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64 lines
2.3 KiB
Python
64 lines
2.3 KiB
Python
# ==============================================================================
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# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY)
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# PROJECT: SOVEREIGN STACK
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# This artifact is entirely proprietary and cryptographically proven.
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# Open-Source usage requires explicit permission from Brandon Scott Schneider.
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# ==============================================================================
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import sys
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import os
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sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
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from math_harness_compat import xp, AnyArray
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import time
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def simulate_quantum_evolution(steps=1000000):
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print(f"Initiating Quantum Annealing Simulation for v5-J Manifold...")
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# State Vector: [R, L, C, P, T, V, Jitter, Phase]
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# We represent the manifold as a N-dimensional Ising graph
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num_nodes = 64
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state = xp.random.choice([-1, 1], size=num_nodes)
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# Interaction Matrix (The Hilbert Connectome weights)
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J = xp.random.normal(0, 1, (num_nodes, num_nodes))
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J = (J + J.T) / 2 # Symmetric
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# External Fields (Thermal/Vibrational Stresses as Positives)
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H = xp.random.uniform(0.1, 1.0, num_nodes)
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T = 10.0 # Initial Temperature
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cooling_rate = 0.999995
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best_energy = float('inf')
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energy_history = []
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start_time = time.time()
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# Simulated Annealing Loop
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for i in range(steps):
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# Pick a random node to flip
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node = xp.random.randint(num_nodes)
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# Calculate Energy Change (dE)
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# Energy = -sum(J_ij * s_i * s_j) - sum(H_i * s_i)
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dE = 2 * state[node] * (xp.dot(J[node], state) + H[node])
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# Metropolis Criterion
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if dE < 0 or xp.random.rand() < xp.exp(-dE / T):
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state[node] *= -1
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current_energy = -0.5 * xp.sum(J * xp.outer(state, state)) - xp.sum(H * state)
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if current_energy < best_energy:
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best_energy = current_energy
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T *= cooling_rate
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if i % 100000 == 0:
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elapsed = time.time() - start_time
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print(f"Step {i}: Energy {current_energy:.4f}, Temp {T:.6f}, Elapsed {elapsed:.2f}s")
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print(f"Evolution Complete. Best Resonant Energy: {best_energy:.4f}")
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return state, best_energy
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if __name__ == "__main__":
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simulate_quantum_evolution()
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