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arxiv: 2604.14955 · v1 · submitted 2026-04-16 · 🪐 quant-ph

Recognition: unknown

Three ways to share a QPU: Scheduling strategies for hybrid Quantum-HPC applications

Marco Cipollini , Simone Rizzo , Sergio Iserte , Paolo Viviani , Giacomo Vitali , Matteo Barbieri , Gabriella Bettonte , Elisabetta Boella
show 16 more authors
Fulvio Ganz Roberto Rocco Orazio Spina Antonio J. Pe\~na Petter Sand{\aa}s Iacopo Colonnelli Alberto Scionti Chiara Vercellino Emanuele Dri Jonathan Frassineti Sara Marzella Andrea Muratori Daniele Ottaviani Olivier Terzo Bartolomeo Montrucchio Daniele Gregori
Authors on Pith no claims yet

Pith reviewed 2026-05-10 10:36 UTC · model grok-4.3

classification 🪐 quant-ph
keywords hybrid quantum-classical computingHPC schedulingQPU sharingmalleabilityworkflow decompositiontime multiplexingresource optimization
0
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The pith

Malleability and workflow strategies cut classical resource use in balanced hybrid quantum-HPC jobs by up to 64 percent while time-multiplexing maximizes QPU sharing for imbalanced cases.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines three approaches to scheduling hybrid quantum and classical computations on shared HPC systems that have scarce quantum processors. It shows that malleability and workflow decomposition save the largest amounts of classical computing resources when quantum and classical workloads are roughly equal, whereas running jobs sequentially in time slots improves quantum processor utilization and shortens overall cluster runtimes when one workload dominates. Readers care because quantum hardware is still limited and expensive, so effective sharing determines how many practical hybrid applications can run at scale. The experiments on real production clusters and quantum devices indicate that the three methods are complementary rather than alternatives. No single strategy works best in all situations, so the choice must depend on the balance of the specific job.

Core claim

Malleability and workflow strategies significantly optimize classical resource utilization, reducing consumption by up to 45.7% and 64% respectively, proving to be best fitted for hybrid jobs where quantum and classical workloads are evenly balanced. Conversely, time-multiplexing enhances QPU utilization and reduces execution time at the cluster level, making it the optimal strategy for the opposite context, which is characterized by high classical-quantum workload imbalances. Experimental validation on production HPC clusters and real quantum hardware demonstrates the effectiveness of these approaches under different workload scenarios.

What carries the argument

Three methodologies for HPC-QC resource scheduling: time-based multiplexing, dynamic resource management, and workflow decomposition. These address mismatches between quantum and classical models plus the scarcity of QPUs by controlling how jobs share heterogeneous resources.

Load-bearing premise

The tested workload scenarios and hardware setups are representative of real-world hybrid quantum-classical applications and that the observed resource savings will generalize beyond the specific production clusters and quantum devices used.

What would settle it

Applying the same three scheduling strategies to a fresh collection of hybrid applications on a different production cluster and real quantum hardware, then checking whether classical resource reductions and QPU utilization gains reach or exceed the reported levels.

Figures

Figures reproduced from arXiv: 2604.14955 by Alberto Scionti, Andrea Muratori, Antonio J. Pe\~na, Bartolomeo Montrucchio, Chiara Vercellino, Daniele Gregori, Daniele Ottaviani, Elisabetta Boella, Emanuele Dri, Fulvio Ganz, Gabriella Bettonte, Giacomo Vitali, Iacopo Colonnelli, Jonathan Frassineti, Marco Cipollini, Matteo Barbieri, Olivier Terzo, Orazio Spina, Paolo Viviani, Petter Sand{\aa}s, Roberto Rocco, Sara Marzella, Sergio Iserte, Simone Rizzo.

Figure 1
Figure 1. Figure 1: Positioning of the three proposed scheduling strate￾gies along two orthogonal axes: the time-scale granularity at which quantum and classical workloads alternate (horizontal) and the degree of programming-model transparency, i.e., the extent to which the application must be explicitly restructured to benefit from the strategy (vertical). run in standard partitions and enabling malleability without interfer… view at source ↗
Figure 2
Figure 2. Figure 2: An illustrative example of the execution of two hybrid jobs, following the naïve co-scheduling policy. The upper/lower row represents the timeline of classical/quantum computations. Since there is only one available QPU in the system, the first submitted job holds it for its entire duration, preventing the second one to even begin its classical computations. This ultimately leads to a major under-utilizati… view at source ↗
Figure 3
Figure 3. Figure 3: A representation of the execution of the same two hybrid jobs in [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: A (1,5)-clustered graph used as input to the GC problem. The central node (label 4) is the vertex separator of 2 clusters with 5 nodes each. Circuit Cutting. Given the limited number of qubits avail￾able on the quantum device, the circuit cutting technique was employed to enable the execution of circuits involving more than 5 qubits [61]. Circuit cutting allows a quantum circuit to be decomposed into small… view at source ↗
Figure 5
Figure 5. Figure 5: The QAOA circuit used to solve a MIS instance for the graph in [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Data and processes of the target application. Yellow shapes are data between processes, green boxes are classical processes, and purple boxes are processes suitable to be performed by quantum resources. The application loops until the Silhouette score of a certain clustering reaches a user￾defined threshold or until the maximum number of loop iterations is reached. than quantum algorithmic performance, we … view at source ↗
Figure 7
Figure 7. Figure 7: The plots depicting Quantum Occupancy (a), Quantum Time (b) and Mean Queue Time (c) with respect to an increasing number of concurrent GC replicas (𝑛_𝑐𝑜𝑝𝑖𝑒𝑠). The labels 𝑅 ∈ {0, 2, 5} refer to runs with a different quantum￾classical workload ratio set, as explained at the beginning of Section 4.3.1. The 𝑦 = 𝑎 ⋅ 𝑥 dashed line in Sub-figure (b) is used to highlight the linearity relation of the data. node fr… view at source ↗
Figure 8
Figure 8. Figure 8: The plots depicting Total Time and Job Runtimes Distribution metrics. The vertical axis is set on a log scale for plots (d.1-3), while a regular linear scale is used for plots (e.1-3). Note that we disregard here the quantum resources, as their usage is constant across the three scenarios. The dataset used in the clustering application consists of 80,000 2D points generated via make_blobs from scikit￾learn… view at source ↗
Figure 9
Figure 9. Figure 9: Timeline of cumulative usage of classical nodes on our Slurm compute partition when launching two concurrent workloads (in blue and orange, respectively) with two minutes long quantum jobs. Each subplot refers to a different schedul￾ing approach, i.e. baseline, workflow, and malleability. ending workload, thus considering two complete end-to-end simulations. The baseline approach is, in this case, the wors… view at source ↗
read the original abstract

As quantum computing (QC) technologies mature, their integration into established high-performance computing (HPC) infrastructures is becoming a central objective for next-generation computing systems. However, unlocking the potential of hybrid platforms for computationally demanding workloads remains challenging. The mismatch between quantum and classical programming models, the limited maturity of quantum software stacks, and the scarcity of quantum processing units (QPUs) above all, necessitate scheduling strategies that go beyond standard HPC mechanisms to manage such heterogeneous and constrained resources. To address this issue, we investigate three distinct methodologies for HPC-QC resource scheduling: time-based multiplexing, dynamic resource management, and workflow decomposition. Experimental validation on production HPC clusters and real quantum hardware demonstrates the effectiveness of these approaches under different workload scenarios. Malleability and workflow strategies significantly optimize classical resource utilization, reducing consumption by up to 45.7% and 64% respectively, proving to be best fitted for hybrid jobs where quantum and classical workloads are evenly balanced. Conversely, time-multiplexing enhances QPU utilization and reduces execution time at the cluster level, making it the optimal strategy for the opposite context, which is characterized by high classical-quantum workload imbalances. These findings underscore the practical viability of tailored scheduling strategies for hybrid HPC-QC environments and highlight their complementarity in building efficient, scalable software stacks for next-generation quantum-accelerated facilities.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper investigates three scheduling strategies for hybrid Quantum-HPC applications—time-based multiplexing, dynamic resource management (malleability), and workflow decomposition—on production HPC clusters and real quantum hardware. It claims that malleability and workflow strategies optimize classical resource utilization with reductions up to 45.7% and 64% respectively and are best suited for evenly balanced quantum-classical workloads, while time-multiplexing improves QPU utilization and reduces cluster-level execution time for imbalanced workloads.

Significance. If the results hold under broader conditions, the work provides practical empirical guidance on tailoring scheduling to workload balance in emerging hybrid systems and demonstrates the complementarity of the three approaches. The use of real quantum hardware alongside production clusters is a clear strength, as is the focus on measurable resource metrics rather than purely theoretical models.

major comments (2)
  1. [Abstract] Abstract: The central claims of up to 45.7% and 64% reductions in classical resource consumption, plus the ranking of strategies as 'best fitted' for balanced vs. imbalanced workloads, are stated without any description of workload definitions, the quantum-classical balance ratios tested, measurement methods, error bars, baseline comparisons, or statistical significance. These omissions are load-bearing for the performance conclusions.
  2. [Experimental validation] Experimental validation: No quantitative details are supplied on the workload generator, number of runs, hardware parameters (qubit count, coherence times, queueing policies), or how the observed ordering of strategies was determined. This directly affects whether the reported savings and optimality rankings can be assessed for robustness.
minor comments (1)
  1. [Introduction] The introduction could more explicitly define 'malleability' and 'workflow decomposition' before the experimental claims are introduced.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. We have carefully reviewed the major comments and provide point-by-point responses below, indicating the revisions we will make to address the concerns raised.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claims of up to 45.7% and 64% reductions in classical resource consumption, plus the ranking of strategies as 'best fitted' for balanced vs. imbalanced workloads, are stated without any description of workload definitions, the quantum-classical balance ratios tested, measurement methods, error bars, baseline comparisons, or statistical significance. These omissions are load-bearing for the performance conclusions.

    Authors: We agree that the abstract would benefit from additional context to support the central claims. In the revised manuscript, we will expand the abstract to briefly define the workload types (balanced workloads with comparable quantum and classical resource demands versus imbalanced workloads with significant classical dominance), specify the tested quantum-classical balance ratios, describe the resource measurement approach using HPC accounting tools, and note that the reported reductions include comparisons to baseline scheduling with associated variability measures detailed in the experimental results. This revision will directly address the concerns while preserving the abstract's conciseness. revision: yes

  2. Referee: [Experimental validation] Experimental validation: No quantitative details are supplied on the workload generator, number of runs, hardware parameters (qubit count, coherence times, queueing policies), or how the observed ordering of strategies was determined. This directly affects whether the reported savings and optimality rankings can be assessed for robustness.

    Authors: We recognize the importance of providing comprehensive experimental details for reproducibility and assessment of robustness. While the manuscript outlines the overall experimental framework using production HPC clusters and real quantum hardware, we will revise the experimental validation section to include quantitative specifications of the workload generator, the number of runs conducted for each configuration, relevant hardware parameters including qubit counts and coherence times, the queueing policies employed, and the criteria and methods used to establish the ordering of strategies (such as direct metric comparisons). These additions will enable a more thorough evaluation of the results. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical scheduling comparisons with measured results, no derivations or fitted models

full rationale

The paper reports experimental validation of three scheduling strategies (time-multiplexing, dynamic resource management, workflow decomposition) on production HPC clusters and real quantum hardware. It presents measured outcomes such as up to 45.7% and 64% reductions in classical resource consumption under specific workload scenarios. No equations, derivations, parameter fittings, or self-citation chains are invoked to support the central claims; the results are direct observations from the described experiments. The reader's assessment of 0.0 circularity is consistent with the absence of any load-bearing step that reduces by construction to prior inputs. Generalization concerns (representativeness of workloads) are separate from circularity and do not involve self-referential definitions or renamings.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are introduced; the work relies on standard assumptions about workload representativeness and hardware accessibility that are not formalized.

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