TBNQuantum
Concurrent Entanglement Purification Scheduling in Dynamic Quantum Networks
Publication: quant-ph updates on arXiv.org
Author: Gongyu Ni, Lester Ho
Source published: 21/10/2026, 04:00:00 UTC
Briefing created: 21/10/2026, 04:22:08 UTC
Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 21/10/2026, 04:13:24 UTC.
Briefings
A 2026 study on arXiv:2610.26900v1 presents a mixed-integer linear programming (MILP) scheduler for concurrent entanglement purification in dynamic quantum networks, alongside reinforcement learning (RL) approaches using DQN, PPO, and SAC. Simulations indicate RL schedulers achieve higher success rates by up to 6% compared to MILP, though at the cost of increased Bell-pair consumption and request deferral. The research addresses scheduling requests competing for link capacity and quantum memories over time-slotted networks with heterogeneous links.
Key points
- The study formulates a mixed-integer linear programming (MILP) scheduler that maximizes scheduled requests while using max-min path allocation as a fairness criterion.
- Reinforcement learning schedulers based on DQN, PPO, and SAC achieve higher success rates than MILP by up to 6% in simulations, though they consume more Bell pairs and may defer requests.
Context
Quantum networks require end-to-end entanglement purification for multi-hop paths, but existing static scheduling methods fail to handle concurrent requests competing for resources over time. This paper addresses this gap by modeling heterogeneous links and time-slotted dynamics.
Limitations
Simulations vary network load, topologies, and target fidelity thresholds. The study does not provide causal claims or specific causal mechanisms for performance differences between schedulers. Results are based on simulation data and may not generalize to real-world conditions.
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