A deep-learning phase-only positioning method, anchored on hyperbola intersection, is reported to stay accurate under antenna failures and to outperform earlier phase-only approaches.
Asymmetric node placement in fiber-based quantum networks
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abstract
Restrictions imposed by existing infrastructure can make it hard to ensure an even spacing between the nodes of future fiber-based quantum networks. We here investigate the negative effects of asymmetric node placement by considering separately the placement of midpoint stations required for heralded entanglement generation, as well as of processing-node quantum repeaters in a chain. For midpoint stations, we describe the effect asymmetry has on the time required to perform one entangling attempt, the success probability of such attempts, and the fidelity of the entangled states created. This includes accounting for the effects of chromatic dispersion on photon indistinguishability. For quantum-repeater chains we numerically investigate how uneven spacing between repeater nodes leads to bottlenecks, thereby increasing both the waiting time and the time states are stored in noisy quantum memory. We find that while the time required to perform one entangling attempt may increase linearly with the midpoint's asymmetry, the success probability and fidelity of heralded entanglement generation and the distribution time and error rate for repeater chains all have vanishing first derivatives with respect to the amount of asymmetry. This suggests resilience of quantum-network performance against small amounts of asymmetry.
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Failure Tolerant Phase-Only Indoor Positioning via Deep Learning
A deep-learning phase-only positioning method, anchored on hyperbola intersection, is reported to stay accurate under antenna failures and to outperform earlier phase-only approaches.