Hybrid Quantum and Classical Workload Management with Graph-based Scheduling
TL;DR
Fluence, a Kubernetes scheduler plugin backed by the Fluxion graph-based scheduler, enabling gang-scheduled placement for quantum-classical workloads and custom resources and shows that quantum-awareness can be added to a cloud-native scheduler without modifying user containers.
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.