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arxiv: 2607.00469 · v1 · pith:CJ4IVPEQnew · submitted 2026-07-01 · 🪐 quant-ph

Dependency-Aware Circuit Scheduling for Multi-Core Quantum Systems to Minimize Makespan

Pith reviewed 2026-07-02 12:41 UTC · model grok-4.3

classification 🪐 quant-ph
keywords multi-core quantum computingcircuit schedulingmakespan minimizationgreedy schedulingquantum gate dependenciescore utilizationNISQ architecturesparallel quantum execution
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The pith

A greedy scheduler that dispatches quantum gates the instant their dependencies and cores are free cuts total execution time by 40 percent on average.

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

Multi-core quantum systems distribute qubits across separate cores to overcome the size limits of single chips. The paper compares a baseline that runs all gates in one layer together and then waits for the next layer against a finer approach that releases each gate as soon as its input gates finish and the needed cores are free. On standard circuit benchmarks the finer approach shortens the full run time by 40 percent while keeping cores occupied longer. The result matters because it shows that execution speed in these architectures can be improved by better ordering of existing gates rather than by adding more qubits or faster gates. If the underlying timing model is realistic, circuit schedulers become a practical lever for making multi-core quantum computers run algorithms more quickly.

Core claim

The paper establishes that a greedy, dependency-aware scheduler, which releases each quantum gate the moment all its predecessor gates have completed and the required cores plus communication links are available, produces an average 40 percent reduction in makespan and higher core utilization than a layered baseline on real quantum circuit benchmarks.

What carries the argument

The greedy scheduler that tracks per-gate dependencies and per-core resource availability to start each gate at the earliest feasible moment.

If this is right

  • Multi-core systems can hide inter-core communication behind useful computation more effectively than layer-by-layer execution allows.
  • Existing quantum algorithms can complete in less wall-clock time on the same multi-core hardware without any change to the circuit itself.
  • Core utilization rises because idle time between layers shrinks when gates are released individually.
  • The performance gap between layered and greedy scheduling widens on circuits whose gate dependencies are not perfectly aligned with layer boundaries.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • In systems with many cores the same greedy logic may need to incorporate dynamic estimates of communication cost between distant cores.
  • Pairing this scheduler with qubit-mapping tools that minimize inter-core moves could produce additive speed-ups.
  • The approach may remain useful once error-corrected logical qubits are placed across multiple cores, provided the dependency graph is updated to reflect logical operations.

Load-bearing premise

The simulation of gate dependencies, communication latency, and core availability matches the timing behavior of actual multi-core quantum hardware.

What would settle it

Running the same benchmark circuits on physical multi-core quantum hardware and observing that the measured makespan reduction is substantially smaller than 40 percent.

Figures

Figures reproduced from arXiv: 2607.00469 by John Jose, Maurizio Palesi, Rajeswari Suance P S, Ruchika Gupta.

Figure 1
Figure 1. Figure 1: Multi-Core Quantum Computing Architecture [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Case study setup (a) 3 × 3 Mesh Topology. (b) Example quantum circuit with five two-qubit gates. (c) Table showing gate number, input qubits, their resident cores, and hop counts. (d) Dependency graph of the circuit gates. C. Circuit Scheduling on Multi-Core Quantum Architectures 1) Layered Scheduling: Layered scheduling groups gates operating on disjoint qubits into parallel layers [10]. A strict sequenti… view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of scheduling strategies: layered schedul [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Comparison of various circuit scheduling with synthetic random circuits [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Comparison of various circuit scheduling results with real benchmarks on a [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
read the original abstract

Multi-core quantum computing architectures have emerged as a promising solution to the qubit scalability limitations of monolithic NISQ devices. Quantum algorithms are expressed as quantum circuits composed of single- and two-qubit gates. However, circuit scheduling in multi-core quantum systems remains largely unexplored. Reducing overall execution time (makespan), increasing core utilization, and hiding communication latency behind computation depends on effective scheduling. In this paper, we first introduce a layered scheduling approach as a baseline where quantum gates within the same layer are executed in parallel, while layers themselves are executed sequentially. We then propose a greedy scheduling strategy which schedules each gate as soon as all its dependencies and required resources are available. This allows fine-grained parallelism across cores. Our evaluation shows that on real benchmarks, greedy scheduling achieves an average 40% reduction in makespan and improvement in core utilization. The results suggest that the use of intelligent circuit scheduling to exploit parallelism can greatly enhance the speed of circuit execution in multi-core quantum architectures.

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

1 major / 1 minor

Summary. The paper introduces a layered baseline scheduler for quantum circuits on multi-core architectures (gates in a layer run in parallel, layers sequentially) and proposes a greedy dependency-aware scheduler that dispatches each gate as soon as its dependencies and resources are free. On real benchmarks the greedy method is reported to deliver an average 40% makespan reduction together with higher core utilization, obtained from a simulation that models gate dependencies, inter-core communication latency, and resource contention.

Significance. If the simulation model is faithful to hardware, the result would show that fine-grained dependency tracking can substantially improve execution time and utilization in multi-core quantum systems, an increasingly relevant engineering problem as monolithic devices scale. The contribution is mainly algorithmic and empirical rather than theoretical.

major comments (1)
  1. [Evaluation] Evaluation section (and abstract claim): the headline 40% makespan reduction and utilization improvement rest entirely on an unvalidated simulation of gate dependencies, communication latency, and core availability. No calibration data, comparison to physical multi-core devices (modular ion-trap or multi-chip superconducting modules), or sensitivity analysis to latency assumptions is provided; this directly undermines the empirical central claim.
minor comments (1)
  1. [Abstract] Abstract and introduction: the phrases 'real benchmarks' and 'simulation model' are used without any enumeration of the benchmark suite, number of cores tested, or concrete latency and parallelism parameters, making it impossible to assess reproducibility from the given text.

Simulated Author's Rebuttal

1 responses · 1 unresolved

We thank the referee for the constructive feedback. We address the major comment on the evaluation below.

read point-by-point responses
  1. Referee: [Evaluation] Evaluation section (and abstract claim): the headline 40% makespan reduction and utilization improvement rest entirely on an unvalidated simulation of gate dependencies, communication latency, and core availability. No calibration data, comparison to physical multi-core devices (modular ion-trap or multi-chip superconducting modules), or sensitivity analysis to latency assumptions is provided; this directly undermines the empirical central claim.

    Authors: We agree that the reported 40% makespan reduction is obtained from simulation rather than physical hardware execution, and that no calibration data or direct comparison to existing modular devices is provided. The simulation incorporates standard models for gate dependencies, inter-core communication latency, and resource contention drawn from the literature on multi-core quantum architectures. In revision we will add a sensitivity analysis over the key latency parameter, include an explicit discussion of modeling assumptions and limitations in both the abstract and evaluation section, and qualify the 40% figure as relative to the layered baseline under the modeled conditions. These changes will clarify the scope of the empirical results. revision: partial

standing simulated objections not resolved
  • Direct empirical comparison against physical multi-core quantum hardware, as such systems remain emerging and not generally available for large-scale circuit benchmarking.

Circularity Check

0 steps flagged

No circularity: empirical scheduling evaluation independent of inputs

full rationale

The paper introduces a baseline layered scheduler and a greedy dependency-aware scheduler, then reports simulation results on benchmarks showing 40% average makespan reduction. No mathematical derivation chain, fitted parameters, or self-citations are used to generate the headline result; the comparison is direct and external to any self-defined quantities. The evaluation relies on a simulation model whose validity is an assumption, but this does not constitute circularity under the defined patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no visible free parameters, axioms, or invented entities; all such elements would need to be extracted from the full manuscript.

pith-pipeline@v0.9.1-grok · 5705 in / 956 out tokens · 18596 ms · 2026-07-02T12:41:46.961525+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

18 extracted references · 1 canonical work pages · 1 internal anchor

  1. [1]

    Scaling Super- conducting Quantum Computers with Chiplet Architectures,

    K. N. Smith, G. S. Ravi, J. M. Baker, and F. T. Chong, “Scaling Super- conducting Quantum Computers with Chiplet Architectures,” in55th IEEE/ACM International Symposium on Microarchitecture (MICRO), 2022, pp. 1092–1109

  2. [2]

    Quantum Computing in the NISQ era and beyond,

    J. Preskill, “Quantum Computing in the NISQ era and beyond,”Quan- tum, vol. 2, p. 79, 2018

  3. [3]

    Interconnect Fabrics for Multi-Core Quantum Processors: A Context Analysis,

    P. Escofet, S. B. Rached, S. Rodrigo, C. G. Almudever, E. Alarc ´on, and S. Abadal, “Interconnect Fabrics for Multi-Core Quantum Processors: A Context Analysis,” inProceedings of the 16th International Workshop on Network on Chip Architectures, 2023, pp. 34–39

  4. [4]

    From Designing Quantum Processors to Large-Scale Quantum Com- puting Systems,

    C. G. Almudever, R. Wille, F. Sebastiano, N. Haider, and E. Alarcon, “From Designing Quantum Processors to Large-Scale Quantum Com- puting Systems,” inDesign, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2024, pp. 1–10

  5. [5]

    Scalable multi-chip quantum architectures enabled by cryogenic hybrid wireless/quantum-coherent network-in- package,

    E. Alarc ´on, S. Abadal, F. Sebastiano, M. Babaie, E. Charbon, P. H. Bol´ıvar, M. Palesi, E. Blokhina, D. Leipold, B. Staszewski, A. Garcia- S´aez, and C. G. Almudever, “Scalable multi-chip quantum architectures enabled by cryogenic hybrid wireless/quantum-coherent network-in- package,” inIEEE International Symposium on Circuits and Systems (ISCAS), 2023, pp. 1–5

  6. [6]

    Decentralized Frame- work for Teleportation in Quantum Core Interconnects,

    P. R. Suance, R. Gupta, M. Palesi, and J. Jose, “Decentralized Frame- work for Teleportation in Quantum Core Interconnects,” inIEEE Com- puter Society Annual Symposium on VLSI (ISVLSI), vol. 1, 2025, pp. 1–6

  7. [7]

    A Modular Quantum Com- pilation Framework for Distributed Quantum Computing,

    D. Ferrari, S. Carretta, and M. Amoretti, “A Modular Quantum Com- pilation Framework for Distributed Quantum Computing,”IEEE Trans- actions on Quantum Engineering, vol. 4, pp. 1–13, 2023

  8. [8]

    Mapping Quantum Circuits to Modular Architectures with QUBO,

    M. Bandic, L. Prielinger, J. N ¨ußlein, A. Ovide, S. Rodrigo, S. Abadal, H. Van Someren, G. Vardoyan, E. Alarcon, C. G. Almudeveret al., “Mapping Quantum Circuits to Modular Architectures with QUBO,” in IEEE International Conference on Quantum Computing and Engineer- ing (QCE), vol. 1, 2023, pp. 790–801

  9. [9]

    TeleSABRE: Heuristic Layout Synthesis in Multi-Core Quantum Sys- tems with Teleport Interconnect,

    E. Russo, E. Vinciguerra, M. Palesi, D. Patti, G. Ascia, and V . Catania, “TeleSABRE: Heuristic Layout Synthesis in Multi-Core Quantum Sys- tems with Teleport Interconnect,” inIEEE International Conference on Quantum Computing and Engineering (QCE), vol. 1. IEEE, 2025, pp. 749–758

  10. [10]

    Entanglement Request Scheduling in Quantum Datacenter Networks,

    F. Vista, S. DiAdamo, E. Kaur, H. Shapourian, and R. Nejabati, “Entanglement Request Scheduling in Quantum Datacenter Networks,” IEEE Network, 2025

  11. [11]

    Execution Management of Distributed Quantum Computing Jobs,

    D. Ferrari, M. Bandini, and M. Amoretti, “Execution Management of Distributed Quantum Computing Jobs,” inIEEE International Confer- ence on Quantum Computing and Engineering (QCE), vol. 2, 2024, pp. 150–154

  12. [12]

    Modelling Short-range Quantum Teleportation for Scalable Multi-Core Quantum Computing Architectures,

    S. Rodrigo, S. Abadal, C. G. Almud ´ever, and E. Alarc ´on, “Modelling Short-range Quantum Teleportation for Scalable Multi-Core Quantum Computing Architectures,” inProceedings of the 8th Annual ACM International Conference on Nanoscale Computing and Communication (NANOCOM), 2021, pp. 1–7

  13. [13]

    Quantum computing with Qiskit

    A. Javadi-Abhari, M. Treinish, K. Krsulich, C. J. Wood, J. Lishman, J. Gacon, S. Martiel, P. D. Nation, L. S. Bishop, A. W. Crosset al., “Quantum computing with Qiskit,”arXiv preprint arXiv:2405.08810, 2024

  14. [14]

    Is Circuit Depth Accurate for Com- paring Quantum Circuit Runtimes?

    M. Tremba, P. Hovland, and J. Liu, “Is Circuit Depth Accurate for Com- paring Quantum Circuit Runtimes?” inIEEE International Conference on Quantum Computing and Engineering (QCE), vol. 1. IEEE, 2025, pp. 368–374

  15. [15]

    Netrace: Dependency-Driven Trace-Based Network-on-Chip Simulation,

    J. Hestness, B. Grot, and S. W. Keckler, “Netrace: Dependency-Driven Trace-Based Network-on-Chip Simulation,” inProceedings of the Third International Workshop on Network on Chip Architectures, 2010, pp. 31–36

  16. [16]

    MQT Bench: Benchmark- ing Software and Design Automation Tools for Quantum Computing,

    N. Quetschlich, L. Burgholzer, and R. Wille, “MQT Bench: Benchmark- ing Software and Design Automation Tools for Quantum Computing,” Quantum, vol. 7, p. 1062, 2023

  17. [17]

    Analysing the Effect of Quantum Network Interconnect on the Per- formance of Distributed Quantum Computing,

    S. Bahrani, R. Wang, R. Oliveira, R. Nejabati, and D. Simeonidou, “Analysing the Effect of Quantum Network Interconnect on the Per- formance of Distributed Quantum Computing,” inIEEE Optical Fiber Communications Conference and Exhibition (OFC), 2023, pp. 1–3

  18. [18]

    Efficient generation of multi-partite entanglement between non-local superconducting qubits using classical feedback,

    A. Hashim, M. Yuan, P. Gokhale, L. Chen, C. J ¨unger, N. Fruitwala, Y . Xu, G. Huang, K. Nowrouzi, L. Jianget al., “Efficient generation of multi-partite entanglement between non-local superconducting qubits using classical feedback,”APL Quantum, vol. 2, no. 4, 2025