Qurator jointly optimizes queue time and fidelity for hybrid quantum-classical workflows across providers using quantum-aware DAG scheduling and a unified logarithmic fidelity score, achieving 30-75% wait reduction at high load with bounded accuracy cost.
Performance-effective and low-complexity task scheduling for heterogeneous computing
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
A new series-parallel decomposition algorithm for general DAGs enables task mapping in heterogeneous systems that improves makespan over HEFT variants while running orders of magnitude faster than genetic algorithms or ILPs.
citing papers explorer
-
Qurator: Scheduling Hybrid Quantum-Classical Workflows Across Heterogeneous Cloud Providers
Qurator jointly optimizes queue time and fidelity for hybrid quantum-classical workflows across providers using quantum-aware DAG scheduling and a unified logarithmic fidelity score, achieving 30-75% wait reduction at high load with bounded accuracy cost.
-
Static task mapping for heterogeneous systems based on series-parallel decompositions
A new series-parallel decomposition algorithm for general DAGs enables task mapping in heterogeneous systems that improves makespan over HEFT variants while running orders of magnitude faster than genetic algorithms or ILPs.