Quantum Data Networks (QDNs) have emerged as a promising framework in the field of information processing and transmission, harnessing the principles of quantum mechanics. QDNs utilize a quantum teleportation technique through long-distance entanglement connections, encoding data information in quantum bits (qubits). Despite being a cornerstone in various quantum applications, quantum entanglement encounters challenges in establishing connections over extended distances due to probabilistic processes influenced by factors like optical fiber losses. The creation of long-distance entanglement connections between quantum computers involves multiple entanglement links and entanglement swapping techniques through successive quantum nodes, including quantum computers and quantum repeaters, necessitating optimal path selection and qubit allocation. Current research predominantly assumes known success rates of entanglement links between neighboring quantum nodes and overlooks potential network attackers. This paper addresses the online challenge of optimal path selection and qubit allocation, aiming to learn the best strategy for achieving the highest success rate of entanglement connections between two chosen quantum computers without prior knowledge of the success rate and in the presence of a QDN attacker. The proposed approach is based on multi-armed bandits, specifically adversarial group neural bandits, which treat each path as a group and view qubit allocation as arm selection. Our contributions encompass formulating an online adversarial optimization problem, introducing the EXPNeuralUCB bandits algorithm with theoretical performance guarantees, and conducting comprehensive simulations to showcase its superiority over established advanced algorithms.

翻译：量子数据网络（QDN）作为一种利用量子力学原理的信息处理与传输框架，已展现出广阔的应用前景。QDN通过长距离纠缠连接采用量子隐形传态技术，将数据信息编码于量子比特中。尽管量子纠缠是众多量子应用的基石，但由于受光纤损耗等因素影响的概率性过程，其在长距离连接建立方面仍面临挑战。量子计算机之间的长距离纠缠连接涉及多个纠缠链路以及通过包括量子计算机与量子中继器在内的连续量子节点进行的纠缠交换技术，这需要最优的路径选择与量子比特分配。现有研究大多假设相邻量子节点间的纠缠链路成功率为已知，且忽略了潜在的网络攻击者。本文针对最优路径选择与量子比特分配的在线优化问题展开研究，目标是在缺乏先验成功率知识且存在QDN攻击者的情况下，通过学习最优策略以实现选定两台量子计算机间纠缠连接的最高成功率。所提出的方法基于多臂老虎机框架，具体采用对抗性群体神经老虎机模型，将每条路径视为一个群体，并将量子比特分配视作臂选择问题。我们的贡献包括：构建在线对抗性优化问题模型，提出具有理论性能保证的EXPNeuralUCB老虎机算法，并通过全面仿真实验验证其相对于现有先进算法的优越性。