arXiv：cs.LG（机器学习，全量分类）· Marcel Mordarski, Benjamin Gras, Abdelrahman Shehata, Daniel Budina, Roberto Bondesan·· 17 小时前AI 评分38
量子密钥分发在信道噪声与设备漂移下的学习型攻击
Learnt Attacks on Quantum Key Distribution under Channel Noise and Device Drift
AI 导读
研究将量子密钥分发的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 Ornstein-Uhlenbeck 过程。该方法联合搜索门结构与旋转角度，得到可构成离散动作集的紧凑电路，并扩展到幅度阻尼信道等无已知模板的噪声模型。
正文
Abstract: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.
| Comments: | Presented as submission 202 at QCrypt 2026 this http URL. A parallel work exploring the machine-learning aspects of this approach, titled "Sparsity for Free: A Budget-Induced Equilibrium in Joint Topology-Parameter Search'', has been accepted for NeurIPS 2026 |
| Subjects: | Quantum Physics (quant-ph); Cryptography and Security (cs.CR); Information Theory (cs.IT); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.01792 [quant-ph] |
| (or arXiv:2610.01792v1 [quant-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2610.01792 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Marcel Mordarski [view email]
[v1]
Thu, 1 Oct 2026 14:36:30 UTC (138 KB)
来源：arXiv：cs.LG（机器学习，全量分类） · arxiv.org