Kubernetes-Orchestrated Hybrid Quantum-Classical Workflows
This addresses the problem of resource orchestration for researchers and developers in quantum computing, but it is incremental as it builds on existing cloud-native tools.
The paper tackles the challenge of coordinating hybrid quantum-classical workflows by presenting a cloud-native framework using Kubernetes, Argo Workflows, and Kueue, which enables scalable and reproducible execution across heterogeneous resources, demonstrated with a proof-of-concept implementation of distributed quantum circuit cutting.
Hybrid quantum-classical workflows combine quantum processing units (QPUs) with classical hardware to address computational tasks that are challenging or infeasible for conventional systems alone. Coordinating these heterogeneous resources at scale demands robust orchestration, reproducibility, and observability. Even in the presence of fault-tolerant quantum devices, quantum computing will continue to operate within a broader hybrid ecosystem, where classical infrastructure plays a central role in task scheduling, data movement, error mitigation, and large-scale workflow coordination. In this work, we present a cloud-native framework for managing hybrid quantum-HPC pipelines using Kubernetes, Argo Workflows, and Kueue. Our system unifies CPUs, GPUs, and QPUs under a single orchestration layer, enabling multi-stage workflows with dynamic, resource-aware scheduling. We demonstrate the framework with a proof-of-concept implementation of distributed quantum circuit cutting, showcasing execution across heterogeneous nodes and integration of classical and quantum tasks. This approach highlights the potential for scalable, reproducible, and flexible hybrid quantum-classical computing in cloud-native environments.