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A Learned Quantum Scheduler Held Success With 71% Weaker Links
Message-passing reinforcement learning scheduled simultaneous entanglement requests across tested network topologies.
Summary
Message-passing reinforcement learning scheduled simultaneous entanglement requests across tested network topologies.
The authors formulated simultaneous quantum-network requests as a Markov decision process and trained graph-based double deep-Q policies. On tested physically relevant topologies, the learned policies maintained 100% success at link-activation probabilities up to 71% lower than heuristic baselines. With hardware-placement restrictions, they retained at least 80% success at probabilities up to 59% lower. These are modeled network results, not deployed quantum-internet measurements.
Why it matters
Message-passing reinforcement learning scheduled simultaneous entanglement requests across tested network topologies.
Limits and context
- These are modeled network results, not deployed quantum-internet measurements.
Key claims
Message-passing reinforcement learning scheduled simultaneous entanglement requests across tested network topologies.
Qualification: These are modeled network results, not deployed quantum-internet measurements.
Evidence: source-2026-09-25-009
Sources
- arXiv preprint 2609.30157arXiv · primary research
Corrections
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