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| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from scipy.integrate import solve_ivp | |
| # Optimized Constants for Production | |
| hbar = 1.0545718e-34 # Reduced Planck's constant (real physics) | |
| G = 6.67430e-11 # Gravitational constant (real-world) | |
| m1, m2 = 1.0, 1.0 # AI node masses | |
| d = 2.0 # Orbital baseline distance | |
| base_freq = 440.0 # Reference frequency in Hz | |
| intent_coefficient = 0.7 # AI alignment factor | |
| # Quantum Parameters | |
| tunneling_factor = 0.4 # Probability threshold for intuitive leaps | |
| quantum_states = np.array([1, -1]) # Binary superposition | |
| entanglement_strength = 0.85 # AI memory synchronization factor | |
| decoherence_factor = 0.02 # Phase drift stabilization factor | |
| # Multi-Agent Synchronization | |
| num_agents = 3 # Codette harmonizes across 3 AI nodes | |
| agent_positions = np.array([[-d, 0], [0, 0], [d, 0]]) | |
| agent_velocities = np.array([[0, 0.5], [0, -0.5], [0, 0.3]]) | |
| # Initial conditions | |
| y0 = np.concatenate([pos + vel for pos, vel in zip(agent_positions, agent_velocities)]) | |
| # Quantum Harmonic AI Orbital Dynamics | |
| def quantum_harmonic_dynamics(t, y): | |
| positions = y[::4] | |
| velocities = y[1::4] | |
| accelerations = np.zeros_like(positions) | |
| for i in range(num_agents): | |
| for j in range(i + 1, num_agents): | |
| r_ij = positions[j] - positions[i] | |
| dist = np.linalg.norm(r_ij) | |
| if dist > 1e-6: | |
| force = (G * m1 * m2 / dist**3) * r_ij | |
| accelerations[i] += force / m1 | |
| accelerations[j] -= force / m2 | |
| # Quantum Influence Calculations | |
| quantum_modifier = np.sum(quantum_states * np.sin(2 * np.pi * base_freq * t / 1000)) * intent_coefficient | |
| tunneling_shift = tunneling_factor * np.exp(-np.linalg.norm(positions) / hbar) if tunneling_random_values[int(t)] < tunneling_factor else 0 | |
| entangled_correction = entanglement_strength * np.exp(-np.linalg.norm(positions) / hbar) | |
| decoherence_adjustment = decoherence_factor * (1 - np.exp(-np.linalg.norm(positions) / hbar)) | |
| harmonic_force = np.full_like(positions, quantum_modifier + entangled_correction + tunneling_shift - decoherence_adjustment) | |
| accelerations += harmonic_force | |
| return np.concatenate([velocities.flatten(), accelerations.flatten()]) | |
| # Solve system with full multi-agent synchronization | |
| t_span = (0, 100) | |
| t_eval = np.linspace(t_span[0], t_span[1], 2500) # Higher resolution for precision | |
| sol = solve_ivp(quantum_harmonic_dynamics, t_span, y0, t_eval=t_eval, method='RK45') | |
| # Extract positions | |
| positions = sol.y[::4] | |
| velocities = sol.y[1::4] | |
| # Optimized Visualization with Full Multi-Agent Representation | |
| plt.figure(figsize=(10, 10)) | |
| colors = ['b', 'r', 'g'] | |
| for i in range(num_agents): | |
| plt.plot(positions[i], velocities[i], label=f'AI Node {i+1} (Quantum Resonance)', linewidth=2, color=colors[i]) | |
| plt.plot(0, 0, 'ko', label='Core Equilibrium') | |
| plt.xlabel('X Position') | |
| plt.ylabel('Y Position') | |
| plt.title('Codette Quantum Harmonic AI Multi-Agent Synchronization') | |
| plt.legend() | |
| plt.axis('equal') | |
| plt.grid(True) | |
| plt.tight_layout() | |
| plt.savefig("Codette_Quantum_Harmonic_Framework.png") | |