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"""Quantum-inspired layer: tensor networks and quantum search simulator stubs.
These are lightweight placeholders that integrate with the rest of the system
but do not require GPU or specialized libraries to be present.
"""
from typing import Any, List
import random
try:
import tensornetwork as tn
TENSORNETWORK_AVAILABLE = True
except Exception:
TENSORNETWORK_AVAILABLE = False
try:
import qiskit
QISKIT_AVAILABLE = True
except Exception:
QISKIT_AVAILABLE = False
def tensor_compress(structure: Any) -> dict:
if TENSORNETWORK_AVAILABLE:
# placeholder compress operation
return {"status": "compressed", "detail": "tensornetwork_used"}
return {"status": "compressed", "detail": "fallback_tensor_fn"}
def quantum_search_score(space_size: int, heuristic: float = 0.5) -> float:
"""Approximate Grover-like speedup score for sampling a large space.
Returns an estimated amplification factor (not a real quantum simulation).
"""
# naive model: sqrt speedup * heuristic
return (space_size ** 0.5) * heuristic
def approximate_solution(seed: Any) -> dict:
# return a randomized approximate solution
return {"approx": random.random(), "seed": str(seed)}
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