将量子资源管理接口（QRMI）作为量子与高性能计算集成的统一接口进行研究
Examining QRMI as a Unified Interface for Quantum-HPC Integration
浏览论文内容
中文总结 AI 辅助
研究如何将量子资源集成到高性能计算环境，核心方法是用QRMI通过中间件层提供标准化API。贡献是将QRMI验证扩展到多种工作负载管理器，证明其提供可移植灵活抽象层，能跨环境一致访问异构量子资源。
中文摘要 AI 辅助
量子资源高效且可扩展地集成到高性能计算（HPC）环境中，需要跨多样异构基础设施的资源管理、调度和工作流编排的标准化机制。量子资源管理接口（QRMI）通过一个薄的、与供应商无关的中间件层来应对这一挑战，该层提供用于调度、执行和监控量子工作负载的标准化API，同时将量子资源作为与CPU和GPU并列的一等可调度资源公开。此前工作展示了QRMI与Slurm工作负载管理器的集成，但其在其他工作负载管理器中的适用性未被检验。本文将QRMI的验证扩展到广泛的工作负载管理器，包括PBS、LSF、Grid Engine、Kubernetes和Flux框架，涵盖传统批处理调度器、云原生编排平台和基于图的调度器。研究了与每个环境相关的集成模式、实现要求和特定于调度器的考虑因素，并将QRMI与量子资源集成的替代方法进行比较。证明了QRMI提供了一个可移植且灵活的抽象层，可最大限度减少特定于调度器的修改，同时能在本地和云环境中一致地访问异构量子资源。
英文摘要
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.
