信道噪声与设备漂移下量子密钥分发的学习型攻击
Learnt Attacks on Quantum Key Distribution under Channel Noise and Device Drift
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中文总结 AI 辅助
本文提出将量子密钥分发中的自适应窃听建模为约束马尔可夫决策过程，联合搜索门结构与旋转角度，在E91和BB84协议下显著提升攻击性能，接近动态规划上界。
中文摘要 AI 辅助
量子密钥分发（QKD）链路的配置基于静态信道的安全分析，而决定信道在重新校准之间漂移的正是设备本身。窃听者能否通过跟随这种漂移获得收益（即使无法改变信道自身的噪声）尚未被量化。本文提出将自适应窃听建模为约束马尔可夫决策过程：攻击者每轮选择一个电路，噪声水平遵循奥恩斯坦-乌伦贝克过程，中止条件为每轮块上的预算。自适应的价值通过最优固定电路和动态规划上界来界定。动作即为学习型攻击。与Decker等人针对固定信道在固定门模板上训练参数化电路不同，本文联合搜索门结构和旋转角度，从而产生足够紧凑的电路以形成离散动作集，并将构造扩展到缺乏已知模板的噪声模型，包括振幅阻尼信道。在双侧去极化噪声下的设备无关E91协议中，强化学习攻击者在零检测时将Holevo信息从最优固定电路的0.135提升至0.348，达到上界的98%。在漂移比特翻转信道下的BB84协议中，攻击者在保真度上超过保守的噪声索引规则0.024，达到上界的99%。在静态噪声下，攻击者从基不对称性中获得的收益在平均错误率约束与每基错误率约束之间改变符号。从随机门序列开始的搜索恢复了解析克隆器和集体攻击密钥率，并从上方满足Winick–Lütkenhaus–Coles目标的下界。
英文摘要
Quantum key distribution (QKD) links are provisioned from security analyses of stationary channels, whereas the devices that determine the channel drift between recalibrations. Whether an eavesdropper who cannot alter the channel's own noise gains by following that drift has not been quantified. Adaptive eavesdropping is posed here as a constrained Markov decision process in which the attacker selects one circuit per round while the noise level follows an Ornstein--Uhlenbeck process and the abort condition is a budget over each block of rounds. The value of adaptation is bounded by the best fixed circuit and a dynamic-programming upper bound. The actions are learnt attacks. Whereas Decker et al. trained a parametrised circuit on a fixed gate template against a fixed channel, here the gate structure and rotation angles are searched jointly. This yields circuits compact enough to form a discrete action set, extending the construction to noise models lacking a known template, including the amplitude damping channel. On device-independent E91 under bilateral depolarising noise, a reinforcement-learning attacker raises her Holevo information from $0.135$ for the best fixed circuit to $0.348$ at zero detection, $98\%$ of the upper bound. On BB84 under a drifting bit-flip channel, she exceeds a conservative noise-indexed rule by $0.024$ in fidelity, reaching $99\%$ of the upper bound. Under stationary noise, the attacker's gain from basis asymmetry changes sign between an averaged and a per-basis error-rate constraint. The search, started from random gate sequences, recovers the analytical cloners and the collective-attack key rate, and meets the lower bound of the Winick--Lütkenhaus--Coles objective from above.
发表机构
- Imperial College London（帝国理工学院）
机构由 AI 辅助整理，请以论文原文为准。
