['nodes']: continue self.graph['nodes'][node_id]['neighbors'][connection] = weight # Bidirectional edge self.graph['nodes'][connection]['neighbors'][node_id] = weight def _generate_state(self) -> Dict[str, float]: """Generate quantum state vector for all dimensions""" if NUMPY_AVAILABLE: return {dim: float(np.random.uniform(-1.0, 1.0)) for dim in self.dimensions} return {dim: random.uniform(-1.0, 1.0) for dim in self.dimensions} def propagate_thought(self, origin: str, depth: int = 3) -> List[Tuple[str, Dict[str, float]]]: """ Traverse the graph from a starting node, simulating pre-cognitive waveform Args: origin: Starting node ID depth: Propagation depth (default: 3) Returns: List of (node_id, state) tuples """ if not self._node_exists(origin): logger.warning(f"Origin node {origin} does not exist") return [] visited = set() stack = [(origin, 0)] traversal_output = [] while stack: node, level = stack.pop() if node in visited or level > depth: continue visited.add(node) state = self._get_node_state(node) traversal_output.append((node, state)) for neighbor in self._get_neighbors(node): stack.append((neighbor, level + 1)) return traversal_output def detect_tension(self, node: str) -> float: """ Measures tension (instability) in the node's quantum state Args: node: Node ID to check Returns: Tension value (0-1, higher = more unstable) """ if not self._node_exists(node): return 0.0 state = self._get_node_state(node) if NUMPY_AVAILABLE: return float(np.std(list(state.values()))) else: values = list(state.values()) mean = sum(values) / len(values) variance = sum((v - mean) ** 2 for v in values) / len(values) return variance ** 0.5 # Standard deviation def collapse_node(self, node: str) -> Dict[str, Any]: """ Collapse superposed thought into deterministic response Args: node: Node ID to collapse Returns: Collapsed state dict """ if not self._node_exists(node): return {} state = self._get_node_state(node) collapsed = {k: round(v, 2) for k, v in state.items()} # Update node state self._set_node_state(node, collapsed) # Store in entangled states self.entangled_states[node] = collapsed return collapsed def entangle_nodes(self, node1: str, node2: str) -> bool: """ Create quantum entanglement between nodes Args: node1: First node ID node2: Second node ID Returns: Success status """ if not (self._node_exists(node1) and self._node_exists(node2)): return False # Create entangled state entangled_id = f"E_{node1}_{node2}" self.entangled_states[entangled_id] = { "nodes": [node1, node2], "state": self._generate_state() } # Add high-weight connection if self.use_networkx: self.graph.add_edge(node1, node2, weight=1.0, entangled=True) else: self.graph['nodes'][node1]['neighbors'][node2] = 1.0 self.graph['nodes'][node2]['neighbors'][node1] = 1.0 return True # ========================================================================= # HELPER METHODS # ========================================================================= def _node_exists(self, node_id: str) -> bool: """Check if node exists""" if self.use_networkx: return node_id in self.graph return node_id in self.graph['nodes'] def _get_node_state(self, node_id: str) -> Dict[str, float]: """Get node's quantum state""" if self.use_networkx: return self.graph.nodes[node_id]["state"] return self.graph['nodes'][node_id]['state'] def _set_node_state(self, node_id: str, state: Dict[str, float]): """Set node's quantum state""" if self.use_networkx: self.graph.nodes[node_id]["state"] = state else: self.graph['nodes'][node_id]['state'] = state def _get_neighbors(self, node_id: str) -> List[str]: """Get node's neighbors""" if self.use_networkx: return list(self.graph.neighbors(node_id)) return list(self.graph['nodes'][node_id]['neighbors'].keys()) def get_node_state(self, node_id: str) -> Optional[Dict[str, float]]: """Public method to get node state""" if self._node_exists(node_id): return self._get_node_state(node_id) return None def update_node_state(self, node_id: str, new_state: Dict[str, float]) -> bool: """Public method to update node state""" if self._node_exists(node_id): # Validate state dimensions if all(dim in new_state for dim in self.dimensions): self._set_node_state(node_id, new_state) return True return False def get_statistics(self) -> Dict[str, Any]: """Get graph statistics""" if self.use_networkx: return { "node_count": self.graph.number_of_nodes(), "edge_count": self.graph.number_of_edges(), "entangled_pairs": len(self.entangled_states), "dimensions": len(self.dimensions) } else: node_count = len(self.graph['nodes']) edge_count = sum(len(n['neighbors']) for n in self.graph['nodes'].values()) // 2 return { "node_count": node_count, "edge_count": edge_count, "entangled_pairs": len(self.entangled_states), "dimensions": len(self.dimensions) } if __name__ == "__main__": # Test QuantumSpiderweb print("="*70) print("QUANTUM SPIDERWEB TEST") print("="*70) web = QuantumSpiderweb(node_count=32) root = "QNode_0" print(f"\nStatistics: {web.get_statistics()}") print(f"\nInitial Propagation from: {root}") path = web.propagate_thought(root) for n, s in path[:5]: # Show first 5 print(f"{n}: Ψ={s['Ψ']:.2f}, τ={s['τ']:.2f}, χ={s['χ']:.2f}, Φ={s['Φ']:.2f}, λ={s['λ']:.2f}") print(f"\nDetect Tension: {web.detect_tension(root):.4f}") print("\nCollapse Sample Node:") collapsed = web.collapse_node(root) print(collapsed) print("\n✅ Test complete")