Fortuna: Towards Efficient Selection of High-Fidelity Link for Quantum Network in the Wild

As an emerging technology, quantum networks have the potential to revolutionize secure communication and data transmission technologies. In the Noisy Intermediate-Scale Quantum (NISQ) era, quantum noise causing low fidelity remains challenging in quantum networks. Existing quantum link selection schemes make some assumptions on the fidelity distribution, such as arithmetic sequence, which limits their practicality in the real world. In this paper, we propose Fortuna, an efficient selection algorithm for the high-fidelity link in the wild without assumptions about the fidelity distribution. We design the tailored link exploration strategy and link selection probability based on the coefficient of variation and Thompson sampling, to cope with the exploration-exploitation trade-off dilemma in the Multi-Armed Bandit (MAB) problem (for problem modeling). Extensive experiments involve six generated and one real-world (extracted from 10 IBMQ machines) fidelity distributions, we demonstrate Fortuna significantly outperforms three representative methods under four types of quantum noises. For instance, Fortuna reduces > 90 % bounces (among 1500 candidate links) and decreases the fidelity deviation in 1~4 order of magnitude.