Hybrid Quantum and Classical Workload Management with Graph-based Scheduling
Abstract
High Performance Computing (HPC) centers are expanding to integrate quantum resources, enabling hybrid quantum-classical workflows for complex optimization. Integrating quantum processing units (QPUs) into workload managers poses an orchestration challenge: a remote QPU introduces a second queue - a "two-queue problem" - alongside the scheduler's own. We present Fluence, a Kubernetes scheduler plugin backed by the Fluxion graph-based scheduler, enabling gang-scheduled placement for quantum-classical workloads and custom resources. First, under contention, Fluence's atomic gang placement eliminates the node-time a default scheduler wastes on partially placed gangs. Second, a synchronization primitive gates consumers behind a single producer's shared quantum task, cutting worker idle time roughly 1.2-12x under short queues and orders of magnitude under long ones. Third, policy-aware backend selection cuts mean per-run cost roughly 72x and time-to-result from hours to under two minutes. Together, these results show that quantum-awareness can be added to a cloud-native scheduler without modifying user containers.
Related
- Hardware-Friendly Estimation of LCU-Based Expectation Values via Quantum-to-Classical Workload Shifting
- Entanglement Swapping Scheduling for Quantum Repeater Chains under Decoherence during Classical Communications
- Exact diffusion coefficients for quantum--classical hybrid walks
- Learning nuclear mass systematics with hybrid quantum-classical mixtures of experts
- Hybrid Variational Quantum-Classical Framework with Adaptive Weighting and Efficiency Assessment
BibTeX
@misc{sochat2026hybrid,
title = {{Hybrid Quantum and Classical Workload Management with Graph-based Scheduling}},
author = {Vanessa Sochat and Daniel Milroy},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2607.09151},
eprint = {2607.09151},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2607.09151},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Hybrid Quantum and Classical Workload Management with Graph-based Scheduling
AU - Vanessa Sochat
AU - Daniel Milroy
PY - 2026
DA - 2026/09/08
PB - arXiv
DO - 10.48550/arxiv.2607.09151
UR - https://arxiv.org/abs/2607.09151
AB - High Performance Computing (HPC) centers are expanding to integrate quantum resources, enabling hybrid quantum-classical workflows for complex optimization. Integrating quantum processing units (QPUs) into workload managers poses an orchestration challenge: a remote QPU introduces a second queue - a "two-queue problem" - alongside the scheduler's own. We present Fluence, a Kubernetes scheduler plugin backed by the Fluxion graph-based scheduler, enabling gang-scheduled placement for quantum-classical workloads and custom resources. First, under contention, Fluence's atomic gang placement eliminates the node-time a default scheduler wastes on partially placed gangs. Second, a synchronization primitive gates consumers behind a single producer's shared quantum task, cutting worker idle time roughly 1.2-12x under short queues and orders of magnitude under long ones. Third, policy-aware backend selection cuts mean per-run cost roughly 72x and time-to-result from hours to under two minutes. Together, these results show that quantum-awareness can be added to a cloud-native scheduler without modifying user containers.
ER -