Examining QRMI as a Unified Interface for Quantum-HPC Integration
badtst, Tim Boyle, Claudio Carvalho, Antonio D. Corcoles, Andrew Damin, Vadim Elisseev, Jonathan Frassineti, D. Gruber, Hiroshi Horii, E R Lee, James Machin, Sara Marzella, Mateusz Meller, Daniel J. Milroy, Matthieu Moreau, Munetaka Ohtani, Elisabeth Ortega‐Carrasco, Doug Oucharek, Yoonho Park, Adarsh Patil, Emre M. Sahin, G. Samu, Seetharami Seelam, Amir Shehata, Vanessa Sochat, James Thorne, Oscar Wallis, Aleksander Wennersteen
The efficient and scalable integration of quantum resources into high-performance computing (HPC) environments requires standardized mechanisms for resource management, scheduling, and workflow orchestration across diverse and heterogeneous infrastructures. The Quantum Resource Management Interface (QRMI) addresses this challenge through a thin, vendor-agnostic middleware layer that provides standardized APIs for scheduling, executing, and monitoring quantum workloads while exposing quantum resources as first-class schedulable resources alongside CPUs and GPUs. Although previous work demonstrated QRMI integration with the Slurm workload manager, its applicability across other workload managers remained unexamined. This paper extends the validation of QRMI to a broad range of workload managers, including PBS, LSF, Grid Engine, Kubernetes, and the Flux Framework, encompassing traditional batch schedulers, a cloud-native orchestration platform, and a graph-based scheduler. We examine the integration patterns, implementation requirements, and scheduler-specific considerations associated with each environment and compare QRMI with alternative approaches to quantum resource integration. We demonstrate that QRMI provides a portable and flexible abstraction layer that minimizes scheduler-specific modifications while enabling consistent access to heterogeneous quantum resources across both on-premises and cloud environments.
The efficient and scalable integration of quantum resources into high-performance computing (HPC) environments requires standardized mechanisms for resource management, scheduling, and workflow orchestration across diverse and heterogeneous infrastructures. The Quantum Resource Management Interface (QRMI) addresses this challenge through a thin, vendor-agnostic middleware layer that provides standardized APIs for scheduling, executing, and monitoring quantum workloads while exposing quantum resources as first-class schedulable resources alongside CPUs and GPUs. Although previous work demonstrated QRMI integration with the Slurm workload manager, its applicability across other workload managers remained unexamined. This paper extends the validation of QRMI to a broad range of workload managers, including PBS, LSF, Grid Engine, Kubernetes, and the Flux Framework, encompassing traditional batch schedulers, a cloud-native orchestration platform, and a graph-based scheduler. We examine the integration patterns, implementation requirements, and scheduler-specific considerations associated with each environment and compare QRMI with alternative approaches to quantum resource integration. We demonstrate that QRMI provides a portable and flexible abstraction layer that minimizes scheduler-specific modifications while enabling consistent access to heterogeneous quantum resources across both on-premises and cloud environments.
Hybrid quantum-classical applications pose significant resource management challenges due to heterogeneity and dynamism in both infrastructure and workloads. Quantum-HPC environments integrate quantum processing units (QPUs) with diverse classical resources (CPUs, GPUs), while applications span coupling patterns from tightly coupled execution to loosely coupled task parallelism with varying resource requirements. Traditional HPC schedulers lack visibility into application semantics and cannot respond to fluctuating resource availability at runtime. This paper presents a middleware-based approach for adaptive resource, workload, and task management in hybrid quantum-HPC systems. We make four contributions: (i) a conceptual four-layer middleware architecture that decomposes management across workflow, workload, task, and resource levels, enabling application-aware scheduling over heterogeneous quantum-HPC resources; (ii) a set of execution motifs capturing interaction and coupling characteristics of hybrid applications, realized as quantum mini-apps for systematic workload characterization; (iii) Pilot-Quantum, a middleware framework built on the pilot abstraction that enables late binding and dynamic resource allocation, adapting to resource and workload dynamics at runtime; and (iv) Q-Dreamer, a performance modeling toolkit providing reusable components for informed workload partitioning, including a circuit-cutting optimizer that analytically derives optimal partitioning strategies. Evaluation on heterogeneous HPC platforms (Perlmutter, NVIDIA DGX with H100/B200 GPUs) demonstrates efficient multi-backend orchestration across CPUs, GPUs, and QPUs for diverse execution motifs. Q-Dreamer predicts optimal circuit cutting configurations with up to 82% accuracy.
High-performance computing (HPC) has evolved over decades through multiple architectural transitions, from vector supercomputers to massively parallel CPU clusters and GPU-accelerated systems, continuously expanding the frontier of scientific discovery. With the emergence of quantum processing units (QPUs) as practical computational accelerators, a new opportunity arises to further extend this trajectory by integrating quantum and classical computing paradigms. Building on the emerging vision of Quantum Integrated High-Performance Computing (QHPC), this paper contributes a full-stack layered architecture that integrates QPUs as first-class accelerators within, not merely alongside, the classical HPC software and hardware stack, with tight, on-premise quantum-classical coupling as its defining characteristic. The architecture comprises of unified resource management, quantum-aware scheduling, hybrid workflow orchestration, middleware and programming abstraction, interconnect technologies, and a tiered execution model enabling seamless workload partitioning across classical and quantum backends. A central aspect of this architecture is a strong user requests abstraction layer that exposes heterogeneous resources through a unified job submission interface, similar in spirit to existing schedulers such as Slurm, allowing users to describe workloads in a consistent template independent of underlying compute type or location. Drawing insights from prior accelerator integration eras, we outline how QHPC can support emerging workloads in quantum chemistry, materials discovery, combinatorial optimization, and climate modeling. We conclude by highlighting open challenges in building scalable, reliable, and programmable quantum-classical infrastructures that seamlessly connect global users to heterogeneous compute resources for future quantum-classical HPC ecosystems.
Quantum computing resources are increasingly being incorporated into high-performance computing (HPC) environments as co-processors for hybrid workloads. To support this paradigm, quantum devices must be treated as schedulable first-class resources within existing HPC infrastructure. This enables consistent workload management, unified resource visibility, and support for hybrid quantum-classical job execution models. This paper presents a reference architecture and implementation for the integration of quantum computing resources, both on-premises and cloud-hosted into HPC centers via standard workload managers. We introduce a Slurm plugin designed to abstract and control quantum backends, enabling seamless resource scheduling, minimizing queue duplication, and supporting job co-scheduling with classical compute nodes. The architecture supports heterogeneous quantum resources and can be extended to any workload (and container) management systems.
High-performance computing (HPC) has evolved over decades through multiple architectural transitions, from vector supercomputers to massively parallel CPU clusters and GPU-accelerated systems, continuously expanding the frontier of scientific discovery. With the emergence of quantum processing units (QPUs) as practical computational accelerators, a new opportunity arises to further extend this trajectory by integrating quantum and classical computing paradigms. Building on the emerging vision of Quantum Integrated High-Performance Computing (QHPC), this paper contributes a full-stack layered architecture that integrates QPUs as first-class accelerators within, not merely alongside, the classical HPC software and hardware stack, with tight, on-premise quantum–classical coupling as its defining characteristic. The architecture comprises of unified resource management, quantum-aware scheduling, hybrid workflow orchestration, middleware and programming abstraction, interconnect technologies, and a tiered execution model enabling seamless workload partitioning across classical and quantum backends. A central aspect of this architecture is a strong user requests abstraction layer that exposes heterogeneous resources through a unified job submission interface, similar in spirit to existing schedulers such as Slurm, allowing users to describe workloads in a consistent template independent of underlying compute type or location. Drawing insights from prior accelerator integration eras, we outline how QHPC can support emerging workloads in quantum chemistry, materials discovery, combinatorial optimization, and climate modeling. We conclude by highlighting open challenges in building scalable, reliable, and programmable quantum–classical infrastructures that seamlessly connect global users to heterogeneous compute resources for future quantum–classical HPC ecosystems.
The convergence of quantum computing and high-performance computing (HPC) is giving rise to hybrid cloud platforms that coordinate workflows across classical and quantum resources. These heterogeneous environments introduce new challenges for workload management, including device variability, noise-prone quantum processors, and the need for synchronized orchestration across distributed systems. This survey reviews state-of-the-art techniques for scheduling, resource allocation, and workflow management in classical HPC, quantum clouds, and emerging hybrid infrastructures. A taxonomy is proposed to categorize workload management approaches by scheduling strategy, allocation model, orchestration mechanism, and performance metric. We present HybridCloudSim, a novel simulation framework designed to model hybrid quantum–HPC cloud environments and optimize resource usage under realistic constraints. HybridCloudSim is a conceptual foundation and a practical tool for advancing workload management in hybrid quantum–HPC systems.
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.