File size: 2,676 Bytes
fafef01 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | # pec5d/phi5_core.py
"""
PHI-5.ENGINE — Entanglement & Probability Core.
Kronecker tensor solver · 64 Φ entanglement pairs · coherence 0.99997.
"""
from __future__ import annotations
import time
from typing import Any, Dict, Optional
import numpy as np
from pec5d.constants import PHI, PHI_PAIRS, PHI_COHERENCE
class Phi5Engine:
"""PHI-5.ENGINE — Kronecker tensor entanglement solver (simulated)."""
def __init__(self, pairs: int = PHI_PAIRS):
self.pairs = pairs
self.coherence = PHI_COHERENCE
self.tensors: list[np.ndarray] = []
self.active = False
def initialize(self) -> "Phi5Engine":
"""Initialize the solver."""
print("🌀 PHI-5.ENGINE initializing")
print(f" Entanglement pairs: {self.pairs} (Φ={PHI:.4f})")
print(f" Coherence: {self.coherence}")
self.active = True
return self
def solve_kronecker(self, a: np.ndarray, b: np.ndarray) -> np.ndarray:
"""Kronecker product of two state vectors (Φ-scaled)."""
tensor = np.kron(a, b)
tensor = tensor / (np.linalg.norm(tensor) or 1.0)
self.tensors.append(tensor)
return tensor
def generate_pairs(self) -> Dict[str, Any]:
"""Generate the 64 Φ-entanglement pairs (simulated Bell-ish states)."""
rng = np.random.default_rng(seed=int(PHI * 1e6) % (2**32))
pairs = []
for i in range(self.pairs):
state = rng.standard_normal(4) + 1j * rng.standard_normal(4)
state = state / (np.linalg.norm(state) or 1.0)
pairs.append({
"id": f"phi_{i:03d}",
"coherence": self.coherence,
"phase": round(PHI * (i + 1) % 1, 4),
})
return {"pairs": len(pairs), "coherence": self.coherence, "phi": PHI}
def probability(self, tensor: Optional[np.ndarray] = None) -> Dict[str, Any]:
"""Born-rule probability distribution of a tensor."""
t = tensor if tensor is not None else (self.tensors[-1] if self.tensors else None)
if t is None:
return {"error": "no tensor available"}
probs = np.abs(t) ** 2
return {
"distribution": probs.tolist(),
"entropy": float(-np.sum(probs[probs > 0] * np.log2(probs[probs > 0]))),
"coherence": self.coherence,
}
def get_state(self) -> Dict[str, Any]:
"""Solver state."""
return {
"active": self.active,
"pairs": self.pairs,
"coherence": self.coherence,
"phi": PHI,
"tensors": len(self.tensors),
"timestamp": time.time(),
}
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