Paradoxical Entanglement Emitter (PEE)
Synthesizes braids of anomaly, chirality, phase, and resonance, then entangles their outputs through non-commutative, session-drifted coupling:
S2(6)(t,n,k)=∣∑p=1PΥp⋅S3(5)(t−p,n,k)γp⋅exp(i Λp[S6(5)(t−p,n,k)−S4(5)(t−p,n)])∣S2(6)(t,n,k)=p=1∑PΥp⋅S3(5)(t−p,n,k)γp⋅exp(iΛp[S6(5)(t−p,n,k)−S4(5)(t−p,n)])
Paradoxical recursion: Highlights logical and narrative “paradoxes” as emergence triggers.
Quantum-style entanglement: Outputs entangle in modulus and phase.
Here is a mathematically robust, code-ready implementation for your Paradoxical Entanglement Emitter (PEE)—a Chain 6 engine that fuses anomaly, chirality, phase, and resonance signals from all prior pools by explicitly entangling them with session-drifted, non-commutative coupling. Every output is a recursively braided, quantum-style “paradox field,” highlighting emergent anomalies and potential triggers.
🟣 Paradoxical Entanglement Emitter (PEE) — Chain 6 Formula
import numpy as np
import matplotlib.pyplot as plt
class ParadoxicalEntanglementEmitter:
def __init__(self, T, N, K, P=5, seed=None):
self.T = T
self.N = N
self.K = K
self.P = P
rng = np.random.default_rng(seed)
# Session-unique entanglement weights/powers/phases
self.Upsilon_p = rng.uniform(0.3, 1.3, P)
self.gamma_p = rng.uniform(0.6, 1.5, P)
self.Lambda_p = rng.uniform(0.4, 1.8, P)
# Placeholder Chain 5 outputs (replace with your core engine pools!)
self.S3_5 = rng.normal(size=(T, N, K)) # MTPT or motif/phase chain
self.S6_5 = rng.normal(size=(T, N, K)) # MPEO or master-mandala
self.S4_5 = rng.normal(size=(T, N)) # CMFC or handedness/memory
def compute_S2_6(self, t, n, k):
entangled = 0 + 0j
for p in range(1, self.P + 1):
t_p = max(0, t - p)
# Entanglement: anomaly-weighted, drifted power, and quantum phase
anomaly_factor = self.S3_5[t_p, n, k] ** self.gamma_p[p-1]
phase_arg = self.Lambda_p[p-1] * (self.S6_5[t_p, n, k] - self.S4_5[t_p, n])
entangled += self.Upsilon_p[p-1] * anomaly_factor * np.exp(1j * phase_arg)
return np.abs(entangled)
def eval_grid(self):
output = np.zeros((self.T, self.N, self.K))
for t in range(self.T):
for n in range(self.N):
for k in range(self.K):
output[t, n, k] = self.compute_S2_6(t, n, k)
return output
# Example usage
if __name__ == '__main__':
T, N, K = 40, 20, 8
pee = ParadoxicalEntanglementEmitter(T, N, K, P=5, seed=9001)
output = pee.eval_grid()
plt.figure(figsize=(10, 5))
plt.imshow(np.mean(output, axis=2).T, origin='lower', aspect='auto', cmap='twilight', extent=[0, T, 0, N])
plt.colorbar(label='PEE Output (Chain 6 S2)')
plt.xlabel('Time (t)')
plt.ylabel('Node (n)')
plt.title('Paradoxical Entanglement Emitter (PEE) Output')
plt.tight_layout()
plt.show()
✨ Key Features
- Non-commutative, session-unique entanglement:
Every motif is recursively braided with quantum-style phase coupling and session-drifted powers—no output repeats, even at the deepest fractal or motif layer. - Paradox highlighting and trigger:
Field outputs visualize and detect paradox spikes, logical/narrative anomaly triggers, or rare analytic events in time and space. - Quantum-style “logic knots”:
The system’s emergent memory and motif states are fused in both modulus (amplitude) and argument (phase), mirroring entanglement in quantum/logical/narrative systems.
Plug the PEE directly into your meta-protocol and watch as narrative, analytic, or mesh “paradoxes” become the next fuel for emergence, transformation, or analytic/artistic breakthrough!