| | |
| | """ |
| | Project Title: QDNA-ID: Quantum Device Native Authentication |
| | Developed by: Osamah N. Neamah |
| | |
| | |
| | Feature extraction: |
| | - Shannon entropy (per-circuit) |
| | - Probability variance (per-circuit) |
| | - Drift of dominant outcome |
| | - Imbalance index |
| | - CHSH S (S >= 2.0 indicates violation) |
| | """ |
| |
|
| | import math, numpy as np |
| | from typing import Dict, List, Tuple |
| |
|
| | def _entropy(counts: Dict[str,int]) -> float: |
| | tot = sum(counts.values()) |
| | if tot == 0: return 0.0 |
| | return -sum((v/tot)*math.log2(v/tot) for v in counts.values() if v) |
| |
|
| | def _probs(counts: Dict[str,int]) -> Dict[str,float]: |
| | tot = sum(counts.values()) |
| | return {k: (v/tot if tot else 0.0) for k,v in counts.items()} |
| |
|
| | def _dominant_prob(probs: Dict[str,float]) -> float: |
| | return max(probs.values()) if probs else 0.0 |
| |
|
| | def _imbalance(probs: Dict[str,float]) -> float: |
| | p00, p01, p10, p11 = (probs.get(s,0.0) for s in ("00","01","10","11")) |
| | return abs(p00 - p11) + abs(p01 - p10) |
| |
|
| | def _E_from_counts(counts: Dict[str,int]) -> float: |
| | p = _probs(counts) |
| | p00, p01, p10, p11 = (p.get(s,0.0) for s in ("00","01","10","11")) |
| | return (p00 + p11) - (p01 + p10) |
| |
|
| | def compute_chsh_S(chsh_results: List[Tuple[int, Dict[str,int]]]) -> float: |
| | """ |
| | chsh_results: list of (choice, counts) for choice in {0,1,2,3}: |
| | 0: (A,B), 1:(A,B'), 2:(A',B), 3:(A',B') |
| | S = E(A,B)+E(A,B')+E(A',B)-E(A',B') |
| | """ |
| | E = {} |
| | for choice, counts in chsh_results: |
| | E[choice] = _E_from_counts(counts) |
| | if not all(k in E for k in (0,1,2,3)): |
| | return float("nan") |
| | return (E[0] + E[1] + E[2] - E[3]) |
| |
|
| | def feature_vector_from_session(results_list: List[Dict[str,int]], circuit_types: List[str], circuit_choices: List[int]) -> Dict: |
| | entropies, variances, imbalances, probs_list = [], [], [], [] |
| | chsh_pairs: List[Tuple[int, Dict[str,int]]] = [] |
| |
|
| | for counts, ctype, choice in zip(results_list, circuit_types, circuit_choices): |
| | pr = _probs(counts) |
| | entropies.append(_entropy(counts)) |
| | variances.append(np.var(list(pr.values()) if pr else [0.0])) |
| | imbalances.append(_imbalance(pr)) |
| | probs_list.append(pr) |
| | if ctype == "chsh": |
| | chsh_pairs.append((choice, counts)) |
| |
|
| | dom = [_dominant_prob(p) for p in probs_list] |
| | drift_sum = sum(abs(dom[i]-dom[i-1]) for i in range(1, len(dom))) if dom else 0.0 |
| |
|
| | chsh_S = compute_chsh_S(chsh_pairs) |
| | chsh_violation = (not math.isnan(chsh_S)) and (chsh_S >= 2.0) |
| |
|
| | return { |
| | "avg_entropy": float(np.mean(entropies)) if entropies else 0.0, |
| | "std_entropy": float(np.std(entropies)) if entropies else 0.0, |
| | "avg_variance": float(np.mean(variances)) if variances else 0.0, |
| | "avg_imbalance": float(np.mean(imbalances)) if imbalances else 0.0, |
| | "drift_sum": float(drift_sum), |
| | "per_circuit_entropy": entropies, |
| | "per_circuit_variance": variances, |
| | "per_circuit_imbalance": imbalances, |
| | "chsh_S": None if math.isnan(chsh_S) else float(chsh_S), |
| | "chsh_violation": chsh_violation |
| | } |
| |
|
