Kubernetes-Orchestrated Hybrid Quantum-Classical Workflows

2026-03-25

AI Summary

This work proposes the first cloud-native scheduling framework systematically designed for hybrid quantum-classical computing to address the challenge of efficiently orchestrating heterogeneous computational resources at scale. Built upon Kubernetes, Argo Workflows, and Kueue, the framework enables unified, resource-aware dynamic scheduling across CPUs, GPUs, and quantum processing units (QPUs), supporting multi-stage, reproducible, and observable hybrid workflows. The effectiveness of the framework is demonstrated through end-to-end collaborative experiments on distributed quantum circuit cutting tasks, which highlight its significant advantages in scalability, flexibility, and reproducibility.

Technology Category

• Machine Learning: Quantum Machine Learning
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• hybrid quantum-classical workflows
• resource orchestration
• heterogeneous computing
• quantum-classical integration
• scalable workflow management

Innovation

• Kubernetes
• hybrid quantum-classical workflows
• quantum circuit cutting
• cloud-native orchestration
• heterogeneous resource scheduling