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Kubernetes-Orchestrated Hybrid Quantum-Classical Workflows

Mar Tejedor, Michele Grossi, Cenk Tüysüz, Ricardo Rocha, Sofia Vallecorsa

Abstract

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.

Kubernetes-Orchestrated Hybrid Quantum-Classical Workflows

Abstract

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.
Paper Structure (16 sections, 4 figures)

This paper contains 16 sections, 4 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. Background
  4. Heterogeneous Resources
  5. Kubernetes for Scientific Workflows
  6. System Architecture
  7. Kubernetes as the Orchestration Backbone
  8. Integration of Quantum Resources in Kubernetes
  9. Workflow Management with Argo Workflows
  10. Queue-Based Scheduling with Kueue
  11. Monitoring and Observability
  12. Proof of Concept: Hybrid Circuit-Cutting Workflow
  13. Workflow Pipeline
  14. Argo DAG Definition and Execution
  15. Discussion
  16. ...and 1 more sections

Figures (4)

  • Figure 1: Kubernetes-native hybrid quantum–HPC workflow architecture.
  • Figure 2: Example of an Argo DAG representation: the left shows a generic Fan Out/Fan In workflow, while the right panel illustrates a specific application workflow.
  • Figure 3: Schematic overview applying 3 cuts in a HEA circuit
  • Figure 4: Concurrent subcircuit execution across heterogeneous nodes. Argo manages data dependencies while Kueue balances workloads between CPU, GPU, and QPU resources.