QFOR is a PPO-trained scheduler that claims 29.5-84% fidelity gains over heuristics for quantum jobs in noisy heterogeneous clouds, using IBM calibration data as its noise model.
2024; Zhang et al
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
quant-ph 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
QFOR: A Fidelity-aware Orchestrator for Quantum Computing Environments using Deep Reinforcement Learning
QFOR is a PPO-trained scheduler that claims 29.5-84% fidelity gains over heuristics for quantum jobs in noisy heterogeneous clouds, using IBM calibration data as its noise model.