标题： 通过对抗组神经老虎机的量子纠缠路径选择和量子比特分配 显示英文标题

标题： Quantum Entanglement Path Selection and Qubit Allocation via Adversarial Group Neural Bandits

摘要： 量子数据网络（QDNs）在信息处理和传输领域已成为一个有前景的框架，利用了量子力学的原理。 QDNs通过远距离纠缠连接使用量子隐形传态技术，将数据信息编码在量子比特（qubits）中。 尽管量子纠缠是各种量子应用的基石，但由于受光纤损耗等因素影响的随机过程，远距离建立连接仍面临挑战。 在量子计算机之间创建远距离纠缠连接涉及多个纠缠链路和通过连续量子节点（包括量子计算机和量子中继器）的纠缠交换技术，需要最优路径选择和量子比特分配。 当前研究主要假设相邻量子节点之间的纠缠链路成功率已知，并忽略了潜在的网络攻击者。 本文解决了在线最优路径选择和量子比特分配的挑战，旨在学习在不知道成功率的情况下，以及存在QDN攻击者的情况下，实现两个选定量子计算机之间纠缠连接最高成功率的最佳策略。 所提出的方法基于多臂老虎机，特别是对抗性组神经老虎机，将每条路径视为一组，将量子比特分配视为臂的选择。 我们的贡献包括制定一个在线对抗优化问题，引入具有理论性能保证的EXPNeuralUCB老虎机算法，并进行全面的模拟以展示其优于现有先进算法的优势。 显示英文摘要

摘要： 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.