Entanglement Distribution Delay Optimization in Quantum Networks with Distillation
<|reference_start|>Entanglement Distribution Delay Optimization in Quantum Networks with Distillation: Quantum networks (QNs) distribute entangled states to enable distributed quantum computing and sensing applications. However, in such QNs, quantum switches (QSs) have limited resources that are highly sensitive to noise and losses and must be carefully allocated to minimize entanglement distribution delay. In this paper, a QS resource allocation framework is proposed, which jointly optimizes the average entanglement distribution delay and entanglement distillation operations, to enhance the end-to-end (e2e) fidelity and satisfy minimum rate and fidelity requirements. The proposed framework considers realistic QN noise and includes the derivation of the analytical expressions for the average quantum memory decoherence noise parameter, and the resulting e2e fidelity after distillation. Finally, practical QN deployment aspects are considered, where QSs can control 1) nitrogen-vacancy (NV) center SPS types based on their isotopic decomposition, and 2) nuclear spin regions based on their distance and coupling strength with the electron spin of NV centers. A simulated annealing metaheuristic algorithm is proposed to solve the QS resource allocation optimization problem. Simulation results show that the proposed framework manages to satisfy all users rate and fidelity requirements, unlike existing distillation-agnostic (DA), minimal distillation (MD), and physics-agnostic (PA) frameworks which do not perform distillation, perform minimal distillation, and does not control the physics-based NV center characteristics, respectively. Furthermore, the proposed framework results in around 30% and 50% reductions in the average e2e entanglement distribution delay compared to existing PA and MD frameworks, respectively. Moreover, the proposed framework results in around 5%, 7%, and 11% reductions in the average e2e fidelity compared to existing DA, PA, and MD frameworks, respectively.<|reference_end|>
@article{chehimi2024entanglement,
    title={Entanglement Distribution Delay Optimization in Quantum Networks with
  Distillation},
    author={Mahdi Chehimi, Kenneth Goodenough, Walid Saad, Don Towsley, Tony X.
  Zhou},
    journal={arXiv preprint arXiv:2405.09034},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.09034},
    primaryClass={quant-ph cs.NI}
}