Quantum resources in resource management systems

• URL: http://arxiv.org/abs/2506.10052v1
• Date: Wed, 11 Jun 2025 16:29:49 GMT
• Title: Quantum resources in resource management systems
• Authors: Iskandar Sitdikov, M. Emre Sahin, Utz Bacher, Aleksander Wennersteen, Andrew Damin, Mark Birmingham, Philippa Rubin, Stefano Mensa, Matthieu Moreau, Aurelien Nober, Hitomi Takahashi, Munetaka Ohtani,
• Abstract summary: This paper presents the design architecture and reference implementation for quantum resources control using existing workload management systems.<n>We introduce a suite of plugins for Slurm that enable integration of on-prem and cloud quantum computing resources into existing high-performance computing centers.
• Score: 30.432877421232842
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum computers are beginning to operate in high-performance computing (HPC) environments. Quantum can complement classical resources for specific workloads, but their adoption depends on integration into existing HPC infrastructure. Treating quantum devices as first-class resources allows for unified scheduling, improved usability, and support for hybrid quantum-classical applications. This paper presents the design architecture and reference implementation for quantum resources control using existing workload management systems. We introduce a suite of plugins for Slurm that enable integration of on-prem and cloud quantum computing resources into existing high-performance computing centers. The paper details the interface design, plugin concept and implementation, operational aspects for heterogeneous compute clusters, as well as considerations for other resource management systems.

Related papers

• Dynamic Solutions for Hybrid Quantum-HPC Resource Allocation [1.2178560464083517]
  This paper presents a novel malleability-based approach, alongside a workflow-based strategy, to optimize resource utilization in hybrid HPC-quantum workloads.<n>Our experiments with a hybrid HPC-quantum use case show the benefits of dynamic allocation, highlighting the potential of those solutions.
  arXiv  Detail & Related papers  (2025-08-06T08:50:27Z)
• QAOA in Quantum Datacenters: Parallelization, Simulation, and Orchestration [0.0]
  We present a massively parallelized, automated QAOA workflow that automates problem decomposition, job generation, and high-performance simulation.<n>Our framework simulator selection, optimize execution across distributed, heterogeneous resources, and provides a cloud-based infrastructure.<n>We find that QAOA does not significantly degrade optimization performance and often outperforms classical solvers.
  arXiv  Detail & Related papers  (2025-03-08T14:30:00Z)
• Building a Software Stack for Quantum-HPC Integration [0.9360388224886863]
  We propose a hardware-agnostic software framework that supports both current intermediate-scale quantum devices and future fault-tolerant quantum computers.<n>The architecture includes a quantum gateway interface, standardized APIs for resource management, and robust scheduling mechanisms.<n>Key innovations include: (1) a unified resource management system that efficiently coordinates quantum and classical resources, (2) a flexible quantum programming interface that abstracts hardware-specific details, and (4) a comprehensive tool chain for quantum circuit optimization and execution.
  arXiv  Detail & Related papers  (2025-03-03T18:18:45Z)
• Pilot-Quantum: A Quantum-HPC Middleware for Resource, Workload and Task Management [1.381966718755792]
  Pilot-Quantum is designed to provide unified application-level management of resources and workloads across hybrid quantum-classical environments.<n>It implements the Pilot Abstraction conceptual model, originally developed for HPC, to manage resources, workloads, and tasks.
  arXiv  Detail & Related papers  (2024-12-24T15:55:46Z)
• A Framework for Integrating Quantum Simulation and High Performance Computing [0.0]
  We describe a framework to help streamline access to quantum simulation software running on HPC resources. This includes an interface for circuit-based quantum computing tasks, as well as the necessary resource management infrastructure.
  arXiv  Detail & Related papers  (2024-08-15T11:48:14Z)
• Quantum Computing Enhanced Service Ecosystem for Simulation in Manufacturing [56.61654656648898]
  We propose a framework for a quantum computing-enhanced service ecosystem for simulation in manufacturing. We analyse two high-value use cases with the aim of a quantitative evaluation of these new computing paradigms for industrially-relevant settings.
  arXiv  Detail & Related papers  (2024-01-19T11:04:14Z)
• Generative AI-enabled Quantum Computing Networks and Intelligent Resource Allocation [80.78352800340032]
  Quantum computing networks execute large-scale generative AI computation tasks and advanced quantum algorithms. efficient resource allocation in quantum computing networks is a critical challenge due to qubit variability and network complexity. We introduce state-of-the-art reinforcement learning (RL) algorithms, from generative learning to quantum machine learning for optimal quantum resource allocation.
  arXiv  Detail & Related papers  (2024-01-13T17:16:38Z)
• A Conceptual Architecture for a Quantum-HPC Middleware [1.82035221675293]
  Quantum computing promises potential for science and industry by solving certain computationally complex problems faster than classical computers. With the increasing scale, systems that facilitate the efficient coupling of quantum-classical computing are becoming critical.
  arXiv  Detail & Related papers  (2023-08-12T16:48:56Z)
• Elastic Entangled Pair and Qubit Resource Management in Quantum Cloud Computing [73.7522199491117]
  Quantum cloud computing (QCC) offers a promising approach to efficiently provide quantum computing resources. The fluctuations in user demand and quantum circuit requirements are challenging for efficient resource provisioning. We propose a resource allocation model to provision quantum computing and networking resources.
  arXiv  Detail & Related papers  (2023-07-25T00:38:46Z)
• Stochastic Qubit Resource Allocation for Quantum Cloud Computing [66.97282014860265]
  In quantum cloud computing, quantum cloud providers provision quantum resources in reservation and on-demand plans for users. We propose a quantum resource allocation for the quantum cloud computing system in which quantum resources and the minimum waiting time of quantum circuits are jointly optimized.
  arXiv  Detail & Related papers  (2022-10-22T04:13:24Z)
• DQC$^2$O: Distributed Quantum Computing for Collaborative Optimization in Future Networks [54.03701670739067]
  We propose an adaptive distributed quantum computing approach to manage quantum computers and quantum channels for solving optimization tasks in future networks. Based on the proposed approach, we discuss the potential applications for collaborative optimization in future networks, such as smart grid management, IoT cooperation, and UAV trajectory planning.
  arXiv  Detail & Related papers  (2022-09-16T02:44:52Z)
• Optimal Stochastic Resource Allocation for Distributed Quantum Computing [50.809738453571015]
  We propose a resource allocation scheme for distributed quantum computing (DQC) based on programming to minimize the total deployment cost for quantum resources. The evaluation demonstrates the effectiveness and ability of the proposed scheme to balance the utilization of quantum computers and on-demand quantum computers.
  arXiv  Detail & Related papers  (2022-09-16T02:37:32Z)
• Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the Race to Practical Quantum Advantage [43.3054117987806]
  We introduce a scalable procedure for harnessing classical computing resources to provide pre-optimized initializations for quantum circuits. We show this method significantly improves the trainability and performance of PQCs on a variety of problems. By demonstrating a means of boosting limited quantum resources using classical computers, our approach illustrates the promise of this synergy between quantum and quantum-inspired models in quantum computing.
  arXiv  Detail & Related papers  (2022-08-29T15:24:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.