pith. sign in

arxiv: 2605.17769 · v1 · pith:ALJYW2XTnew · submitted 2026-05-18 · 🪐 quant-ph

InterQ: Communication-Aware Scheduling Across Modular QPUs with Classical and Quantum Links

Pith reviewed 2026-05-20 11:27 UTC · model grok-4.3

classification 🪐 quant-ph
keywords modular quantum computingcommunication-aware schedulingcircuit cuttingQPU interconnectionquantum linksclassical linksarchitecture tradeoffs
0
0 comments X

The pith

InterQ scheduler accounts for classical and quantum links to optimize qubit placement and adaptive circuit cutting across modular QPUs.

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

This paper presents InterQ, a scheduler for modular quantum computers made of multiple QPUs linked by classical or quantum connections. It decides how to split and assign parts of a quantum circuit to different processors while accounting for how much communication is needed and its impact on speed and accuracy. The scheduler can adaptively cut the circuit to finish faster if needed. Simulations of superconducting, trapped-ion, and neutral-atom systems show clear differences: neutral atoms give the most accurate results, superconducting systems run fastest, and trapped ions fall in between. This is important because future quantum computers will likely be built from many smaller units that must coordinate efficiently.

Core claim

InterQ is a communication-aware scheduler that jointly considers qubit capacity, placement, parallel execution, and communication-driven dependencies across distributed subcircuits on modular QPUs. It supports adaptive circuit cutting to shorten the overall runtime while trading off fidelity against communication overhead. By distinguishing classical-link execution with its synchronization needs from quantum-link execution with entanglement costs, and applying a unified simulator to three hardware types, the work demonstrates that neutral-atom systems deliver the highest fidelity, superconducting systems the shortest runtimes, and trapped-ion systems an intermediate balance.

What carries the argument

InterQ, the communication-aware scheduler that models distinct costs for classical and quantum inter-QPU links and incorporates adaptive circuit cutting for distributed subcircuits.

If this is right

  • Explicit modeling of link types allows better scheduling decisions for distributed quantum circuits.
  • Adaptive cutting decisions can trade some fidelity for reduced overall execution time.
  • The choice of hardware platform affects the achievable balance between fidelity and makespan under the same scheduler.
  • Simulation frameworks can highlight tradeoffs between different modular quantum architectures.

Where Pith is reading between the lines

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

  • Future quantum cloud providers might use similar schedulers to allocate jobs across heterogeneous modular systems.
  • The findings suggest that hardware developers should prioritize either low-latency classical links or high-fidelity quantum links depending on target workloads.
  • Testing the scheduler with real inter-QPU entanglement generation could reveal additional overheads not captured in simulation.

Load-bearing premise

The unified simulation framework correctly captures the distinct synchronization and error costs of classical links versus quantum links for each hardware platform.

What would settle it

Executing the scheduled circuits on physical modular quantum hardware and verifying whether the observed fidelity and makespan match the simulation predictions for the three architectures.

Figures

Figures reproduced from arXiv: 2605.17769 by Aaron Orenstein, Daniel Blankenberg, Jaehyun Lee, Lauren Li, Shuai Xu, Vinooth Kulkarni, Vipin Chaudhary, Xinpeng Li.

Figure 1
Figure 1. Figure 1: Execution semantics of circuit cutting. In LO, cut fragments execute independently and are stitched through offline [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: QComm abstraction used by InterQ. Remote execution [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Execution schedule comparison of IBM LOCC, Atom [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: InterQ schedules for the MQT and QUEKO bench [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: QUEKO remote operations versus extra qubits. [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: Workload distribution for the MQT and Queko bench [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: QComm partitioning and remote-operation summaries. The two plots show how required partition count and remote [PITH_FULL_IMAGE:figures/full_fig_p010_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: MQT small remote operations vs. extra qubits. [PITH_FULL_IMAGE:figures/full_fig_p010_10.png] view at source ↗
read the original abstract

As quantum computing scales toward practical workloads, future systems are expected to move beyond single monolithic processors toward modular architectures that connect multiple QPUs. Different platforms realize this modularity through different communication models: superconducting systems rely on real-time classical links and dynamic-circuit coordination, trapped-ion systems use photonic interconnects for remote entanglement, and neutral-atom systems provide strong intra-core connectivity with proposed optical links for inter-core communication. This heterogeneity makes communication-aware scheduling essential for shared modular quantum cloud environments. We present InterQ, a communication-aware scheduler for modular QPU architectures with heterogeneous communication models. InterQ jointly considers qubit capacity, placement, parallel execution, and communication-driven dependencies across distributed subcircuits, while enabling adaptive circuit cutting to reduce makespan while balancing fidelity and communication overhead. The framework distinguishes classical-link execution, where measurement and feedforward impose synchronization constraints, from quantum-link execution, where entanglement distribution and state transfer determine coordination cost. Using a unified simulation framework to compare superconducting, trapped-ion, and neutral-atom modular systems, InterQ shows how communication models and scheduler-driven cutting decisions affect throughput, latency, and fidelity. Across evaluated workloads, InterQ exposes an architecture-dependent tradeoff: neutral-atom modular QPUs achieve the highest fidelity, superconducting systems minimize runtime, and trapped-ion systems provide a balanced intermediate profile across fidelity and makespan.

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 / 2 minor

Summary. The paper introduces InterQ, a communication-aware scheduler for modular QPUs supporting heterogeneous links: classical dynamic-circuit coordination for superconducting systems, photonic interconnects for trapped-ion systems, and optical links for neutral-atom systems. It jointly optimizes qubit placement, parallel execution, and communication dependencies while using adaptive circuit cutting to trade off makespan against fidelity and overhead. A unified simulator is used to compare the three platforms across workloads, revealing architecture-dependent tradeoffs (neutral-atom highest fidelity, superconducting lowest runtime, trapped-ion intermediate).

Significance. If the simulation models prove accurate, the work would provide a practical framework for scheduling in future modular quantum clouds and highlight how communication primitives shape performance tradeoffs across hardware platforms.

major comments (2)
  1. [§5] §5 (Simulation Framework and Evaluation): The platform-specific latency, synchronization, and fidelity parameters for classical versus quantum links are not validated or cross-checked against published experimental data on photonic interconnects, classical feedforward latency, or inter-core optical links. This is load-bearing for the central claim of architecture-dependent tradeoffs, because the reported differences (e.g., neutral-atom fidelity advantage) could arise from the chosen constants rather than inherent platform properties.
  2. [§6] §6 (Results): Workload definitions, exact makespan and fidelity numbers, error bars, and sensitivity analysis to the assumed error rates and timing parameters are not reported in sufficient detail to allow reproduction or assessment of robustness.
minor comments (2)
  1. [Abstract] The abstract would benefit from one or two concrete quantitative results (e.g., percentage makespan reduction or fidelity values) to substantiate the tradeoff claims.
  2. [§3] Notation for classical-link synchronization constraints versus quantum-link entanglement distribution costs should be introduced earlier and used consistently in the scheduler description.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. The comments highlight important aspects of parameter sourcing and reproducibility that will improve the manuscript. We address each major comment below and commit to revisions that directly respond to the concerns raised.

read point-by-point responses
  1. Referee: [§5] §5 (Simulation Framework and Evaluation): The platform-specific latency, synchronization, and fidelity parameters for classical versus quantum links are not validated or cross-checked against published experimental data on photonic interconnects, classical feedforward latency, or inter-core optical links. This is load-bearing for the central claim of architecture-dependent tradeoffs, because the reported differences (e.g., neutral-atom fidelity advantage) could arise from the chosen constants rather than inherent platform properties.

    Authors: We agree that the simulation parameters require clearer grounding in experimental literature to support the architecture-dependent tradeoff claims. While the values were drawn from published works on each platform, the original manuscript did not include explicit citations or cross-checks for each latency, synchronization, and fidelity constant. In the revised version we will add a dedicated table in §5 that lists every parameter together with its source reference (e.g., specific photonic-interconnect experiments for trapped-ion systems and feedforward-latency measurements for superconducting dynamic circuits). We will also include a short robustness discussion showing how the reported fidelity and makespan differences persist when parameters are varied within the ranges given in those experimental papers. revision: yes

  2. Referee: [§6] §6 (Results): Workload definitions, exact makespan and fidelity numbers, error bars, and sensitivity analysis to the assumed error rates and timing parameters are not reported in sufficient detail to allow reproduction or assessment of robustness.

    Authors: We accept that the current results presentation lacks the granularity needed for independent reproduction and robustness evaluation. The revised manuscript will expand §6 (and add an appendix if space is limited) to provide: (i) precise workload definitions including qubit counts, gate statistics, and inter-subcircuit communication patterns; (ii) tabulated numerical values for makespan and fidelity on each platform; (iii) error bars or standard deviations obtained from repeated simulation runs; and (iv) a sensitivity analysis that sweeps key error rates and timing parameters across literature-reported ranges. These additions will enable readers to verify the findings and assess how sensitive the architecture tradeoffs are to the underlying assumptions. revision: yes

Circularity Check

0 steps flagged

No circularity: InterQ introduces new scheduler and simulation framework without self-referential derivations

full rationale

The paper presents InterQ as a newly constructed communication-aware scheduler for modular QPUs, jointly considering qubit capacity, placement, parallel execution, and adaptive circuit cutting. It distinguishes classical-link synchronization from quantum-link entanglement costs within a unified simulation framework to compare superconducting, trapped-ion, and neutral-atom architectures. No equations, fitted parameters, or load-bearing self-citations appear in the abstract or description that would reduce any result to its own inputs by construction. The architecture-dependent tradeoffs (neutral-atom highest fidelity, superconducting lowest runtime) are outputs of the simulation rather than mathematical identities or renamed prior fits. This is a standard case of an independent modeling contribution.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available. No explicit free parameters, axioms, or invented entities are stated. The simulation framework itself is treated as a black-box assumption whose internal parameters are unknown.

pith-pipeline@v0.9.0 · 5794 in / 1193 out tokens · 39007 ms · 2026-05-20T11:27:48.815289+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

37 extracted references · 37 canonical work pages · 1 internal anchor

  1. [1]

    Quantum computing in the nisq era and beyond,

    J. Preskill, “Quantum computing in the nisq era and beyond,”Quantum, vol. 2, p. 79, 2018

  2. [2]

    Quantum error correction: an introductory guide,

    J. Roffe, “Quantum error correction: an introductory guide,”Contempo- rary Physics, vol. 60, no. 3, pp. 226–245, 2019

  3. [3]

    Review of distributed quantum computing: From single qpu to high performance quantum computing,

    D. Barral, F. J. Cardama, G. D ´ıaz-Camacho, D. Fa ´ılde, I. F. Llovo, M. Mussa-Juane, J. V ´azquez-P´erez, J. Villasuso, C. Pi ˜neiro, N. Costas, J. C. Pichel, T. F. Pena, and A. G ´omez, “Review of distributed quantum computing: From single qpu to high performance quantum computing,” Computer Science Review, vol. 57, p. 100747, Aug. 2025. [Online]. Avai...

  4. [4]

    Distributed quantum computing across an optical network link,

    D. Main, P. Drmota, D. P. Nadlinger, E. M. Ainley, A. Agrawal, B. C. Nichol, R. Srinivas, G. Araneda, and D. M. Lucas, “Distributed quantum computing across an optical network link,” Nature, vol. 638, no. 8050, p. 383–388, Feb. 2025. [Online]. Available: http://dx.doi.org/10.1038/s41586-024-08404-x

  5. [5]

    Combining quantum processors with real-time classical communication,

    A. Carrera Vazquez, C. Tornow, D. Rist `e, S. Woerner, M. Takita, and D. J. Egger, “Combining quantum processors with real-time classical communication,”Nature, vol. 636, no. 8041, p. 75–79, Nov. 2024. [Online]. Available: http://dx.doi.org/10.1038/s41586-024-08178-2

  6. [6]

    Cutqc: using small quantum computers for large quantum circuit evaluations,

    W. Tang, T. Tomesh, M. Suchara, J. Larson, and M. Martonosi, “Cutqc: using small quantum computers for large quantum circuit evaluations,” in Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2021, pp. 473–486

  7. [7]

    Cutting circuits with multiple two-qubit unitaries,

    L. Schmitt, C. Piveteau, and D. Sutter, “Cutting circuits with multiple two-qubit unitaries,”Quantum, vol. 9, p. 1634, Feb. 2025. [Online]. Available: https://doi.org/10.22331/q-2025-02-18-1634

  8. [8]

    Qiskit addon: circuit cutting,

    A. M. Bra ´nczyk, A. Carrera Vazquez, D. J. Egger, B. Fuller, J. Gacon, J. R. Garrison, J. R. Glick, C. Johnson, S. Joshi, E. Pednault, C. D. Pemmaraju, P. Rivero, I. Shehzad, and S. Woerner, “Qiskit addon: circuit cutting,” https://github.com/Qiskit/qiskit-addon-cutting, 2024

  9. [9]

    Circuit knitting with classical communication,

    C. Piveteau and D. Sutter, “Circuit knitting with classical communication,”IEEE Transactions on Information Theory, vol. 70, no. 4, p. 2734–2745, Apr. 2024. [Online]. Available: http://dx.doi.org/10.1109/TIT.2023.3310797

  10. [10]

    Optimal wire cutting with classical communication,

    L. Brenner, C. Piveteau, and D. Sutter, “Optimal wire cutting with classical communication,” 2023. [Online]. Available: https: //arxiv.org/abs/2302.03366

  11. [11]

    Experimental demonstration of a high-fidelity virtual two-qubit gate,

    A. P. Singh, K. Mitarai, Y . Suzuki, K. Heya, Y . Tabuchi, K. Fujii, and Y . Nakamura, “Experimental demonstration of a high-fidelity virtual two-qubit gate,”Phys. Rev. Res., vol. 6, p. 013235, Mar 2024. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevResearch.6.013235

  12. [12]

    Entanglement of trapped-ion qubits separated by 230 meters,

    V . Krutyanskiy, M. Galli, V . Krcmarsky, S. Baier, D. A. Fioretto, Y . Pu, A. Mazloom, P. Sekatski, M. Canteri, M. Teller, J. Schupp, J. Bate, M. Meraner, N. Sangouard, B. P. Lanyon, and T. E. Northup, “Entanglement of trapped-ion qubits separated by 230 meters,”Phys. Rev. Lett., vol. 130, p. 050803, Feb 2023. [Online]. Available: https://link.aps.org/do...

  13. [13]

    High-rate and high-fidelity modular interconnects between neutral atom quantum processors,

    Y . Li and J. D. Thompson, “High-rate and high-fidelity modular interconnects between neutral atom quantum processors,” 2024. [Online]. Available: https://arxiv.org/abs/2401.04075

  14. [14]

    Quflex: Parallel quantum job scheduling using adaptive circuit cutting,

    V . Kulkarni, A. Orenstein, X. Li, S. Xu, D. Blankenberg, and V . Chaud- hary, “Quflex: Parallel quantum job scheduling using adaptive circuit cutting,” inProceedings of Supercomputing India (SCI), 2025, to appear

  15. [15]

    Scalable circuit cutting and scheduling in a resource- constrained and distributed quantum system,

    S. Kan, Z. Du, M. Palma, S. A. Stein, C. Liu, W. Wei, J. Chen, A. Li, and Y . Mao, “Scalable circuit cutting and scheduling in a resource- constrained and distributed quantum system,” in2024 IEEE Interna- tional Conference on Quantum Computing and Engineering (QCE), vol. 01, 2024, pp. 1077–1088

  16. [16]

    Qiskit: An open-source framework for quantum computing,

    H. Abraham, I. AduOffei, I. Y . Akhalwaya, G. Aleksandrowicz, T. Alexanderet al., “Qiskit: An open-source framework for quantum computing,” Zenodo, 2019. [Online]. Available: https: //doi.org/10.5281/zenodo.2562111

  17. [17]

    Qucloud+: A holistic qubit mapping scheme for single/multi-programming on 2d/3d nisq quantum computers,

    L. Liu and X. Dou, “Qucloud+: A holistic qubit mapping scheme for single/multi-programming on 2d/3d nisq quantum computers,”ACM Trans. Archit. Code Optim., vol. 21, no. 1, Jan. 2024. [Online]. Available: https://doi.org/10.1145/3631525

  18. [18]

    Enabling multi-programming mechanism for quantum computing in the nisq era,

    S. Niu and A. Todri-Sanial, “Enabling multi-programming mechanism for quantum computing in the nisq era,”Quantum, vol. 7, p. 925, 2023

  19. [19]

    Qgroup: Parallel quantum job schedul- ing using dynamic programming,

    A. Orenstein and V . Chaudhary, “Qgroup: Parallel quantum job schedul- ing using dynamic programming,” in2024 IEEE International Confer- ence on Quantum Computing and Engineering (QCE), vol. 01, 2024, pp. 990–999

  20. [20]

    Two-step approach to scheduling quantum circuits,

    G. G. Guerreschi and J. Park, “Two-step approach to scheduling quantum circuits,”Quantum Science and Technology, vol. 3, no. 4, p. 045003, 2018

  21. [21]

    Adaptive job schedul- ing in quantum clouds using reinforcement learning,

    W. Luo, J. Zhao, T. Zhan, and Q. Guan, “Adaptive job schedul- ing in quantum clouds using reinforcement learning,”arXiv preprint arXiv:2506.10889, 2025

  22. [22]

    Quantum circuit cutting with maximum-likelihood tomography,

    M. A. Perlin, Z. H. Saleem, M. Suchara, and J. C. Osborn, “Quantum circuit cutting with maximum-likelihood tomography,”npj Quantum Information, vol. 7, no. 1, p. 64, 2021. [Online]. Available: https://doi.org/10.1038/s41534-021-00390-6

  23. [23]

    Simulating large quantum circuits on a small quantum computer,

    T. Peng, A. W. Harrow, M. Ozols, and X. Wu, “Simulating large quantum circuits on a small quantum computer,”Physical review letters, vol. 125, no. 15, p. 150504, 2020

  24. [24]

    Fast quantum circuit cutting with randomized measurements,

    A. Lowe, M. Medvidovi ´c, A. Hayes, L. J. O’Riordan, T. R. Bromley, J. M. Arrazola, and N. Killoran, “Fast quantum circuit cutting with randomized measurements,”Quantum, vol. 7, p. 934, 2023

  25. [25]

    Cutting multi-control quantum gates with zx calculus,

    C. Ufrecht, M. Periyasamy, S. Rietsch, D. D. Scherer, A. Plinge, and C. Mutschler, “Cutting multi-control quantum gates with zx calculus,” Quantum, vol. 7, p. 1147, 2023

  26. [26]

    Ap- proximate quantum circuit reconstruction,

    D. Chen, B. Baheri, V . Chaudhary, Q. Guan, N. Xie, and S. Xu, “Ap- proximate quantum circuit reconstruction,” in2022 IEEE International Conference on Quantum Computing and Engineering (QCE). IEEE, 2022, pp. 509–515

  27. [27]

    Efficient quantum circuit cutting by neglecting basis elements,

    D. T. Chen, E. H. Hansen, X. Li, V . Kulkarni, V . Chaudhary, B. Ren, Q. Guan, S. Kuppannagari, J. Liu, and S. Xu, “Efficient quantum circuit cutting by neglecting basis elements,”arXiv preprint arXiv:2304.04093, 2023

  28. [28]

    Efficient circuit wire cutting based on commuting groups,

    X. Li, V . Kulkarni, D. T. Chen, Q. Guan, W. Jiang, N. Xie, S. Xu, and V . Chaudhary, “Efficient circuit wire cutting based on commuting groups,”arXiv preprint arXiv:2410.20313, 2024

  29. [29]

    QuMod: Parallel Quantum Job Scheduling on Modular QPUs using Circuit Cutting

    V . Kulkarni, A. Orenstein, X. Li, S. Xu, D. Blankenberg, and V . Chaudhary, “Qumod: Parallel quantum job scheduling on modular qpus using circuit cutting,” 2026, published in QCNC 2026. [Online]. Available: https://arxiv.org/abs/2604.11013

  30. [30]

    MQT Bench: Benchmarking software and design automation tools for quantum computing,

    N. Quetschlich, L. Burgholzer, and R. Wille, “MQT Bench: Benchmarking software and design automation tools for quantum computing,”Quantum, vol. 7, p. 1067, 2023. [Online]. Available: https://doi.org/10.22331/q-2023-09-28-1067

  31. [31]

    Optimality study of existing quantum computing layout synthesis tools,

    B. Tan and J. Cong, “Optimality study of existing quantum computing layout synthesis tools,”IEEE Transactions on Computers, vol. 70, no. 9, pp. 1363–1373, 2021, introduces QUEKO benchmarks with known optimal depths and gate counts. [Online]. Available: https://doi.org/10.1109/TC.2020.3009140

  32. [32]

    Revlib: An online resource for reversible functions and reversible circuits,

    R. Wille, S. Offermann, and R. Drechsler, “Revlib: An online resource for reversible functions and reversible circuits,” in2008 38th International Symposium on Multiple Valued Logic. IEEE, 2008, pp. 220–225. [Online]. Available: https://doi.org/10.1109/ISMVL.2008.13

  33. [33]

    Constructing a virtual two-qubit gate by sampling single-qubit operations,

    K. Mitarai and K. Fujii, “Constructing a virtual two-qubit gate by sampling single-qubit operations,”New J. Phys., vol. 23, p. 023021, 2021

  34. [34]

    Experimental simulation of larger quantum circuits with fewer superconducting qubits,

    C. Ying, B. Cheng, Y . Zhao, H.-L. Huang, Y .-N. Zhang, M. Gong, Y . Wu, S. Wang, F. Liang, J. Lin, Y . Xu, H. Deng, H. Rong, C.-Z. Peng, M.-H. Yung, X. Zhu, and J.-W. Pan, “Experimental simulation of larger quantum circuits with fewer superconducting qubits,”Phys. Rev. Lett., vol. 130, p. 110601, Mar 2023. [Online]. Available: https://link.aps.org/doi/10...

  35. [35]

    Qoncord: A multi-device job scheduling framework for variational quantum algorithms,

    M. Wang, P. Das, and P. J. Nair, “Qoncord: A multi-device job scheduling framework for variational quantum algorithms,” in2024 57th IEEE/ACM International Symposium on Microarchitecture (MICRO), 2024, pp. 735–749

  36. [36]

    Qusplit: Achieving both high fidelity and throughput via job splitting on noisy quantum computers,

    J. Li, Y . Song, Y . Liu, J. Pan, L. Yang, T. Humble, and W. Jiang, “Qusplit: Achieving both high fidelity and throughput via job splitting on noisy quantum computers,”arXiv preprint arXiv:2501.12492, 2025

  37. [37]

    PeerJ Computer Science 3, e103 (Jan 2017).https://doi.org/10.7717/peerj-cs.103

    T. SimPy, “Simpy: A discrete-event simulation library,”PeerJ Computer Science, vol. 2, p. e103, 2016. [Online]. Available: https://doi.org/10.7717/peerj-cs.103