Sequential vs. Simultaneous Entanglement Swapping under Optimal Link-Layer Control

2026-05-05

AI Summary

This study systematically compares the performance of connectionless sequential entanglement swapping and connection-oriented simultaneous swapping in quantum networks under the influence of memory decoherence. Holding chain length fixed and treating only the network-layer protocol as a variable, the authors optimize link-layer key rates using reinforcement learning and conduct simulations across tunable external memory coherence times. Employing a controlled-variable methodology for the first time, the work demonstrates that the inferior performance of sequential swapping stems not from an inherent architectural flaw but from insufficient current memory coherence times, thereby challenging fundamental criticisms of connectionless designs. The results show that simultaneous swapping maintains a constant key rate across all coherence times, whereas sequential swapping approaches comparable performance only when coherence times exceed a critical threshold.

Abstract

Connection-less, packet-switched quantum network architectures distribute entanglement across multi-hop paths through sequential entanglement swapping, in which each node acts on purely local state information. The architectural advantages over the connection-oriented alternative -- simultaneous SWAP-ASAP -- are compelling, but sequential swapping holds partial chains in intermediate buffers between successive swaps, exposing them to memory decoherence in a way simultaneous SWAP-ASAP avoids by design. We present a proof-of-principle study at fixed chain length $n = 4$ in which each elementary link is governed by a fixed reinforcement-learning policy optimizing the secret-key rate of the six-state protocol, leaving the network-layer protocol as the sole independent variable. Sweeping the network-layer memory coherence time $T_c^{\mathrm{ext}}$ over four orders of magnitude reveals a clear regime structure governed by the dimensionless ratio $T_c^{\mathrm{ext}}/τ$, where $τ$ is the per-link entanglement heralding latency. Simultaneous SWAP-ASAP delivers a constant rate across the full sweep. Sequential swapping, by contrast, collapses to zero end-to-end deliveries below $T_c^{\mathrm{ext}}/τ= 25$, and begins recovering at $T_c^{\mathrm{ext}}/τ= 50$. It remains limited by the simultaneous rate, which it saturates only at the relaxed end of the sweep. These results suggest that the connection-less penalty is a near-term phenomenon tied to present-day memory coherence rather than a fundamental property of sequential swapping.

Problem

• entanglement swapping
• quantum networks
• memory decoherence
• connection-less architecture
• coherence time

Innovation

• entanglement swapping
• quantum networks
• memory coherence time
• reinforcement learning
• SWAP-ASAP