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Fluence introduces a graph-based Kubernetes scheduler plugin that optimizes hybrid quantum-classical workflows by intelligently routing tasks to the most cost-effective or available AWS Braket devices. By solving synchronization delays between classical and remote processors, this tool achieves a 70-fold reduction in per-run costs while significantly minimizing worker idle time during long queues. This advancement makes large-scale scientific applications on cloud-native infrastructure more accessible and resource-efficient without requiring modifications to user containers.
Hybrid Quantum and Classical Workload Management with Graph-based Scheduling
Fluence, a Kubernetes plugin using the Fluxion graph-based scheduler, cuts AWS quantum device costs by 70×.
Researchers at Lawrence Livermore National Laboratory have developed Fluence, a new Kubernetes scheduler plugin that reduces the cost of utilizing quantum computing resources. By intelligently pinning workloads to the cheapest or shortest-queue device available on AWS Braket, Fluence achieves a roughly 70-fold reduction in mean per-run cost. This advancement addresses a key challenge in hybrid quantum-classical workflows: coordinating tasks between classical and remote quantum processors introduces significant delays. Fluence incorporates a synchronization primitive that reduces worker idle time by approximately five times under short queues, and by several orders of magnitude when device queues extend to hours, enabling faster results and more efficient resource allocation. A roughly 70-fold reduction in mean per-run…
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