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arxiv: 2508.04217 · v1 · submitted 2025-08-06 · 🪐 quant-ph · cs.DC

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Dynamic Solutions for Hybrid Quantum-HPC Resource Allocation

Roberto Rocco , Simone Rizzo , Matteo Barbieri , Gabriella Bettonte , Elisabetta Boella , Fulvio Ganz , Sergio Iserte , Antonio J. Pe\~na
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Petter Sand\r{a}s Alberto Scionti Olivier Terzo Chiara Vercellino Giacomo Vitali Paolo Viviani Jonathan Frassineti Sara Marzella Daniele Ottaviani Iacopo Colonnelli Daniele Gregori
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classification 🪐 quant-ph cs.DC
keywords quantumallocationhybridresourceclassicalcomputersdynamichpc-quantum
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The integration of quantum computers within classical High-Performance Computing (HPC) infrastructures is receiving increasing attention, with the former expected to serve as accelerators for specific computational tasks. However, combining HPC and quantum computers presents significant technical challenges, including resource allocation. This paper presents a novel malleability-based approach, alongside a workflow-based strategy, to optimize resource utilization in hybrid HPC-quantum workloads. With both these approaches, we can release classical resources when computations are offloaded to the quantum computer and reallocate them once quantum processing is complete. Our experiments with a hybrid HPC-quantum use case show the benefits of dynamic allocation, highlighting the potential of those solutions.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. A Test Taxonomy and Continuous Integration Ecosystem for Dynamic Resource Management in HPC

    cs.DC 2026-04 unverdicted novelty 6.0

    Introduces a test taxonomy and HPC CI ecosystem to improve validation of dynamic resource management frameworks, evaluated via the DMR case study.

  2. Hybrid Quantum-HPC Middleware Systems for Adaptive Resource, Workload and Task Management

    quant-ph 2026-04 unverdicted novelty 5.0

    The authors present Pilot-Quantum, a middleware for adaptive resource management in hybrid quantum-HPC systems, along with execution motifs and a performance modeling toolkit called Q-Dreamer.