pith. machine review for the scientific record. sign in

arxiv: 2604.19814 · v1 · submitted 2026-04-17 · 🪐 quant-ph · cs.ET

Recognition: unknown

Quantum Integrated High-Performance Computing: Foundations, Architectural Elements and Future Directions

Authors on Pith no claims yet

Pith reviewed 2026-05-10 08:46 UTC · model grok-4.3

classification 🪐 quant-ph cs.ET
keywords quantumcomputingarchitecturalheterogeneoushigh-performanceresourcesabstractionclassical
0
0 comments X

The pith

The authors describe a visionary layered architecture for unifying classical and quantum compute resources under a single job submission and scheduling interface.

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

High-performance computers have grown by adding new kinds of processors over time. Now quantum chips are emerging that can solve certain hard problems faster than regular computers. This paper sketches how to plug those quantum chips into existing supercomputer setups so that a single program can send parts of its work to whichever processor type is best suited. The design includes a common way for users to submit jobs, schedulers that know about quantum hardware, and layers that handle moving data between classical and quantum parts. It draws lessons from how GPUs were added to supercomputers years ago and points to applications like simulating molecules or solving optimization puzzles.

Core claim

We propose a layered system design comprising unified resource management, quantum-aware scheduling, hybrid workflow orchestration, middleware and programming abstraction, interconnect technologies, and a tiered execution model enabling seamless workload partitioning across classical and quantum backends.

Load-bearing premise

That practical, reliable QPUs will soon exist at scales that can be treated as interchangeable accelerators within existing HPC resource managers and interconnect fabrics.

Figures

Figures reproduced from arXiv: 2604.19814 by Kyle Chard, Rajkumar Buyya, Siva Sai, Suman Raj, Yogesh Simmhan.

Figure 1
Figure 1. Figure 1: Timeline of the emergence of hybrid HPC and Quantum era [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Representative anatomy of a Quantum Computer showcasing a quantum computer, a quantum processing [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Abstraction vs Expertise for QHPC Systems [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Quantum-Integrated HPC Architecture Layers [PITH_FULL_IMAGE:figures/full_fig_p012_4.png] view at source ↗
read the original abstract

High-performance computing (HPC) has evolved over decades through multiple architectural transitions, from vector supercomputers to massively parallel CPU clusters and GPU-accelerated systems, continuously expanding the frontier of scientific discovery. With the emergence of quantum processing units (QPUs) as practical computational accelerators, a new opportunity arises to further extend this trajectory by integrating quantum and classical computing paradigms. This paper presents Quantum Integrated High-Performance Computing (QHPC), a visionary architectural framework that unifies CPUs, GPUs, FPGAs, and QPUs as first-class heterogeneous resources. We propose a layered system design comprising unified resource management, quantum-aware scheduling, hybrid workflow orchestration, middleware and programming abstraction, interconnect technologies, and a tiered execution model enabling seamless workload partitioning across classical and quantum backends. A central aspect of our vision is a strong user requests abstraction layer that exposes heterogeneous resources through a unified job submission interface, similar in spirit to existing schedulers such as Slurm, allowing users to describe workloads in a consistent template independent of underlying compute type or location. Drawing insights from prior accelerator integration eras, we outline how QHPC can support emerging workloads in quantum chemistry, materials discovery, combinatorial optimization, and climate modeling. We conclude by highlighting open challenges in building scalable, reliable, and programmable quantum-classical infrastructures that seamlessly connect global users to heterogeneous compute resources for future quantum-classical HPC ecosystems.

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.

Circularity Check

0 steps flagged

No circularity: forward-looking architectural proposal with no derivations or self-referential reductions

full rationale

The paper proposes a layered QHPC framework (unified resource management, quantum-aware scheduling, tiered execution model, unified job submission interface) as a visionary design drawing from prior accelerator eras. No equations, fitted parameters, or derivation chains exist. Central claims are design elements presented as forward-looking rather than results derived from the paper's own inputs or self-citations. No load-bearing steps reduce by construction to definitions, fits, or author-overlapping citations. This matches the default expectation of non-circularity for conceptual HPC architecture papers.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that quantum hardware will mature into reliable, schedulable accelerators and that existing HPC abstractions can be extended without fundamental redesign. No free parameters, invented physical entities, or ad-hoc mathematical axioms are introduced.

axioms (1)
  • domain assumption Quantum processing units can be integrated as first-class heterogeneous resources in HPC systems with manageable overhead.
    Invoked throughout the layered design description as the premise enabling unified scheduling and workflow orchestration.

pith-pipeline@v0.9.0 · 5556 in / 1236 out tokens · 32622 ms · 2026-05-10T08:46:39.892806+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

111 extracted references · 16 canonical work pages · 4 internal anchors

  1. [1]

    C. Chen, D. T. Nguyen, S. J. Lee, N. A. Baker, A. S. Karakoti, L. Lauw, C. Owen, K. T. Mueller, B. A. Bilodeau, V. Murugesan, et al., Accelerating computational materials discovery with machine learning and cloud high-performance computing: from large-scale screening to experimental validation, Journal of the American Chemical Society (2024)

  2. [3]

    Habib, A

    S. Habib, A. Pope, H. Finkel, et al., Hacc: Simulating sky surveys on state-of-the-art supercomputing architectures, New Astronomy (2016)

  3. [4]

    Shalf, The future of computing beyond moore’s law, Philosophical Transactions of the Royal Society A (2020)

    J. Shalf, The future of computing beyond moore’s law, Philosophical Transactions of the Royal Society A (2020)

  4. [5]

    Atchley, C

    S. Atchley, C. Zimmer, J. Lange, et al., Frontier: Exploring exascale, in: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC, 2023

  5. [6]

    Stevens, J

    R. Stevens, J. Ramprakash, P. Messina, M. Papka, K. Riley, Aurora: Argonne’s next- generation exascale supercomputer, Tech. rep., ANL (Argonne National Laboratory (ANL), Argonne, IL (United States)) (2019)

  6. [7]

    Y. Cao, J. Romero, J. P. Olson, et al., Quantum chemistry in the age of quantum computing, Chemical reviews (2019). 24

  7. [8]

    K. A. Britt, T. S. Humble, High-performance computing with quantum processing units, J. Emerg. Technol. Comput. Syst. (2017)

  8. [9]

    P. W. Shor, Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer, SIAM review (1999)

  9. [10]

    L. K. Grover, A fast quantum mechanical algorithm for database search, in: ACM sympo- sium on Theory of computing, 1996

  10. [11]

    M. Wang, F. Hua, C. Liu, et al., Enabling scalable vqe simulation on leading hpc systems, in: SC’23 Workshops of The International Conf. on High Performance Computing, Network, Storage, and Analysis, 2023

  11. [12]

    B. Bach, J. Falla, I. Safro, Mlqaoa: Graph learning accelerated hybrid quantum-classical multilevel qaoa, in: IEEE International Conference on Quantum Computing and Engineer- ing (QCE), 2024

  12. [13]

    Oak Ridge National Laboratory, ORNL, NVIDIA, HPE Advance Quantum Computing, AI and HPC for Science (November 2025)

  13. [14]

    IBM, IBM Releases a New Blueprint for Quantum-Centric Supercomputing (March 2026)

  14. [15]

    Leadership in the Next Computing Era (March 2026)

    Center for Strategic and International Studies, Pioneering Quantum-Supercomputing Integration: U.S. Leadership in the Next Computing Era (March 2026)

  15. [16]

    Pasqal, Inside Pasqal’s 2026 Vision on Quantum for Industry and Research (January 2026)

  16. [17]

    H. H. Goldstine, A. Goldstine, The electronic numerical integrator and computer (eniac), in: The origins of digital computers: selected papers, Springer, 1946

  17. [18]

    Swensen, Control data 6600, in: Encyclopedia of Parallel Computing, Springer US, 2011

    J. Swensen, Control data 6600, in: Encyclopedia of Parallel Computing, Springer US, 2011

  18. [19]

    Bouknight, S

    W. Bouknight, S. Denenberg, D. McIntyre, J. Randall, A. Sameh, D. Slotnick, The illiac iv system, Proceedings of the IEEE (1972)

  19. [20]

    R. M. Russell, The cray-1 computer system, Communications of the ACM (1978)

  20. [21]

    August, G

    M. August, G. Brost, C. Hsiung, A. Schiffleger, Cray x-mp: the birth of a supercomputer, Computer (1989)

  21. [22]

    Snir, MPI–the Complete Reference: the MPI core, Vol

    M. Snir, MPI–the Complete Reference: the MPI core, Vol. 1, MIT press, 1998

  22. [23]

    D. W. Walker, J. J. Dongarra, Mpi: a standard message passing interface, Supercomputer (1996)

  23. [24]

    T. L. Sterling, Beowulf cluster computing with Linux, MIT press, 2002

  24. [25]

    Foster, C

    I. Foster, C. Kesselman, The Grid 2: Blueprint for a new computing infrastructure, Elsevier, 2003

  25. [26]

    Nickolls, I

    J. Nickolls, I. Buck, M. Garland, K. Skadron, Scalable parallel programming with cuda, ACM Queue (2008)

  26. [27]

    Munshi, The opencl specification, in: IEEE Hot Chips 21 Symposium (HCS), 2009

    A. Munshi, The opencl specification, in: IEEE Hot Chips 21 Symposium (HCS), 2009

  27. [28]

    Lawrence Livermore National Laboratory, El Capitan: NNSA’s First Exascale Machine (2024)

  28. [29]

    Merritt, What Is a QPU? (Jul

    R. Merritt, What Is a QPU? (Jul. 2022). 25

  29. [30]

    M. A. Nielsen, I. L. Chuang, Quantum computation and quantum information, Cambridge university press, 2010

  30. [31]

    C. D. Hill, E. Peretz, S. J. Hile, et al., A surface code quantum computer in silicon, Science Advances (2015)

  31. [32]

    N. C. Jones, R. Van Meter, A. G. Fowler, et al., Layered architecture for quantum computing, Physical Review X (2012)

  32. [33]

    Beverland, V

    M. Beverland, V. Kliuchnikov, E. Schoute, Surface code compilation via edge-disjoint paths, PRX Quantum (2022)

  33. [34]

    L. Li, L. D. Santis, I. B. Harris, et al., Heterogeneous integration of spin–photon interfaces with a cmos platform, Nature (2024)

  34. [35]

    Riera-Sàbat, W

    F. Riera-Sàbat, W. Dür, A modular entanglement-based quantum computer architecture, New Journal of Physics (2024)

  35. [36]

    V. V. Albert, Bosonic coding: introduction and use cases, arXiv:2211.05714 (2022)

  36. [37]

    Hastrup, U

    J. Hastrup, U. L. Andersen, All-optical cat-code quantum error correction, Physical Review Research, APS (2022)

  37. [38]

    T. J. Yoder, E. Schoute, P. Rall, et al., Tour de gross: A modular quantum computer based on bivariate bicycle codes, arXiv:2506.03094 (2025)

  38. [39]

    Mittal, J

    S. Mittal, J. S. Vetter, A survey of cpu-gpu heterogeneous computing techniques, ACM Comput. Surv. (2015)

  39. [40]

    Wahlgren, G

    J. Wahlgren, G. Schieffer, R. Shi, et al., Dissecting cpu-gpu unified physical memory on amd mi300a apus, in: Inter. Symp. on Workload Characterization (IISWC), IEEE, 2025

  40. [41]

    Understanding data movement in tightly coupled heterogeneous systems: A case study with the grace hopper superchip.arXiv preprint arXiv:2408.11556, 2024

    L. Fusco, M. Khalilov, M. Chrapek, et al., Understanding data movement in tightly coupled heterogeneous systems: A case study with the grace hopper superchip, arXiv 2408.11556 (2024)

  41. [42]

    Saroliya, E

    U. Saroliya, E. Arima, D. Liu, M. Schulz, Hierarchical resource partitioning on modern gpus: A reinforcement learning approach, in: International Conference on Cluster Computing (CLUSTER), 2023

  42. [43]

    NVIDIA Corporation, NVIDIA Ampere Architecture Whitepaper (A100 Tensor Core GPU) (2020)

  43. [44]

    Augonnet, S

    C. Augonnet, S. Thibault, R. Namyst, P.-A. Wacrenier, Starpu: A unified platform for task scheduling on heterogeneous multicore architectures, in: International Euro-Par Conference on Parallel Processing, 2009

  44. [45]

    S.Kato, K.Lakshmanan, R.Rajkumar, Y.Ishikawa, etal., {TimeGraph}:{GPU}scheduling for {Real-Time}{Multi-Tasking} environments, in: USENIX Annual Technical Conference (ATC), 2011

  45. [46]

    J. D. Owens, D. Luebke, N. Govindaraju, et al., A survey of general-purpose computation on graphics hardware, in: Computer graphics forum, Wiley Online Library, 2007

  46. [47]

    Paszke, S

    A. Paszke, S. Gross, F. Massa, et al., Pytorch: An imperative style, high-performance deep learning library, NeurIPS (2019). 26

  47. [48]

    Abadi, P

    M. Abadi, P. Barham, J. Chen, et al., Tensorflow: a system for large-scale machine learning, in: USENIX Conference on Operating Systems Design and Implementation (OSDI), 2016

  48. [49]

    Nguyen, S

    G. Nguyen, S. Dlugolinsky, M. Bobák, et al., Machine learning and deep learning frameworks and libraries for large-scale data mining: a survey, Artificial Intelligence Review (2019)

  49. [50]

    Riedel, R

    M. Riedel, R. Sedona, C. Barakat, et al., Practice and experience in using parallel and scalable machine learning with heterogenous modular supercomputing architectures, in: IEEE IPDPS Workshops, 2021

  50. [51]

    Suarez, N

    E. Suarez, N. Eicker, T. Lippert, Modular supercomputing architecture: from idea to production, in: Contemporary high performance computing, CRC Press, 2019

  51. [52]

    D. Reed, D. Gannon, J. Dongarra, Hpc forecast: Cloudy and uncertain, Communications of the ACM (2023)

  52. [53]

    Gabriel, G

    E. Gabriel, G. E. Fagg, G. Bosilca, , et al., Open mpi: Goals, concept, and design of a next generation mpi implementation, in: European Parallel Virtual Machine/Message Passing Interface Users’ Group Meeting, Springer, 2004

  53. [54]

    G. M. Kurtzer, V. Sochat, M. W. Bauer, Singularity: Scientific containers for mobility of compute, PloS one (2017)

  54. [55]

    Merkel, Docker: lightweight linux containers for consistent development and deployment, Linux J

    D. Merkel, Docker: lightweight linux containers for consistent development and deployment, Linux J. 2014 (239) (Mar. 2014)

  55. [56]

    Wilfong, A

    B. Wilfong, A. Radhakrishnan, H. A. L. Berre, et al., Testing and benchmarking emerging supercomputers via the mfc flow solver, arXiv:2509.13575 (2025)

  56. [57]

    B. Li, T. Patel, S. Samsi, V. Gadepally, D. Tiwari, Miso: exploiting multi-instance gpu capability on multi-tenant gpu clusters, in: Proceedings of the 13th Symposium on Cloud Computing, 2022

  57. [58]

    Narayanan, M

    D. Narayanan, M. Shoeybi, J. Casper, et al., Efficient large-scale language model training on gpu clusters using megatron-lm, in: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC), 2021

  58. [59]

    Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

    M. Shoeybi, M. Patwary, R. Puri, P. LeGresley, J. Casper, B. Catanzaro, Megatron-lm: Training multi-billion parameter language models using model parallelism, arXiv 1909.08053 (2020)

  59. [60]

    Rasley, S

    J. Rasley, S. Rajbhandari, O. Ruwase, Y. He, Deepspeed: System optimizations enable train- ing deep learning models with over 100 billion parameters, in: ACM SIGKDD international conference on knowledge discovery & data mining, 2020

  60. [61]

    Rajbhandari, J

    S. Rajbhandari, J. Rasley, O. Ruwase, Y. He, Zero: Memory optimizations toward training trillion parameter models, in: Inter. Conf. for High Performance Computing, Networking, Storage and Analysis, IEEE, 2020

  61. [62]

    Mantha, F

    P. Mantha, F. J. Kiwit, et al., Pilot-quantum: a middleware for quantum-hpc resource, workload and task management, in: Inter. Symp. on Cluster, Cloud and Internet Computing (CCGrid), IEEE, 2025

  62. [63]

    Shehata, P

    A. Shehata, P. Groszkowski, T. Naughton, et al., Bridging paradigms: Designing for hpc-quantum convergence, Future Generation Computer Systems (2025). 27

  63. [64]

    Burgholzer, J

    L. Burgholzer, J. Echavarria, P. Hopf, et al., The munich quantum software stack: Connect- ing end users, integrating diverse quantum technologies, accelerating hpc, in: Proceedings of the SC Asia and Inter. Conf. on High Performance Computing in Asia Pacific Region, 2026

  64. [65]

    G. G. Guerreschi, J. Hogaboam, F. Baruffa, N. P. Sawaya, Intel quantum simulator: A cloud-ready high-performance simulator of quantum circuits, Quantum Science and Technology (2020)

  65. [66]

    Esposito, J

    A. Esposito, J. R. Jones, S. Cabaniols, D. Brayford, A hybrid classical-quantum hpc workload, in: International Conference on Quantum Computing and Engineering (QCE), 2023

  66. [67]

    Schüsler, A

    O. Schüsler, A. Torres-Knoop, J. Dijkshoorn, C. Hollemans, B. Van Der Vlies, R. Versluis, Towards a dutch hybrid quantum/hpc infrastructure, in: IEEE QCE, 2023

  67. [68]

    Shehata, T

    A. Shehata, T. Naughton, I.-S. Suh, A framework for integrating quantum simulation and high performance computing, in: International Conference on Quantum Computing and Engineering (QCE), 2024

  68. [69]

    Chundury, A

    S. Chundury, A. Shehata, T. Naughton III, et al., Qfw: A quantum framework for large-scale hpc ecosystems, Tech. rep., ORNL (2024)

  69. [70]

    S. A. Caldwell, M. Khazraee, E. Agostini, et al., Platform architecture for tight coupling of high-performance computing with quantum processors, arXiv preprint arXiv:2510.25213 (2025)

  70. [71]

    K.-C. Chen, X. Li, X. Xu, Y.-Y. Wang, C.-Y. Liu, Quantum-classical-quantum work- flow in quantum-hpc middleware with gpu acceleration, in: Inter. Conf. on Quantum Communications, Networking, and Computing (QCNC), IEEE, 2024

  71. [72]

    X. Zhan, K. G. Johnson, A. Esposito, et al., A full stack framework for high performance quantum-classical computing, arXiv preprint arXiv:2510.20128 (2025)

  72. [73]

    Clements, and James Fletcher

    M. Slysz, P. Rydlichowski, K. Kurowski, et al., Hybrid classical-quantum supercomputing: A demonstration of a multi-user, multi-qpu and multi-gpu environment, arXiv preprint arXiv:2508.16297 (2025)

  73. [74]

    Elsharkawy, X

    A. Elsharkawy, X. Guo, M. Schulz, Integration of quantum accelerators into hpc: Toward a unified quantum platform, in: International Conference on Quantum Computing and Engineering (QCE), IEEE, 2024

  74. [75]

    McArdle, S

    S. McArdle, S. Endo, A. Aspuru-Guzik, et al., Quantum computational chemistry, Reviews of Modern Physics (2020)

  75. [76]

    Peruzzo, J

    A. Peruzzo, J. McClean, P. Shadbolt, et al., A variational eigenvalue solver on a photonic quantum processor, Nature communications (2014)

  76. [77]

    W. Li, Z. Yin, X. Li, et al., A hybrid quantum computing pipeline for real world drug discovery, Scientific Reports (2024)

  77. [78]

    Kim, et al., Machine learning for accelerating energy materials discovery: Bridging quantum accuracy with computational efficiency, Advanced Energy Materials (2026)

    J. Kim, et al., Machine learning for accelerating energy materials discovery: Bridging quantum accuracy with computational efficiency, Advanced Energy Materials (2026)

  78. [79]

    Santagati, A

    R. Santagati, A. Aspuru-Guzik, R. Babbush, et al., Drug design on quantum computers, Nature Physics (2024). 28

  79. [80]

    M. A. G. A. Barroca, et al., Quantum simulations of battery electrolytes with VQE–qEOM and SQD: Active-space, arXiv:2509.13826 (2025)

  80. [81]

    Department of Energy Announces $30 Million to Use Quantum Computing for Groundbreaking Chemistry and Materials Science Simulations (2024)

    ARPA-E, U.S. Department of Energy Announces $30 Million to Use Quantum Computing for Groundbreaking Chemistry and Materials Science Simulations (2024)

Showing first 80 references.