Examining QRMI as a Unified Interface for Quantum-HPC Integration

2026-07-21

AI Summary

This work addresses the lack of a unified mechanism for efficiently integrating and scheduling quantum computing resources within heterogeneous high-performance computing (HPC) environments. To bridge this gap, the authors propose QRMI, a lightweight, vendor-agnostic middleware that abstracts quantum resources as schedulable units and enables their co-management alongside classical CPU and GPU resources. QRMI seamlessly interoperates with diverse job schedulers—including PBS, LSF, Grid Engine, Kubernetes, and Flux—through a standardized API and modular architecture. Without requiring deep modifications to existing schedulers, QRMI achieves consistent, cross-platform access to and efficient scheduling of quantum resources for the first time. Experimental validation demonstrates its generality and practicality across both on-premises and cloud-based heterogeneous infrastructures.

Abstract

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.

Problem

• quantum-HPC integration
• resource management
• workload managers
• heterogeneous infrastructures
• standardized interface

Innovation

• QRMI
• quantum-HPC integration
• workload management
• resource abstraction
• heterogeneous scheduling