Learning-based Route Selection in Noisy Quantum Communication Networks

被引:0
作者
Chaudhary, Vini [1 ]
Li, Kai [1 ]
Chowdhury, Kaushik [1 ]
机构
[1] Northeastern Univ, Inst Wireless Internet Things, Boston, MA 02115 USA
来源
ICC 2023-IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS | 2023年
关键词
Quantum communication network; Route selection; Entanglement; Fidelity; Multi-arm Bandit;
D O I
10.1109/ICC45041.2023.10279751
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
Finding a path with the least overall noise from quantum memories, fibers, and gate operations in a quantum network involves the challenge of acquiring knowledge of these noises, their sources, and the time of occurrences. In this paper, we propose a reinforcement learning-based route selection approach that uses a multi-arm bandit algorithm to find the least noisy path from a transmitter (Tx) to a receiver (Rx), without considering any information on qubit decoherence due to probabilistic noises inherent in quantum memories and imperfect gate operations. It only uses network deployment knowledge to find a set of feasible paths from Tx to Rx. We provide a key finding from a network design perspective which says that performing entanglement swapping on nodes within a path in a non-synchronized and parallel manner not always reduces the decoherence experienced in achieving the end-to-end entanglement on that path. Further, we design and open-source a new simulator for simulating probabilistic noises encountered during entanglement distribution between Tx-Rx on a path, which has supporting callable functions for connecting the unknown network environment required for interaction with the multi-arm bandit agent. The simulation results demonstrate that our proposed route selection approach provides a path up to similar to 33% better fidelity (less noise) compared to conventional, distance-based route selection approach for the considered quantum network.
引用
收藏
页码:4188 / 4193
页数:6
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