Quantum Use Case #93: Data-centre workload scheduling

Short answer: This use case proposes treating quantum devices as schedulable first-class resources within high-performance computing (HPC) infrastructure to manage hybrid quantum-classical workloads. It is a research-stage concept where proposed schedulers aim to balance resource contention and optimize job execution, but deployment is not established.

The integration of quantum computing resources into high-performance computing (HPC) environments represents a significant shift in data-centre architecture. As quantum devices are increasingly incorporated as co-processors for hybrid workloads, they must be treated as schedulable first-class resources within existing infrastructure. This approach enables consistent workload management, unified resource visibility, and support for hybrid quantum-classical job execution models. Recent research has proposed reference architectures and implementations to facilitate this integration, addressing the complex scheduling questions that arise when quantum tasks interact with classical computing resources.

A primary challenge in this domain is the abstraction of quantum backends from the underlying hardware complexity. Researchers have proposed systems that allow quantum devices, whether on-premises or cloud-hosted, to be managed via standard workload managers. One such proposal involves a Slurm plugin designed to abstract and control quantum backends. By treating quantum resources as standard schedulable entities, data centres can maintain unified visibility over their resource pools. This abstraction is critical for enabling hybrid workloads where classical and quantum tasks must coexist without manual intervention for resource allocation. The goal is to allow existing HPC infrastructure to manage quantum tasks seamlessly, ensuring that the addition of quantum co-processors does not fragment the operational visibility of the data centre.

Another area of active research focuses on cloud orchestration for hybrid quantum-classical applications. Researchers have described orchestrators that abstract away the complexity of hybrid programming and resource management. These systems expose high-level, hardware-agnostic APIs to simplify the interaction between application developers and the underlying infrastructure. A key component of such orchestration is the resource estimator, which strategically balances quantum and classical resources. This balancing aims to mitigate resource contention and the effects of hardware noise, which are inherent challenges in current quantum hardware. Furthermore, hybrid schedulers in these proposed systems automate job scheduling on hybrid resources. They attempt to balance the tradeoff between user objectives, such as quality of service, and the cloud operator’s objective of resource efficiency. By automating these decisions, the orchestrator can manage heterogeneous hybrid resources more effectively than manual or static allocation strategies.

Specific scheduling algorithms have also been proposed to address the unique characteristics of quantum tasks. One research proposal, SCIM MILQ, is a scheduler for quantum tasks in HPC infrastructure that combines well-established scheduling techniques with methods unique to quantum computing. A notable example of such a unique method is circuit cutting, a technique that allows for the decomposition of quantum circuits to optimize execution. The objective of such schedulers is often to minimize the makespan, defined as the time that elapses from the start of work to the end. In simulations, researchers have shown that such scheduling approaches can improve average makespan by 25 percent compared to baseline methods. This improvement suggests that specialized scheduling logic can yield significant efficiency gains in hybrid environments, provided the scheduler accounts for the specific constraints and capabilities of the quantum hardware.

The integration of these technologies raises questions about access models that go beyond current cloud access paradigms. As quantum hardware becomes more sophisticated and capable, its employment in HPC infrastructure opens largely unexplored scheduling questions. Standard cloud models may not adequately address the latency, noise, and resource constraints of quantum devices. Therefore, new access models and scheduling frameworks are required to support the hybrid nature of these workloads. These frameworks must account for the fact that quantum resources are scarce and distinct from classical compute resources, requiring different prioritization and allocation strategies.

It is important to note that these developments are currently at the research stage. The architectures, plugins, and schedulers described in recent literature are proposed frameworks and have been evaluated in simulation or theoretical contexts. They are not yet operational standards for commercial data centres. The hardware-agnostic APIs and resource estimators discussed are conceptual tools intended to guide future system design. While the potential for improved efficiency and unified resource management is clear, the practical deployment of such systems faces significant hurdles, including the immaturity of quantum hardware and the lack of standardized interfaces.

The proposed solutions emphasize the need for a unified view of resources. By abstracting the quantum backend, data centres can avoid the fragmentation that would occur if quantum devices were managed in isolation. This unified view is essential for supporting hybrid workloads where the boundary between classical and quantum computation is blurred. The ability to schedule quantum tasks alongside classical tasks within a single framework is a prerequisite for scalable quantum-classical computing. Without such integration, the benefits of quantum acceleration would be limited to isolated, manually managed experiments rather than integrated, automated workloads.

Research into these areas continues to explore the tradeoffs between resource efficiency and service quality. The proposed schedulers and orchestrators aim to navigate these tradeoffs automatically, using algorithms that account for the specific characteristics of quantum tasks. However, the effectiveness of these approaches depends on accurate models of the quantum hardware and its noise characteristics. As hardware evolves, the scheduling strategies will need to adapt. The current focus is on establishing the foundational architectures and APIs that will allow for such adaptation in the future.

In summary, the scheduling of data-centre workloads involving quantum resources is an emerging field of research. Proposed solutions include Slurm plugins for backend abstraction, cloud orchestrators for hybrid resource management, and specialized schedulers like SCIM MILQ for optimizing makespan. These approaches aim to integrate quantum devices into existing HPC infrastructure as first-class resources. While simulations suggest potential for significant efficiency improvements, these systems remain in the research phase. The path to operational adoption requires further development of hardware, standardization of interfaces, and validation of scheduling strategies in real-world environments. The ultimate goal is a seamless, automated management of hybrid quantum-classical workloads that maximizes the utility of both resource types.

FAQ

How are quantum resources integrated into HPC?

Quantum devices are treated as schedulable first-class resources within existing HPC infrastructure, enabling consistent workload management and unified resource visibility for hybrid jobs.

What is the role of the Slurm plugin?

The plugin is designed to abstract and control quantum backends, allowing them to be integrated into HPC centers via standard workload managers.

How does the hybrid scheduler manage resources?

It automates job scheduling on hybrid resources by balancing the tradeoff between user quality of service objectives and the cloud operator's objective of resource efficiency.

What specific scheduling technique is mentioned for quantum tasks?

The scheduler combines well-established techniques with methods unique to quantum computing, such as circuit cutting, to minimize the makespan of tasks.

Is this technology currently deployed?

Deployment is not established by current research, as the sources describe reference architectures, proposed schedulers, and research-stage integration models.