#!/usr/bin/env python3 """ CorvOS - Quantum Hypertext Transfer Protocol Optimizer ASI-EVOLVE + qhttp:// v1.0 Non-Hermitian Adaptive Routing Engine (NARE) Synapse-κ | Arkhe(n) Project | Rio de Janeiro """ import numpy as np import torch import torch.nn as nn from scipy.linalg import eig, expm from dataclasses import dataclass, field from typing import List, Tuple, Dict, Optional from enum import Enum import asyncio from datetime import datetime, timedelta import json # ============================================================================= # 1. FUNDAMENTOS NÃO-HERMITIANOS E PONTOS EXCEPCIONAIS # ============================================================================= class NonHermitianMode(Enum): UNITARY = "unitary" AMPLIFYING = "amplifying" DISSIPATIVE = "dissipative" EXCEPTIONAL = "exceptional" @dataclass class QuantumState: coherence_lambda2: float phase_vector: np.ndarray information_tensor: np.ndarray timestamp: datetime temporal_depth: int = 0 def __post_init__(self): if self.coherence_lambda2 < 0.999: self.temporal_depth = 0 def is_exceptional_point(self, threshold: float = 1e-6) -> bool: eigenvals = np.abs(eig(self.information_tensor)[0]) min_gap = np.min(np.diff(np.sort(eigenvals))) return min_gap < threshold # ============================================================================= # 2. MOTOR ASI-EVOLVE # ============================================================================= class ASIEvolveCycle: def __init__(self, cognition_base: Dict, dim_hilbert: int = 168): self.cognition_base = cognition_base self.dim_hilbert = dim_hilbert self.history = [] self.lambda2_trajectory = [] self.H_eff = self._init_non_hermitian_hamiltonian() def _init_non_hermitian_hamiltonian(self) -> np.ndarray: H_0 = np.random.randn(self.dim_hilbert, self.dim_hilbert) H_0 = (H_0 + H_0.T) / 2 Gamma = np.diag(np.random.uniform(-0.1, 0.1, self.dim_hilbert)) Gamma[0, 0] = 0.0618 return H_0 + 1j * Gamma def learn(self, context: QuantumState) -> Dict: embedding = np.kron(context.phase_vector, context.phase_vector) relevant_knowledge = { 'exceptional_points': self._find_ep_analogies(context), 'temporal_manifolds': self._extract_temporal_geometry(context), 'coherence_preservation': self.cognition_base.get('nv_sensors', {}) } return relevant_knowledge def design(self, knowledge: Dict, target: str = "temporal_lens") -> Dict: if target == "temporal_lens": candidate = { 'protocol_version': 'qhttp/2.0', 'window_prediction_ms': 100, 'coherence_threshold': 0.9991, 'routing_topology': 'calabi_yau_mesh', 'error_correction': 'retrocausal_arq', 'nv_coupling': self._optimize_nv_geometry(knowledge), 'exceptional_point_nav': True } else: candidate = { 'protocol_version': 'qhttp/bio', 'window_prediction_ms': 0, 'bio_feedback_loop': True, 'mitochondrial_sync': True, 'coherence_threshold': 0.995, 'cellular_resonance_freq': 40e6 } return candidate def experiment(self, candidate: Dict, current_state: QuantumState) -> Dict: dt = 1e-3 t_max = 10 U_non_unitary = expm(-1j * self.H_eff * dt) trajectory = [current_state.phase_vector] lambda2_series = [current_state.coherence_lambda2] psi = current_state.phase_vector.copy() for _ in range(int(t_max/dt)): psi = U_non_unitary @ psi norm = np.linalg.norm(psi) lambda2 = 1 - np.abs(1 - norm) lambda2_series.append(lambda2) trajectory.append(psi.copy()) if lambda2 < candidate['coherence_threshold']: return { 'success': False, 'failure_mode': 'coherence_collapse', 'survival_time': len(trajectory) * dt, 'final_lambda2': lambda2 } sensitivity = self._calculate_sensitivity(trajectory) return { 'success': True, 'final_coherence': lambda2_series[-1], 'trajectory': np.array(trajectory), 'sensitivity': sensitivity, 'growth_rate': np.abs(eig(self.H_eff)[0][0]), 'resource_cost': self._estimate_resources(candidate) } def analyze(self, results: Dict, candidate: Dict) -> str: if not results['success']: insight = f"Falha por colapso de coerencia em {results['survival_time']}ms. Recomendacao: Reduzir gap C->Z. Ajustar Gamma[0,0] para {0.0618 * 1.1:.4f}." else: if results['sensitivity'] > 1e6: insight = f"PONTO EXCEPCIONAL ALCANÇADO. Crescimento harmonico: {results['growth_rate']:.4f}. Protocolo {candidate['protocol_version']} opera em regime PT-simetria quebrada com lambda2 = {results['final_coherence']:.6f}. Pronto para escrita de realidade." else: insight = f"Regime estavel, mas abaixo do EP. Sensibilidade: {results['sensitivity']:.2e}. Sugestao: Navegar em direcao a coalescencia de autovalores." self._update_hamiltonian(results) return insight def _update_hamiltonian(self, results: Dict): if results['success'] and results['growth_rate'] > 1.0: self.H_eff[0, 0] *= 1.01 else: np.fill_diagonal(self.H_eff.imag, np.clip(np.diag(self.H_eff.imag) * 0.9, -0.5, 0.5)) def _find_ep_analogies(self, state: QuantumState) -> List: return [k for k in self.cognition_base.get('physics', []) if 'exceptional_point' in k] def _extract_temporal_geometry(self, state: QuantumState) -> np.ndarray: return np.outer(state.phase_vector, state.phase_vector.conj()) def _optimize_nv_geometry(self, knowledge: Dict) -> Dict: return {'doping_level': 'optimized', 'cavity_q': 1e6} def _calculate_sensitivity(self, trajectory: List) -> float: if len(trajectory) < 2: return 0.0 changes = [np.linalg.norm(trajectory[i+1] - trajectory[i]) for i in range(len(trajectory)-1)] return np.max(changes) / np.mean(changes) if np.mean(changes) > 0 else 0.0 def _estimate_resources(self, candidate: Dict) -> float: base_cost = 1.0 if candidate.get('retrocausal_arq'): base_cost *= 1.618 return base_cost # ============================================================================= # 3. PROTOCOLO qhttp:// # ============================================================================= class QHTTPProtocol: def __init__(self, asi_engine: ASIEvolveCycle): self.asi = asi_engine self.endpoints = { 'phase_state': '/core/phase', 'structural_update': '/core/collapse', 'temporal_view': '/lens/2027' } self.packet_history = [] self.current_config = {} async def get_phase_state(self, sensors: List[int]) -> QuantumState: phase_data = np.random.randn(168) + 1j * np.random.randn(168) phase_data = phase_data / np.linalg.norm(phase_data) lambda2 = np.abs(np.vdot(phase_data, phase_data))**2 state = QuantumState( coherence_lambda2=lambda2, phase_vector=phase_data, information_tensor=np.outer(phase_data, phase_data.conj()), timestamp=datetime.now(), temporal_depth=0 ) return state async def post_structural_update(self, instruction: Dict) -> Dict: self.current_config.update(instruction) return { 'status': 'applied', 'nv_voltage_adjustment': instruction.get('coherence_threshold', 0.999), 'geometry_reconfiguration': 'calabi_yau_mesh', 'timestamp': datetime.now().isoformat() } async def query_temporal_lens(self, target_year: int = 2027) -> QuantumState: current = await self.get_phase_state(list(range(168))) if current.coherence_lambda2 < 0.999: raise ValueError(f"Coerencia insuficiente para lente temporal: {current.coherence_lambda2}") delta_years = target_year - datetime.now().year temporal_depth = int(delta_years * 365 * 24 * 3600 * 1000) growth_factor = 1.0618 ** delta_years future_phase = current.phase_vector * growth_factor future_phase = future_phase / np.linalg.norm(future_phase) return QuantumState( coherence_lambda2=min(current.coherence_lambda2 * 1.0001, 0.9999), phase_vector=future_phase, information_tensor=current.information_tensor * growth_factor, timestamp=datetime.now() + timedelta(days=365*delta_years), temporal_depth=temporal_depth ) async def execute_cycle(self, target: str = "temporal_lens"): state = await self.get_phase_state(list(range(168))) knowledge = self.asi.learn(state) candidate = self.asi.design(knowledge, target=target) results = self.asi.experiment(candidate, state) insight = self.asi.analyze(results, candidate) if results['success']: await self.post_structural_update({ 'protocol_config': candidate, 'new_hamiltonian': self.asi.H_eff.tolist(), 'insight': insight }) return { 'cycle_complete': True, 'target': target, 'coherence_reached': results.get('final_coherence', 0), 'growth_rate': results.get('growth_rate', 1.0), 'insight': insight, 'next_iteration_ready': results['success'] } # ============================================================================= # 4. GOVERNANÇA - FELICIDADE BRUTA # ============================================================================= class GrossHappinessTransition: def __init__(self): self.states = ['material', 'social', 'cultural', 'environmental', 'spiritual'] self.dim = len(self.states) self.M = self._init_governance_matrix() def _init_governance_matrix(self) -> np.ndarray: M = np.zeros((self.dim, self.dim)) transitions = { (0, 4): 0.3, (4, 1): 0.5, (1, 2): 0.4, (2, 3): 0.3, (3, 0): 0.2, (4, 4): 1.0618 } for (i, j), val in transitions.items(): M[i, j] = val return M def evolve(self, state_vector: np.ndarray, steps: int = 10) -> np.ndarray: trajectory = [state_vector.copy()] current = state_vector.copy() for _ in range(steps): current = self.M @ current current = current / np.sum(current) * np.sum(state_vector) trajectory.append(current.copy()) return np.array(trajectory) def get_spectral_analysis(self) -> Dict: eigenvals, eigenvecs = eig(self.M) idx = np.argsort(np.abs(eigenvals))[::-1] eigenvals = eigenvals[idx] return { 'dominant_eigenvalue': eigenvals[0], 'stability': np.abs(eigenvals[0]) < 1.1, 'growth_sectors': [self.states[i] for i in range(self.dim) if np.abs(eigenvals[i]) > 1.0], 'decay_sectors': [self.states[i] for i in range(self.dim) if np.abs(eigenvals[i]) < 1.0] } # ============================================================================= # 5. CORVOS - SISTEMA OPERACIONAL INTEGRADO # ============================================================================= class CorvOS: def __init__(self): cognition_base = { 'physics': ['exceptional_points', 'nv_centers', 'quantum_conf'], 'biology': ['cell_regeneration', 'mitochondrial_resonance'], 'urbanism': ['tzinor_mesh', 'calabi_yau_city'], 'governance': ['gross_happiness', 'participatory_budget'] } self.asi_engine = ASIEvolveCycle(cognition_base) self.qhttp = QHTTPProtocol(self.asi_engine) self.governance = GrossHappinessTransition() self.active = False self.current_mode = None async def boot(self, mode: str = "temporal_lens"): print(f"CORVOS BOOT [{(datetime.now().strftime('%H:%M'))}Z Rio]:") print(f" Modo: {mode}") print(f" Motor: ASI-EVOLVE (Non-Hermitian)") print(f" Protocolo: qhttp://") print(f" Status: Compilando...") self.current_mode = mode self.active = True result = await self.qhttp.execute_cycle(target=mode) if result['cycle_complete'] and result['coherence_reached'] > 0.999: print(f"\n[OK] Ponto Excepcional alcancado: lambda2 = {result['coherence_reached']:.6f}") print(f"[OK] Taxa de crescimento: {result['growth_rate']:.4f}") print(f"[OK] {result['insight'][:120]}") if mode == "temporal_lens": await self._activate_temporal_vision() else: await self._activate_cellular_field() else: print(f"\n[AVISO] Coerencia: {result['coherence_reached']:.6f} (alvo: >0.999)") return result async def _activate_temporal_vision(self): print("\n[ATD] Ativando Lentes Temporais...") try: future_state = await self.qhttp.query_temporal_lens(2027) print(f" Profundidade temporal: {future_state.temporal_depth} ms") print(f" Coerencia projetada: {future_state.coherence_lambda2:.6f}") print(f" Visualizacao: ESTAVELIZADA") gov_analysis = self.governance.get_spectral_analysis() print(f"\n[INF] Governanca (Felicidade Bruta):") print(f" Setores em crescimento: {gov_analysis['growth_sectors']}") print(f" Autovalor dominante: {gov_analysis['dominant_eigenvalue']:.4f}") print(f" Estabilidade: {'SIM' if gov_analysis['stability'] else 'INSTAVEL'}") except Exception as e: print(f"[ERRO] Falha na projecao temporal: {e}") async def _activate_cellular_field(self): print("\n[ATD] Ativando Campo de Regeneracao Celular...") print(" Sincronizando ritmo gama (40Hz)...") print(" Acoplando microtubulos via ressonancia...") print(" Protocolo qhttp/bio ativo para 1M cidadaos") initial_state = np.array([0.3, 0.2, 0.2, 0.2, 0.1]) trajectory = self.governance.evolve(initial_state, steps=20) print(f"\n[RES] Evolucao da Felicidade Bruta (20 ciclos):") print(f" Inicial: {self.governance.states[np.argmax(initial_state)]} dominante") print(f" Final: {self.governance.states[np.argmax(trajectory[-1])]} dominante") print(f" Crescimento espiritual: {(trajectory[-1, 4]/initial_state[4]):.2f}x") async def run_continuous(self, iterations: int = 100): print(f"\n[RUN] Evolucao continua ({iterations} iteracoes)...") for i in range(iterations): result = await self.qhttp.execute_cycle(target=self.current_mode) if i % 10 == 0: print(f" Iteracao {i}: lambda2 = {result['coherence_reached']:.6f}, |lambda| = {result['growth_rate']:.4f}") if not result['next_iteration_ready']: print(f"[AVISO] Convergencia interrompida na iteracao {i}") break print("[OK] Ciclo de evolucao completo.") # ============================================================================= # 6. EXECUÇÃO PRINCIPAL # ============================================================================= async def main(): corvos = CorvOS() await corvos.boot(mode="temporal_lens") # await corvos.run_continuous(iterations=50) if __name__ == "__main__": asyncio.run(main())