Quantum and Classical Computers Now Share a Unified Management System
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Researchers at the Barcelona Supercomputing Centre, in collaboration with CERN, have developed a new framework for effectively integrating quantum and classical computing resources to address complex computational challenges. Mar Tejedor and colleagues present a cloud-native system leveraging Kubernetes, Argo Workflows, and Kueue to manage hybrid quantum-high performance computing pipelines. This system integrates central processing units (CPUs), graphics processing units (GPUs), and quantum processing units (QPUs) under unified orchestration, enabling dynamic resource scheduling and reproducible workflows. It provides a scalable approach to hybrid quantum-classical computing, crucial for harnessing the benefits of both technologies as quantum devices continue to develop within a broader classical infrastructure. Unified resource management for scalable hybrid quantum-classical computation Previously, coordinating hybrid quantum-classical workflows suffered from a lack of integrated organisation. Existing approaches often treated quantum and classical resources as separate entities, requiring manual intervention and hindering scalability and reproducibility. This new framework now provides a unified system capable of managing pipelines with up to three distinct resource types: CPUs, GPUs, and quantum processing units, all under a single orchestration layer. Resource management and workflow coordination were formerly addressed separately, creating bottlenecks and limiting the potential for complex computations. This multi-resource integration overcomes these limitations by providing a cohesive environment for executing hybrid algorithms. The system is built upon a robust foundation of cloud-native technologies. Kubernetes, a widely adopted container orchestration platform, provides the underlying infrastructure for deploying and managing the various computational resources. Argo Workflows, a workflow engine for Kubernetes, enables the definition and execution of complex pipelines as Directed Acyclic Graphs (DAGs) using YAML-based configuration files. Kueue, a Kubernetes-native queuing system, facilitates dynamic, resource-aware scheduling, particularly important given the limited availability and unique characteristics of quantum processing units. The combination of these technologies allows the framework to intelligently allocate tasks to the most appropriate hardware, maximising efficiency and throughput. This is especially critical as QPUs currently represent a limited and expensive resource. A proof-of-concept implementation involving distributed quantum circuit cutting demonstrates execution across these heterogeneous nodes, validating the framework’s potential for complex quantum-classical computations. Quantum circuit cutting is a technique used to decompose large quantum circuits into smaller, more manageable fragments that can be executed on hardware with limited qubit connectivity or coherence times. The framework dynamically schedules tasks, as evidenced by the partitioning of large quantum circuits into smaller fragments executable on different hardware accelerators. Reproducibility and scalability are ensured through secure API token management, via Kubernetes Secrets, and the declarative workflow definition using YAML-based Directed Acyclic Graphs (DAGs). This declarative approach allows workflows to be version controlled and easily reproduced, essential for scientific research and development. Real-time metrics on task states, resource utilisation, and workflow throughput are provided by monitoring with Prometheus and Grafana, allowing for performance tuning and optimisation. Crucially, QPU latency was successfully monitored as a key metric, providing insights into the performance of the quantum hardware and identifying potential bottlenecks. The current backend selection policy remains a simplified example, however, and does not yet reflect the complexities of optimising for real-world quantum hardware limitations or error rates; it currently prioritises resource availability over performance characteristics. Establishing a unified platform for hybrid quantum-classical computation At the Barcelona Supercomputing Centre, researchers are actively building the infrastructure for a future where quantum computers seamlessly integrate with existing high-performance computing infrastructure. The convergence of CPUs, GPUs, and quantum processors promises to unlock more complex calculations than either could manage alone. This is because certain computational tasks are inherently well-suited to classical hardware, while others benefit significantly from the unique capabilities of quantum computers, such as superposition and entanglement. By combining these strengths, hybrid algorithms can achieve significant performance gains. Such infrastructure will be vital even as quantum computers improve, enabling broader hybrid workflows for tasks like complex simulations in materials science, drug discovery, financial modelling, and large-scale data analysis. The ability to offload computationally intensive tasks to the most appropriate hardware will be crucial for maximising efficiency and accelerating scientific discovery. The current implementation’s simplified backend selection policy limits its ability to optimise for the subtle characteristics of real-world quantum hardware, including error rates, qubit connectivity, and performance variability. Quantum computers are susceptible to noise and errors, which can significantly impact the accuracy of computations. Developing strategies to mitigate these errors and optimise workflows for specific hardware limitations is a major research challenge. This work establishes a foundation for exploring more intricate quantum algorithms and raises important questions regarding automated backend selection to optimise for the specific limitations of varied quantum hardware. Future work will focus on developing more sophisticated scheduling algorithms that take into account these factors, as well as exploring techniques for error mitigation and fault tolerance. By orchestrating CPUs, GPUs, and quantum processing units under Kubernetes, the system enables dynamic scheduling and reproducible workflows, representing a major advancement beyond previous, isolated resource management approaches. Distributed quantum circuit cutting serves as a compelling demonstration of this capability, proving the potential for complex calculations spanning heterogeneous hardware and paving the way for more sophisticated hybrid quantum-classical applications. The researchers successfully demonstrated a system for managing combined quantum and classical computing tasks using cloud-based Kubernetes, Argo Workflows, and Kueue. This matters because it allows complex calculations, such as those used in materials science and drug discovery, to utilise the strengths of both quantum processing units and conventional CPUs/GPUs, improving efficiency. The proof-of-concept implementation used distributed quantum circuit cutting to show how these different components can work together. Future work will concentrate on refining scheduling algorithms to account for the specific limitations of quantum hardware and improving error mitigation techniques. 👉 More information🗞 Kubernetes-Orchestrated Hybrid Quantum-Classical Workflows🧠 ArXiv: https://arxiv.org/abs/2603.24206 Tags: