research
Quantum Requests Compete for Purified Links
Reinforcement-learning schedulers served more requests in simulation while consuming more Bell pairs.
Summary
Reinforcement-learning schedulers served more requests in simulation while consuming more Bell pairs.
Dynamic quantum networks must schedule purification across shared links and memories. Simulated RL schedulers improved request success over a mixed-integer program by up to 6 percent and over two heuristics by 16.5 and 19.5 percent, while using more Bell pairs and deferring some traffic. The optimization remains a network simulation study.
Why it matters
Reinforcement-learning schedulers served more requests in simulation while consuming more Bell pairs.
Limits and context
No additional limitation was separately recorded.
Key claims
Reinforcement-learning schedulers served more requests in simulation while consuming more Bell pairs.
Evidence: source-2026-10-21-018
Sources
- arXiv preprint 2610.26900, original v1arXiv · primary research
Corrections
No corrections have been recorded for this story.