"""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)}